Ocean current measurement method and system based on distributed sound sensing

Abstract

The invention discloses an ocean current measurement method and system based on distributed sound sensing, and relates to the technical field of data processing, and the method comprises the steps: obtaining data collected by a plurality of sensors disposed on a submarine optical fiber as sensing data; performing space-time alignment on the sensing data acquired by each sensor so as to synchronize the sensing data; converting the sensing data after space-time alignment into a signal in a frequency domain form as an original signal; removing tidal signals in the original signals to obtain ocean current signals; doppler frequency shift is calculated according to the ocean current signals, and then ocean current data are obtained based on Doppler frequency shift inversion. According to the method, data acquired by a plurality of sensors are fused, and processing such as time alignment and Doppler frequency shift calculation is carried out, so that the diversity and reliability of the data are improved, and the accuracy of ocean current inversion is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and system for measuring ocean currents based on distributed acoustic sensing. Background Art

[0002] The existing ocean current monitoring technology solutions have the following technical problems: (1) Mechanical current meter: The measurement dimension is limited and can only measure one-dimensional flow velocity, and cannot obtain three-dimensional flow field information. Low flow rate measurement failure: The mechanical rotor has large inertia and is prone to stalling in low flow or turbulent environments, resulting in data distortion. Susceptible to environmental influences: Contact measurement will interfere with the flow field, and the components are prone to rust and jam, with high maintenance costs. (2) Electromagnetic current meter: Weak anti-interference ability, susceptible to changes in the geomagnetic field and electromagnetic interference, requires frequent calibration, and is complex to operate. Limited application scenarios: It cannot measure flow velocity in shallow water areas near the bottom or in highly turbulent environments (such as nearshore sediment transport areas). High energy consumption: Deep-sea applications require special protection designs and high long-term operating costs. (3) Acoustic Doppler technology (ADCP / ADV): ​​Depends on scatterers and requires suspended particles or organisms in the water as sound wave reflection sources. It cannot be applied in low-scatterer areas such as the polar regions and deep sea. Measurement blind spots: Sound wave propagation is affected by temperature, salinity, and suspended matter, resulting in accuracy fluctuations. High equipment cost: A single ADCP is expensive, and large-scale deployment is not economical. (4) Satellite remote sensing technology: Two-dimensional plane observation can only monitor parameters such as sea surface height and temperature, and cannot obtain water profile flow velocity information. Low spatiotemporal resolution: Long revisit cycle (such as Argo floats require weeks to months), making it difficult to capture rapid changes. Data island problem: Difficulty in integrating multi-source data, affecting comprehensive analysis capabilities.

[0003] Common technical problems of existing technologies include: (1) poor adaptability to extreme environments: equipment in deep sea and polar regions is easily damaged and difficult to maintain; (2) insufficient data real-time performance: traditional equipment needs to transmit data regularly and cannot respond to environmental changes in real time. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to propose a method and system for measuring ocean currents based on distributed acoustic sensing to improve the accuracy of ocean current measurements.

[0005] To achieve the above object, one aspect of an embodiment of the present application provides a method for measuring ocean currents based on distributed acoustic sensing, the method comprising the following steps:

[0006] acquiring data collected by a plurality of sensors arranged on a submarine optical fiber as sensing data;

[0007] Performing spatiotemporal alignment on the sensor data collected by each of the sensors to synchronize the sensor data;

[0008] Converting the time-space aligned sensing data into a signal in the frequency domain as an original signal;

[0009] Removing the tidal signal from the original signal to obtain an ocean current signal;

[0010] The Doppler frequency shift is calculated according to the ocean current signal, and then the ocean current data is obtained by inverting the Doppler frequency shift.

[0011] In some embodiments, the performing spatiotemporal alignment on the sensor data collected by each of the sensors to synchronize the sensor data comprises the following steps:

[0012] The sensing data collected by each of the sensors is temporally and spatially aligned based on GPS data and tidal data to synchronize the sensing data; wherein the sensing data includes vibration signals, temperature, salinity and turbidity.

[0013] In some embodiments, converting the time-space aligned sensing data into a signal in the frequency domain as the original signal comprises the following steps:

[0014] The sensing data after time-space alignment is converted into a signal in frequency domain form as the original signal by fast Fourier transform;

[0015] The expression of the fast Fourier transform is:

[0016]

[0017] Where ω is the angular frequency, k is the wave number vector, and r is the position coordinate;

[0018] The expression of the angular frequency is:

[0019]

[0020] Among them, σ is the surface tension coefficient of seawater, and α is the nonlinear coefficient.

[0021] In some embodiments, before removing the tidal signal from the original signal to obtain the ocean current signal, the method further includes a step of determining the tidal signal, and the step of determining the tidal signal includes the following steps:

[0022] Using multi-beam sonar data, satellite altimeter data and vibration signals in the sensor data as multi-source data, and synchronizing the multi-source data;

[0023] The synchronized multi-source data are subjected to wavelet packet denoising and time-space alignment and then converted into a signal in the frequency domain, and then feature screening is performed to obtain a target vibration signal;

[0024] Calculating a phase difference between the target vibration signals corresponding to any two sensors separated by a set distance;

[0025] smoothing the phase difference by a sliding window to suppress transient noise;

[0026] Inputting the phase difference, the multi-beam sonar data and the satellite altimeter data into an LSTM network to obtain a tidal phase-amplitude curve output by the LSTM network;

[0027] The tidal signal is obtained by reconstructing the tidal phase-amplitude curve.

[0028] In some embodiments, the step of converting the synchronized multi-source data into a signal in frequency domain comprises the following steps:

[0029] constructing a local water depth gradient matrix based on the multibeam sonar data;

[0030] Correcting the wave number vector according to the local water depth gradient matrix;

[0031] The synchronized multi-source data are converted into a signal in frequency domain according to the corrected wave number vector.

[0032] In some embodiments, calculating the Doppler frequency shift according to the ocean current signal comprises the following steps:

[0033] Calculating the phase difference between adjacent sensors according to the ocean current signal through a cross-correlation function, and calculating the relative flow velocity of the ocean current between adjacent sensors in combination with the sound velocity;

[0034] The expression of the relative flow velocity is:

[0035]

[0036] Wherein, Δυ represents the relative flow velocity, f d is the Doppler frequency shift, f 0 is the transmitting frequency, θ is the sound beam installation angle;

[0037] determining a flow velocity gradient based on the relative flow velocity;

[0038] Determine a dynamic Doppler frequency shift mapping model in combination with the flow velocity gradient and then calculate the Doppler frequency shift;

[0039] The dynamic Doppler shift mapping model is:

[0040]

[0041] Where v is the speed of the sound source, is the flow velocity gradient, σ bedis the reflectivity of the seabed.

[0042] In some embodiments, the obtaining of ocean current data based on the Doppler frequency shift inversion comprises the following steps:

[0043] Based on the Green function and flow field coupling algorithm, the distributed acoustic sensor-flow field relationship model is established as follows:

[0044] u(x,t)=∫G(x,x′)·f(x′,t)dx′;

[0045] Wherein, G is the Green's function, and f is the flow field source term;

[0046] The Green's function and the distributed acoustic sensor-flow field relationship model are combined to establish the joint inversion equation of the acoustic wave propagation equation and the flow field dynamics as follows:

[0047]

[0048] Wherein, P is the sound pressure field, c(x) is the sound velocity profile, and Q is the source term excited by each of the sensors;

[0049] The ocean current data is obtained by inverting the joint inversion equation of the acoustic wave propagation equation and the flow field dynamics.

[0050] To achieve the above object, another aspect of the embodiment of the present application provides an ocean current measurement system based on distributed acoustic sensing, the system comprising:

[0051] A data acquisition unit, used to acquire data collected by a plurality of sensors arranged on the submarine optical fiber as sensing data;

[0052] A data preprocessing unit, used for performing spatiotemporal alignment on the sensing data collected by each of the sensors to synchronize the sensing data;

[0053] A time-frequency conversion unit, used to convert the sensing data after time-space alignment into a signal in the frequency domain as an original signal;

[0054] A tidal removal unit, used to remove the tidal signal from the original signal to obtain an ocean current signal;

[0055] The ocean current inversion unit is used to calculate the Doppler frequency shift according to the ocean current signal, and then obtain the ocean current data based on the Doppler frequency shift inversion.

[0056] To achieve the above objective, another aspect of an embodiment of the present application provides an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the above method when executing the computer program.

[0057] To achieve the above objective, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0058] The embodiments of the present application include at least the following beneficial effects:

[0059] The present application can obtain data collected by multiple sensors set on submarine optical fibers as sensing data; perform time-space alignment on the sensing data collected by each sensor to synchronize the sensing data; convert the time-space aligned sensing data into a signal in the frequency domain as the original signal; remove the tidal signal in the original signal to obtain the ocean current signal; calculate the Doppler frequency shift based on the ocean current signal, and then invert the ocean current data based on the Doppler frequency shift. The present application improves the diversity and reliability of the data by fusing the data collected by multiple sensors and performing time alignment and Doppler frequency shift calculation, thereby improving the accuracy of the inverted ocean current. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0061] Figure 1 A schematic diagram of a flow chart of an ocean current measurement method based on distributed acoustic sensing provided in an embodiment of the present application;

[0062] Figure 2 A schematic diagram of the structure of an ocean current measurement system based on distributed acoustic sensing provided in an embodiment of the present application;

[0063] Figure 3 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of systems and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0065] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0066] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0067] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0068] Before describing the embodiments of the present application in detail, some related technologies involved in the embodiments of the present application are first described as follows:

[0069] Ocean currents are the core parameters of the ocean dynamic environment, directly affecting global climate stability, ecosystem balance and marine resource development (such as shipping safety and fishery resource distribution). The development of its monitoring technology began with drift bottle observations in the 17th century, and was gradually improved with the advancement of mechanical current meters (1905), acoustic Doppler current profilers (ADCP, 1970s) and satellite remote sensing technology. Traditional methods include reliance on mechanical current meters (such as Eckman current meters) and drifting buoys, which have problems such as low measurement accuracy and susceptibility to interference. Modern technologies include combining satellite remote sensing (such as Argo buoys), acoustic Doppler technology and artificial intelligence to achieve large-scale, high-precision, multi-parameter comprehensive monitoring.

[0070] In the early days, drift bottle current measurement was used to estimate the flow rate through the drift path of drifting objects. It is now only used for surface current observation. Drifting buoy current measurement uses GPS / satellite positioning to track the trajectory of the buoy. It is divided into sea surface buoys (shallow, less than 3m) and neutral buoys (deep, up to 6000m). For example, there are more than 3,800 Argo buoys deployed worldwide. Fixed-point current measurement includes bench / anchor current measurement, which uses mechanical (Andra current meter) or acoustic (ADCP) equipment to continuously monitor small-scale flow fields for a long time, and is suitable for local refined research. Submerged buoy current measurement is to hang a self-contained ADCP or Andra current meter on a submerged buoy, which can monitor single-layer or multi-layer flow velocities, and the deployment period is ≥6 months. Cruise current measurement uses ship-borne ADCP or single-point acoustic Doppler current meter (such as Andra RCM) to achieve mobile measurement, which is suitable for large-scale rapid surveys. Remote sensing and numerical models combine satellite remote sensing (sea surface height, temperature) with numerical models (such as ocean current flow calculation algorithms) to improve the accuracy of large-scale monitoring.

[0071] The core advantages of distributed acoustic sensing (DAS) technology are mainly reflected in the following:

[0072] ① Non-contact three-dimensional measurement, through the time difference and phase change of sound wave propagation, realize three-dimensional flow velocity vector measurement, breaking through the one-dimensional / two-dimensional limitations of traditional technology.

[0073] ② Long-distance and large-area coverage: using fiber-optic sensor networks (such as submarine optical cables), thousands of sensor nodes can be deployed to cover areas from the deep sea to the polar regions that are difficult to reach with traditional technologies.

[0074] ③ Strong anti-interference ability, strong sound wave signal penetration, little interference from environmental factors such as waves and wind noise, and high data stability.

[0075] ④ High precision and real-time performance, with a resolution of up to millimeter level, supporting real-time data collection and transmission to meet disaster warning and scientific research needs.

[0076] ⑤ Low cost and easy deployment. Based on the transformation of existing optical fiber networks, the deployment cost is low; it supports long-term continuous monitoring and has a long maintenance cycle.

[0077] ⑥ Multi-parameter fusion potential, simultaneous monitoring of temperature, salinity, internal waves and other parameters, and inversion of three-dimensional flow field using acoustic tomography technology.

[0078] Table 1 is a comparison between the existing flow measurement technology solution and the distributed acoustic sensing flow measurement technology solution of the present application.

[0079] Table 1

[0080]

[0081] Therefore, the embodiment of the present application provides a method and system for measuring ocean currents based on distributed acoustic sensing, which relates to the field of data processing technology. The method and system for measuring ocean currents based on distributed acoustic sensing provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or a server cluster or distributed system composed of multiple physical servers, and can also be configured to provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms and other basic cloud computing services. The server can also be a node server in a blockchain network; the software can be an application that implements an ocean current measurement method based on distributed acoustic sensing, etc., but is not limited to the above forms.

[0082] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0083] Reference Figure 1 The embodiment of the present application provides an ocean current measurement method based on distributed acoustic sensing, which may include but is not limited to S100 to S140, as follows:

[0084] S100: Acquire data collected by multiple sensors arranged on the submarine optical fiber as sensing data;

[0085] S110: performing spatiotemporal alignment on the sensor data collected by each of the sensors to synchronize the sensor data;

[0086] S120: converting the sensing data after time-space alignment into a signal in frequency domain as an original signal;

[0087] S130: removing the tidal signal from the original signal to obtain an ocean current signal;

[0088] S140: Calculate the Doppler frequency shift according to the ocean current signal, and then obtain the ocean current data based on the Doppler frequency shift inversion.

[0089] Optionally, the performing spatiotemporal alignment on the sensing data collected by each of the sensors to synchronize the sensing data comprises the following steps:

[0090] The sensing data collected by each of the sensors is temporally and spatially aligned based on GPS data and tidal data to synchronize the sensing data; wherein the sensing data includes vibration signals, temperature, salinity and turbidity.

[0091] Optionally, converting the time-space aligned sensing data into a signal in the frequency domain as the original signal comprises the following steps:

[0092] The sensing data after time-space alignment is converted into a signal in frequency domain form as the original signal by fast Fourier transform;

[0093] The expression of the fast Fourier transform is:

[0094]

[0095] Where ω is the angular frequency, k is the wave number vector, and r is the position coordinate;

[0096] The expression of the angular frequency is:

[0097]

[0098] Among them, σ is the surface tension coefficient of seawater, and α is the nonlinear coefficient.

[0099] Optionally, before removing the tidal signal from the original signal to obtain the ocean current signal, the method further comprises a step of determining the tidal signal, and the step of determining the tidal signal comprises the following steps:

[0100] Using multi-beam sonar data, satellite altimeter data and vibration signals in the sensor data as multi-source data, and synchronizing the multi-source data;

[0101] The synchronized multi-source data are subjected to wavelet packet denoising and time-space alignment and then converted into a signal in the frequency domain, and then feature screening is performed to obtain a target vibration signal;

[0102] Calculating a phase difference between the target vibration signals corresponding to any two sensors separated by a set distance;

[0103] smoothing the phase difference by a sliding window to suppress transient noise;

[0104] Inputting the phase difference, the multi-beam sonar data and the satellite altimeter data into an LSTM network to obtain a tidal phase-amplitude curve output by the LSTM network;

[0105] The tidal signal is obtained by reconstructing the tidal phase-amplitude curve.

[0106] Optionally, the step of converting the synchronized multi-source data into a signal in frequency domain form comprises the following steps:

[0107] constructing a local water depth gradient matrix based on the multibeam sonar data;

[0108] Correcting the wave number vector according to the local water depth gradient matrix;

[0109] The synchronized multi-source data are converted into a signal in frequency domain according to the corrected wave number vector.

[0110] Optionally, calculating the Doppler frequency shift according to the ocean current signal comprises the following steps:

[0111] Calculating the phase difference between adjacent sensors according to the ocean current signal through a cross-correlation function, and calculating the relative flow velocity of the ocean current between adjacent sensors in combination with the sound velocity;

[0112] The expression of the relative flow velocity is:

[0113]

[0114] Wherein, Δυ represents the relative flow velocity, f d is the Doppler frequency shift, f 0 is the transmitting frequency, θ is the sound beam installation angle;

[0115] determining a flow velocity gradient based on the relative flow velocity;

[0116] Determine a dynamic Doppler frequency shift mapping model in combination with the flow velocity gradient and then calculate the Doppler frequency shift;

[0117] The dynamic Doppler shift mapping model is:

[0118]

[0119] Where v is the speed of the sound source, is the flow velocity gradient, σ bed is the reflectivity of the seabed.

[0120] Optionally, obtaining ocean current data based on the Doppler frequency shift inversion comprises the following steps:

[0121] Based on the Green function and flow field coupling algorithm, the distributed acoustic sensor-flow field relationship model is established as follows:

[0122] u(x,t)=∫G(x,x′)·f(x′,t)dx′;

[0123] Wherein, G is the Green's function, and f is the flow field source term;

[0124] The Green's function and the distributed acoustic sensor-flow field relationship model are combined to establish the joint inversion equation of the acoustic wave propagation equation and the flow field dynamics as follows:

[0125]

[0126] Wherein, P is the sound pressure field, c(x) is the sound velocity profile, and Q is the source term excited by each of the sensors;

[0127] The ocean current data is obtained by inverting the joint inversion equation of the acoustic wave propagation equation and the flow field dynamics.

[0128] Next, the solution of the embodiment of the present application will be introduced and explained in detail with reference to specific application examples.

[0129] This embodiment provides a three-dimensional ocean current monitoring solution based on DAS technology, which realizes high-precision, low-cost, and real-time ocean current monitoring through multi-node (sensor) collaborative acquisition, combined FK transformation and deep learning data processing, nonlinear dispersion model optimization and automatic fitting algorithm. The steps of this embodiment are: fiber optic cable deployment → data acquisition and preprocessing → FK transformation and dispersion analysis → tidal modulation removal → Doppler shift calculation → automatic fitting algorithm → three-dimensional inversion of ocean currents. The specific implementation process is:

[0130] Step 1: Fiber optic cable deployment method.

[0131] Multi-node distributed deployment: Optical fiber cables are laid along the seabed topography in key sea areas (such as straits and ocean current intersections), with a node spacing of 500-2000 meters and a coverage depth of 0-6000 meters. Repeater optimization: A high-frequency signal enhancement module is set at the optical cable repeater to improve the signal-to-noise ratio of the remote signal through the HLLB (high-loss loopback) path. Tensile and corrosion-resistant design: Armored loose-tube optical fiber is used, with a tensile strength of >600N and resistance to seawater corrosion (fixed by UV curing glue).

[0132] The deployment structure and core components are as follows:

[0133] ①Layered structure design.

[0134] Global marine optical fiber cables use a multi-layer composite structure, and the core components include: Optical fiber core layer: transmits optical signals, covered with polyethylene or polyester resin protective layer. Reinforcement system: High-strength steel wire strands (single armor / double armor) provide tensile strength, and deep-sea sections use double armor (DA2) or rock armor (RA2) to withstand high pressure. Sheath system: Aluminum waterproof layer prevents seawater penetration;

[0135] Copper or aluminum tubes are used to power the repeater to isolate the paraffin and alkane layers from hydrogen permeation.

[0136] ②Functional module integration.

[0137] Repeaters: deployed every 40-60 kilometers, powered by submarine cables, amplifying signals and extending transmission distances (e.g. SAIL submarine cables use 100G technology). Branching units: connect networks in different sea areas and support multi-national data interoperability (e.g. 2Africa cables branch to South Africa and India). Junction boxes: seal and protect optical cable splicing points, with a pressure resistance rating of up to 6,000 meters in water.

[0138] The deployment steps and technical specifications are as follows:

[0139] ①Route planning and investigation.

[0140] Multimodal data fusion: Combine satellite remote sensing (such as Google Earth Engine), multi-beam sonar and historical submarine cable fault data to avoid high-risk areas such as anchorage areas and volcanic belts. Environmental parameter assessment: Measure water depth (shallow sea <500 meters / deep sea >1000 meters), bottom quality (sand / rock) and current intensity to select the appropriate armor type.

[0141] ② Laying of ship and traction system.

[0142] The dedicated cable-laying vessel is equipped with an automatic tension control system, which supports laying 150 kilometers per day. Two-stage laying method: Shallow sea section (<200 meters): towing laying, buried at a depth of 3 meters (using underwater robots for gullying); Deep sea section (>2000 meters): direct sinking, using its own weight to cover the seabed.

[0143] ③Relay and maintenance system.

[0144] Dynamic calibration: Verify repeater performance through Argo buoy data and adjust supply voltage quarterly (e.g. ±3% range). Intelligent monitoring: Integrated fiber optic vibration sensor to detect abnormal events such as shark bites and fishing boat trawling in real time. Adopt gradient pressure compensation design, such as RA2 rock armor can withstand 11,000 meters of water depth pressure (about 1,100 times atmospheric pressure). Add shark skin bionic texture to the outer layer to reduce the probability of biological attachment.

[0145] Step 2: Data collection and preprocessing.

[0146] The specific steps of data preprocessing are:

[0147] ①Data cleaning and quality control.

[0148] Integrity check: Identify transmission interruptions through CRC check and automatically complete missing data (using KNN interpolation method).

[0149] Outlier detection uses statistical methods: the 3σ principle eliminates data that exceeds 3 times the standard deviation;

[0150] Machine learning: Identify burst noise based on Isolation Forest algorithm.

[0151] ②Spatiotemporal alignment and registration.

[0152] Multi-source data fusion: Remote sensing data (such as Sentinel-3) is converted to the WGS84 coordinate system through RPC parameters; buoy data is interpolated in time and space using Kalman filtering and DAS data. Tidal phase correction: Kelvin wave signals in the 0.1-1Hz frequency band are extracted and tidal parameters are calculated using T_TIDE software.

[0153] ③Signal enhancement and feature extraction.

[0154] Wavelet transform denoising: db4 wavelet basis is used, the decomposition level is 5, and the threshold is set to 3 times the noise standard deviation.

[0155] The spectrum analysis uses Morlet wavelet transform to extract the 0.2-2Hz gravity wave frequency band characteristics;

[0156] The formula for calculating the dispersion relation is:

[0157]

[0158] Where σ is the surface tension coefficient of seawater.

[0159] ④Data standardization and storage.

[0160] Normalization: Min-Max normalization is used to map the parameters to the [0,1] interval to eliminate dimensional differences.

[0161] Distributed storage: Build a storage cluster based on Hadoop HDFS. A single cluster supports PB-level data storage with a latency of <50ms.

[0162] The key technology implementation includes three parts:

[0163] ①Fiber optic sensing signal demodulation.

[0164] Polarization diversity detection (PDD) technology is used to calculate the vibration amplitude and phase difference through two orthogonal polarization state signals. Signal acquisition frequency: 192kHz (covering 0.1-100Hz frequency band), dynamic range ±100dB3.

[0165] ②Automated quality control framework.

[0166] Real-time calibration: The Argo program obtains high-precision seawater temperature (±0.005°C) and salinity (±0.01) data through globally distributed automatic profiling floats (ARGO). However, seawater conductivity sensors are susceptible to oil pollution, biological adhesion and physical deformation, resulting in the accumulation of long-term observation errors. Real-time calibration technology solves the timeliness defects of traditional offline calibration and ensures data availability by dynamically correcting sensor deviations. The velocity measurement accuracy is verified daily through Argo float data, and the calibration coefficient is updated dynamically. Abnormal warning: When the data of three consecutive sampling points deviate from the mean by more than 2σ, an alarm is triggered and the backup channel is started. The specific implementation process of real-time calibration:

[0167] Profile data quality control:

[0168] Temperature / salinity threshold filtering: temperature limit is -2.5~40℃, salinity limit is 2~41, pressure range is -5×10 4 ~2200×10 4 Pa.

[0169] Density consistency check: The density difference between adjacent layers shall not exceed 0.03kg / m 3 , to prevent density reversal anomalies. Abnormal profile identification: TS curve verification: Identify bottom temperature and salinity anomalies (such as salinity mutation > 0.08) through historical data comparison. Repeated profile detection: If two consecutive profiles are exactly the same, they are marked as faulty data.

[0170] Trajectory data quality control: Positioning anomaly detection: Distance threshold: When the distance between adjacent positioning points exceeds the satellite positioning accuracy (such as 200m), an alarm is triggered. Drift speed limit: Set an upper limit of 3m / s to eliminate extreme motion interference. Trajectory smoothing: Use Kalman filtering to fuse satellite positioning and inertial sensor data to correct drift trajectory.

[0171] Real-time correction algorithm:

[0172] Pressure correction: Use the pressure value measured during the floating stage on the sea surface to correct the zero drift of the underwater pressure sensor.

[0173] Salinity Correction: Dynamically corrects real-time salinity data based on conductivity offset parameters determined by delayed quality control.

[0174] ③Multi-parameter joint analysis.

[0175] Construct the correlation model of marine environmental parameters: Chl-a=0.023T+0.001S+0.55U;

[0176] Where T, S, and U are temperature, salinity, and flow velocity, respectively.

[0177] Step 3: FK transformation and dispersion analysis.

[0178] ①Signal acquisition and preprocessing.

[0179] Multimodal data acquisition: Through the Rayleigh scattering effect of submarine optical fiber cables (armored loose-tube optical fiber, tensile strength > 600N), 192kHz vibration signals are collected per second, covering the 0.1-100Hz frequency band; environmental parameters such as temperature, salinity, and turbidity are collected simultaneously (sensor spacing 500-2000 meters).

[0180] Preprocessing steps:

[0181] Noise filtering: Use wavelet transform (db4 basis function, decomposition 5 layers) to remove high-frequency noise;

[0182] Time and space alignment: synchronize multi-node signals based on GPS / tidal data, with a latency of <50ms;

[0183] Feature extraction: Extract gravity wave signals in the 0.2-2 Hz frequency band for subsequent dispersion analysis.

[0184] ②F-K transformation and signal separation.

[0185] Time-frequency domain conversion:

[0186]

[0187] Where ω is the angular frequency, k is the wave number vector, and r is the position coordinate.

[0188] Signal separation: Separate ocean current signals (low frequency, long wave) from tidal signals (high frequency, short wave) through threshold setting (SNR>15dB).

[0189] ③Nonlinear dispersion relationship modeling.

[0190] Traditional model modification:

[0191] Based on the linear OSGW model:

[0192]

[0193] Where σ is the surface tension coefficient of seawater.

[0194] Introducing a nonlinear correction term:

[0195]

[0196] Where α is the nonlinear coefficient, which is inverted by multibeam sonar data.

[0197] Seabed topography compensation: Combine DEM data to construct a local water depth gradient matrix and correct the spatial distribution of the wave number vector k.

[0198] ④Tidal modulation signal removal.

[0199] Dual-channel synchronous detection: Tidal signals (such as the 12.4-hour period of M2 waves) are extracted through the phase difference of adjacent optical fiber nodes; the tidal model is trained using an LSTM network, and the residual signal is the non-tidal ocean current signal.

[0200] Multivariate input fusion mechanism:

[0201] The multi-source environmental parameters such as flow velocity, water level, air pressure, wind speed, and rainfall are used as LSTM input features to build a multi-input single output (MISO) prediction model. Wavelet packet decomposition (db8 basis, 6 decomposition levels) is used to denoise the non-stationary tidal signal, retaining the key information of the 0.05-0.3Hz frequency band. Sequence data is generated through a sliding window (window length = 12 hours), and the time series step is set to 6 hours.

[0202] Dynamic attention mechanism optimization:

[0203] The long-term and short-term attention are combined: Short-term attention (local window = 1 hour): captures the instantaneous changes in the transition phase of high and low tides. Long-term attention (global window = 24 hours): captures the long-term periodicity of astronomical tides. Dynamic calibration of attention weights: Introduce the tidal sensitivity index (TMI) as the attention gating parameter, the formula is as follows:

[0204] α t =σ(W 1 ·[h t ]+W 2 ·[TMI t ]+b);

[0205] where h t is a hidden state, TMI t is the tidal modulation index.

[0206] Real-time enhancement technology:

[0207] Using knowledge distillation technology, the pre-trained Seq2Seq LSTM model (1.2M parameters) is compressed to increase the inference speed to 30 frames per second. Deployed on the FPGA platform, combined with fixed-point quantization (INT8) technology, it reduces the time consumption of a single prediction. Dual-channel synchronous detection achieves accurate separation and suppression of tidal signals through spatial correlation analysis of two signals, while retaining the characteristics of ocean currents.

[0208] Dual-channel data acquisition and synchronization:

[0209] The two acoustic sensor nodes are separated by 50-200m (covering the typical tidal horizontal gradient range), deployed on the same submarine optical cable, and share the Beidou / GPS clock (synchronization accuracy ±1μs). The tidal-related frequency band signals are retained through bandpass filtering (0.05-0.3Hz). The delay is calculated based on the cross-correlation function to correct the signal offset between nodes:

[0210]

[0211] Among them, S1 and S2 are two original signals, t align is the optimal delay.

[0212] Dual-channel signal feature extraction:

[0213] Perform FFT on the preprocessed signal and extract the amplitude ratio (A1 / A2) and phase difference (φ1-φ2) per second. Define the tidal modulation index (TMI):

[0214]

[0215] Among them, ΔA=A1-A2, Δφ=φ1-φ2.

[0216] Threshold determination: When TMI>0.3, it is marked as a potential tidal disturbance area.

[0217] Dynamic tidal model construction and removal:

[0218] Combine historical tide data (Tides-2000 global model) with real-time water level gauge data to generate spatiotemporal enhanced tide prediction fields. Two-channel joint filtering: Separate the tide prediction signal from the two signals by minimizing the residual sum of squares:

[0219]

[0220] Where T(t) is the tidal basis function, and α and β are coupling coefficients.

[0221] Residual signal:

[0222]

[0223] Real-time verification and feedback:

[0224] The signal-to-noise ratio (SNR) improvement is calculated using a sliding window (window length = 1 hour):

[0225]

[0226] If the SNR improvement is <5dB, the model retraining mechanism will be triggered.

[0227] ⑤ Doppler frequency shift calculation and flow velocity inversion.

[0228] Doppler shift formula:

[0229]

[0230] Where λ is the wavelength of the sound wave, and θ is the angle between the sound beam and the flow direction.

[0231] 3D velocity inversion:

[0232] Combining the wave number vector k obtained by FK transformation with the Doppler frequency shift, the velocity vector is solved by the following set of equations:

[0233]

[0234] Graph neural network (GNN) is introduced to optimize the inversion accuracy, and the input features include wave number, frequency, tidal residual, etc.

[0235] ⑥Automated fitting and result optimization.

[0236] Data-driven fitting: Construct a joint feature space containing velocity, direction, and dispersion parameters; use Gaussian Process Regression (GPR) to implement nonlinear least squares fitting to reduce manual intervention bias. Real-time calibration: Verify the model daily through Argo float data, and dynamically update the dispersion relationship and velocity mapping function.

[0237] Step 4: Tidal modulation effect removal.

[0238] ①Data collection and preprocessing.

[0239] Multi-source data synchronization:

[0240] Optical fiber vibration signal: 192kHz sampling per second, covering the frequency range of 0.1-100Hz;

[0241] Multibeam sonar data: obtain seabed topography elevation every 10 minutes (resolution 10cm);

[0242] Satellite altimeter data: Sea surface height is updated every 30 minutes (accuracy ±3cm).

[0243] Preprocessing steps:

[0244] Wavelet packet denoising: using sym8 wavelet basis, decomposition level 5, the threshold is set to 1.5 times the global energy mean;

[0245] Time and space alignment: Based on Beidou / GPS clock synchronization, the delay error between nodes is less than 10μs;

[0246] Feature screening: retain the 0.01-1Hz frequency band signal and remove high-frequency ship noise (>5Hz).

[0247] ②Dual-channel phase difference extraction.

[0248] Adjacent node signal alignment: Select fiber nodes A and B, which are 500m apart, and calculate the phase difference of their vibration signals

[0249] Δφ(t)=φ A (t)-φ B (t);

[0250] The phase difference is smoothed by a sliding window (window length 1 hour) to suppress transient noise.

[0251] Tidal component identification: extraction using Fourier transform The spectrum of the star is used to identify the main tidal components (M2, S2, K1, etc.); based on the tidal stellar period (such as the M2 wave 12.42h), the initial phase-amplitude relationship model is established.

[0252] ③LSTM network time series prediction.

[0253] Network architecture:

[0254] Input layer: Fusion Multibeam sonar elevation (H), satellite altimeter sea surface height (η);

[0255] Hidden layer: two layers of bidirectional LSTM (128 units per layer), introducing the attention mechanism to strengthen the temporal correlation;

[0256] Output layer: predict the tidal phase-amplitude curve for the next 30 minutes.

[0257] Training strategy: Use sliding window method to generate time series (window length 24 hours, step length 1 hour); loss function: weighted MSE (weight: the inverse of the square of the amplitude of the tidal component); optimizer: AdamW (learning rate 0.001, weight decay 0.01).

[0258] ④Reconstruction and removal of tidal signals.

[0259] Signal synthesis: Reconstruct the original tidal signal based on the phase-amplitude of the tidal component predicted by LSTM:

[0260]

[0261] Among them, An, ωn, φn are the parameters of each tidal component.

[0262] Residual signal extraction:

[0263] The original vibration signal minus the tidal signal:

[0264]

[0265] The residual signal is the low-frequency signal dominated by ocean currents.

[0266] ⑤Multi-beam sonar data compensation.

[0267] Seafloor terrain correction:

[0268] Constructing local water depth gradient matrix based on multibeam sonar data Corrected wave number vector k:

[0269]

[0270] Where c is the speed of sound and g is the acceleration due to gravity.

[0271] Dispersion relation optimization:

[0272] The corrected wave number vector is introduced into the nonlinear OSGW model to improve the accuracy of dispersion calculation in shallow water areas.

[0273] The data compensation method implementation process includes the following solutions:

[0274] Multi-source data fusion preprocessing:

[0275] A hierarchical data fusion framework is used: Level 1: multi-beam raw data (70% weight); Level 2: real-time satellite altimeter inversion tidal height data (15% weight); Level 3: seabed sediment echo characteristic parameters (15% weight). Based on Kalman filtering, the signal-to-noise ratio of each data source is evaluated in real time, and the weight distribution is automatically optimized.

[0276] Array integrated self-calibration:

[0277] Focused beamforming technology: Through field experimental data homing compensation, the transducer array can be self-calibrated in real time (error < 0.5°). Dynamic beam pointing control: Real-time attitude data (yaw, roll) is fed back to the transmit / receive array to ensure that the sound beam is always pointed directly downward.

[0278] Deploy a distributed hydrophone array (≥16 channels), covering a horizontal ±60° solid angle, with a sampling rate of ≥192kHz, to support the capture of spatiotemporal correlation of environmental noise. Integrate MEMS inertial sensors (three-axis gyroscope + accelerometer) to monitor the motion status of the equipment in real time (accuracy ≤0.01° / s). Use bandpass filtering (100Hz-5kHz) to remove low-frequency mechanical noise interference, and extract non-stationary noise features through wavelet packet decomposition (db4 basis, 5 decomposition levels). Iterative beamforming calibration: Calculate the delay matrix based on the generalized cross-correlation (GCC-PHAT) algorithm, and estimate the signal source direction based on the beamforming pattern. Iteratively correct the array element coordinates using the least squares method:

[0279]

[0280] Where J is the array output signal-to-noise ratio cost function and α is the step size parameter.

[0281] When the position change between two iterations is less than 0.1 mm, the calibration is terminated. Blocking matrix reconstruction: The interference noise covariance matrix (INCM) is projected into the signal subspace through the projection matrix to eliminate the interference component:

[0282]

[0283] Where P proj is the eigenvector projection matrix.

[0284] The iterative mismatch approximation method is used, which only relies on the prior knowledge of array geometry and signal orientation to reduce the computational complexity. Multi-level alarm system: Level 1 alarm (threshold ±3σ): triggers the sound and light alarm and records the abnormal period data; Level 2 alarm (threshold ±5σ): automatically switches to the backup sensor and starts the emergency calibration process.

[0285] Multiphysics coupling compensation:

[0286] Green function-fluid-solid coupling model: Establish a joint inversion framework of the acoustic wave propagation equation and flow field dynamics, dynamically correct the sound velocity distribution (lateral resolution ≤ 10cm); introduce the seabed reflectivity parameter (obtained through multi-frequency mixed imaging) to correct the angle response deviation. Use wavelet packet decomposition to extract the spatiotemporal characteristics of the acoustic field, compress the original data, and improve the calculation efficiency.

[0287] Dynamic abnormal data repair:

[0288] Train an abnormal noise recognition model based on YOLOv7-Tiny, with a detection speed of 30 frames per second; combine the Restoration algorithm (such as total variation TV) to perform spatial domain repair on the detected noise. Spatiotemporal consistency verification: Calculate the data consistency index (DCI) through a sliding window (window length = 1 hour), and automatically remove abnormal strips with DCI < 0.8.

[0289] ⑥Dynamic calibration and verification.

[0290] Argo float real-time verification: randomly select three Argo float positions every day, compare the tidal phase-amplitude prediction value with the actual observation value; dynamically adjust the LSTM network weight based on maximum likelihood estimation (MLE). Visual monitoring: develop a web-based dashboard to display the tidal signal removal effect in real time (signal-to-noise ratio SNR>20dB is qualified).

[0291] Step 5: Doppler frequency shift calculation and ocean current inversion.

[0292] The three-dimensional velocity inversion consists of the following two parts:

[0293] (1) Doppler frequency shift calculation process.

[0294] ①Data collection and preprocessing.

[0295] Distributed acoustic sensor deployment: submarine optical cables are used as distributed acoustic arrays, with a channel spacing of 4 meters, a sampling rate of 500Hz, and a frequency band of 0.05-0.2Hz. The seabed noise caused by ocean currents is captured by environmental noise interferometry to avoid active sound sources interfering with marine life. Preprocessing steps: wavelet packet denoising: using db8 wavelet basis, 6 decomposition levels, and the threshold is set to 2.5 times the local energy mean. Spatiotemporal alignment: based on Beidou / GPS clock synchronization, the delay error between nodes is less than 5μs.

[0296] ②Doppler frequency shift extraction.

[0297] Frequency domain analysis: Perform fast Fourier transform (FFT) on the preprocessed signal to extract the Doppler frequency shift component in the 0.05-0.2Hz frequency band. Calculate the phase difference between adjacent nodes through the cross-correlation function, and calculate the relative flow velocity in combination with the sound speed (1500m / s):

[0298]

[0299] where f d is the Doppler frequency shift, f 0 is the transmitting frequency (200-7000Hz), θ is the sound beam installation angle.

[0300] Dynamic Doppler shift mapping model:

[0301]

[0302] Where v is the sound source velocity, f0 is the fundamental frequency, θ is the angle between the sound source and the receiver, is the velocity gradient, σ bed is the reflectivity of the seabed.

[0303] Adaptive weight optimization: The tidal sensitivity index (TMI) is introduced as the weight coefficient, and the formula is as follows:

[0304]

[0305] Where λ is the dynamic adjustment coefficient and θthr is the threshold setting value.

[0306] A hybrid architecture of convolutional neural network (CNN) and recurrent neural network (RNN) is used, with the input being a noise time-frequency map (STFT resolution 0.5s×0.1Hz) and the output being a Doppler frequency shift time series. Wasserstein GAN is used to generate synthetic data in extreme noise environments (such as typhoons) to improve the generalization ability of the model.

[0307] (2) Ocean current inversion process.

[0308] ①Signal post-processing and feature extraction.

[0309] Frequency domain beam stretching: Using the frequency domain waveform stretching method, the compressed time-frequency data is restored to a high-resolution velocity field with a spatial resolution of 10 cm and a temporal resolution of 10 minutes. Feature engineering: Extracting velocity gradient tensors and vorticity component (ω) to construct the flow field dynamics feature set.

[0310] ②Inversion model construction.

[0311] Three-dimensional velocity field reconstruction:

[0312] Based on Green's function and flow field coupling algorithm, a distributed acoustic sensor-flow field relationship model is established:

[0313] u(x,t)=∫G(x,x′)·f(x′,t)dx′;

[0314] Where G is the Green's function and f is the flow field source term.

[0315] The Green function is combined with the flow field coupling model to establish a joint inversion framework of the acoustic wave propagation equation and flow field dynamics:

[0316]

[0317] Where P is the acoustic pressure field, c(x) is the acoustic velocity profile (including the influence of flow velocity), and Q is the source term (excited by distributed acoustic sensor nodes).

[0318] Dynamic sound velocity profile compensation: by introducing the velocity gradient tensor Correct the sound velocity distribution, eliminate the sound velocity deviation caused by temperature / salinity stratification, and improve the inversion accuracy (lateral resolution ≤ 10cm).

[0319] Boundary condition optimization: The hybrid boundary condition (HBC) Green's function is used to build a seabed terrain model in combination with multi-beam sonar bathymetry data to eliminate the interference of complex seabed reflections. Inhomogeneous medium correction: Introduce horizontal anisotropy parameters (such as the difference between transverse and longitudinal wave velocities), dynamically adjust the Green function weight factor through Bayesian inference, and adapt to the switching between deep-sea and shallow-sea environments. Deep learning acceleration: Design a Green's function approximation network (GF-Net) based on a convolutional neural network (CNN) to reduce the complexity of integral calculation to O(n log n), and support three-dimensional flow field reconstruction updated every 10 minutes. Feature space dimensionality reduction: Extract the spatiotemporal correlation characteristics of the sound field through wavelet packet decomposition, compress the original data, improve the calculation efficiency, and retain key flow field information. Dynamic calibration mechanism: Introduce Argo buoy real-time data (updated every 3 hours), and dynamically correct the model parameters through the maximum expectation (EM) algorithm to adapt to extreme weather such as typhoons.

[0320] Adaptive calibration algorithm: Based on Kalman filtering, multi-source data (sensor measurements, visual features, posture parameters) are integrated in real time to dynamically correct the calibration coefficients:

[0321]

[0322] Where Pv(t) is the visual feature covariance matrix and H is the measurement matrix.

[0323] The tidal sensitivity index (TMI) is introduced as the calibration trigger threshold, and the calibration process is automatically started when TMI>0.3.

[0324] ③Verification and visualization of inversion results.

[0325] Cross-validation: Compare with the adjacent ADCP (RDI 150kHz) measurement results and calculate the RMS error of flow velocity and flow direction error.

[0326] Visualization platform: Develop a three-dimensional flow field visualization system on the Web, supporting dynamic playback of flow velocity fields, vortex identification (Q value > 0.2) and abnormal flow velocity alarm.

[0327] 3D flow field visualization engine: uses WebGL technology to build an immersive interface, supports dynamic playback of velocity fields (frame rate ≥ 60fps), vortex identification (Q value > 0.2) and abnormal alarm thermal maps. Multi-dimensional data display: integrates sensor status, calibration logs, and real-time dashboards of environmental parameters (air pressure / wind speed / temperature and salinity), and supports custom data subscriptions.

[0328] In summary, this embodiment includes the following technical solutions:

[0329] (1) Multimodal signal collaborative processing: Combine FK transform with deep learning to achieve joint analysis of gravity wave signals and ocean current velocities, and improve signal separation accuracy in low signal-to-noise ratio environments.

[0330] (2) Application of nonlinear dispersion model: Breaking through the limitations of the traditional linear OSGW model, optimizing the dispersion relationship through seabed topography and multi-beam data, and reducing shallow water errors.

[0331] (3) Automated fitting framework: Build an end-to-end machine learning pipeline to achieve automatic mapping from raw vibration data to three-dimensional flow velocity, improving accuracy.

[0332] (4) Economical deployment plan: By transforming existing submarine communication optical cables, the per-kilometer deployment cost is reduced and global ocean network monitoring is supported.

[0333] (5) Spatiotemporal joint denoising framework: The dual-modal tidal signal extraction method of "fiber phase difference + multi-beam sonar" is used to improve the signal-to-noise ratio.

[0334] (6) Dynamic adaptive calibration: Based on minute-by-minute feedback from Argo buoys, tidal parameters can be updated in real time to adapt to sudden tidal changes caused by extreme weather such as typhoons.

[0335] (7) Enhanced interpretability: SHAP value analysis is used to reveal the tide-current coupling mechanism (such as the phase lag relationship between the M2 wave and the Kuroshio extension), providing a physical basis for model optimization.

[0336] (8) The dual-channel phase difference extraction device achieves efficient suppression of tidal modulation signals through dual-channel synchronous detection, dynamic parameter optimization and multi-source data fusion, while retaining the micro-scale characteristics of ocean currents. Compared with traditional methods, its spatial resolution is improved and the dynamic response speed is accelerated, providing key technical support for high-precision ocean current monitoring.

[0337] (9) The LSTM-based tidal time series prediction algorithm solves the bottleneck of traditional LSTM models in extreme environmental adaptability and ultra-short-term prediction through multi-variable input fusion, dynamic attention mechanism and real-time enhancement technology, and provides a high-precision, low-latency tidal prediction solution for fields such as marine engineering.

[0338] (10) The multi-beam sonar data compensation method solves the problems of calibration lag, large depth measurement error and weak anti-interference ability of traditional multi-beam sonar in complex marine environments through three major innovations: array self-calibration, multi-physical field coupling compensation and dynamic attitude correction. It provides a full-process, low-power solution for high-precision seabed mapping.

[0339] (11) Dynamic calibration and visual monitoring system, through three major innovations: visual-physical coupling calibration, dynamic anomaly adaptive response, and lightweight edge computing, solves the real-time, adaptability, and reliability bottlenecks of traditional calibration systems in complex marine environments, and provides a full-process, low-power solution for high-precision marine monitoring.

[0340] (12) The Doppler frequency shift extraction device driven by environmental noise solves the accuracy bottleneck of traditional Doppler measurement technology in complex noise environments through three major innovations: environmental noise dynamic modeling, multi-physics field coupling compensation, and deep learning-driven frequency shift extraction. It provides a high-sensitivity, low-power solution for the fields of ocean monitoring, intelligent transportation, etc.

[0341] (13) The distributed acoustic sensor calibration method with dynamic beamforming solves the problems of calibration lag, weak anti-interference ability and low computational efficiency of traditional distributed acoustic sensor systems in complex marine environments through three major innovations: real-time iterative calibration, low-complexity robust beamforming and multi-physical field coupling compensation.

[0342] (14) Based on the three-dimensional velocity field inversion algorithm of Green’s function, a three-level architecture of “Green’s function-fluid-solid coupling-deep learning” is proposed to solve the multi-physics field coupling problem in three-dimensional flow field inversion. This method breaks through the limitations of traditional ocean current monitoring technology in terms of resolution, energy consumption and environmental adaptability, and provides a high-precision, low-cost three-dimensional velocity field inversion method for global ocean dynamics research.

[0343] (15) Argo buoy real-time calibration system, through the three major innovations of multi-source data fusion, dynamic abnormal response and lightweight edge computing, solves the problems of calibration lag, weak anti-interference ability and low computing efficiency of traditional Argo buoys in complex ocean environments.

[0344] Reference Figure 2 The embodiment of the present application further provides an ocean current measurement system based on distributed acoustic sensing, which can implement the above-mentioned ocean current measurement method based on distributed acoustic sensing, and the system includes:

[0345] A data acquisition unit, used to acquire data collected by a plurality of sensors arranged on the submarine optical fiber as sensing data;

[0346] A data preprocessing unit, used for performing spatiotemporal alignment on the sensing data collected by each of the sensors to synchronize the sensing data;

[0347] A time-frequency conversion unit, used to convert the sensing data after time-space alignment into a signal in the frequency domain as an original signal;

[0348] A tidal removal unit, used to remove the tidal signal from the original signal to obtain an ocean current signal;

[0349] The ocean current inversion unit is used to calculate the Doppler frequency shift according to the ocean current signal, and then obtain the ocean current data based on the Doppler frequency shift inversion.

[0350] It can be understood that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0351] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the method of the embodiment of the present application when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0352] It can be understood that the contents of the above method embodiments are all applicable to the embodiments of the present device, the functions specifically implemented by the embodiments of the present device are the same as those of the method of the present application, and the beneficial effects achieved are also the same as those achieved by the method of the present application.

[0353] See also Figure 3 , Figure 3 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0354] The processor 301 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0355] The memory 302 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 302 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 302, and the processor 301 calls and executes the methods of the embodiments of this application;

[0356] Input / output interface 303, used to implement information input and output;

[0357] The communication interface 304 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0358] A bus 305 that transmits information between the various components of the device (e.g., the processor 301, the memory 302, the input / output interface 303, and the communication interface 304);

[0359] The processor 301 , the memory 302 , the input / output interface 303 and the communication interface 304 are connected to each other in communication within the device via the bus 305 .

[0360] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program, and the computer program implements the method of the present application when executed by a processor.

[0361] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0362] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0363] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0364] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0365] The system embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0366] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0367] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0368] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0369] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or units, which can be electrical, mechanical or other forms.

[0370] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0371] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0372] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0373] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A method for measuring ocean currents based on distributed acoustic sensing, characterized in that: The method comprises the following steps: acquiring data collected by a plurality of sensors arranged on a submarine optical fiber as sensing data; Performing spatiotemporal alignment on the sensor data collected by each of the sensors to synchronize the sensor data; Converting the time-space aligned sensing data into a signal in the frequency domain as an original signal; Removing the tidal signal from the original signal to obtain an ocean current signal; The Doppler frequency shift is calculated according to the ocean current signal, and then the ocean current data is obtained by inverting the Doppler frequency shift.

2. The method for measuring ocean currents based on distributed acoustic sensing according to claim 1, characterized in that: The step of performing spatiotemporal alignment on the sensing data collected by each sensor to synchronize the sensing data comprises the following steps: The sensing data collected by each of the sensors is temporally and spatially aligned based on GPS data and tidal data to synchronize the sensing data; wherein the sensing data includes vibration signals, temperature, salinity and turbidity.

3. The ocean current measurement method based on distributed acoustic sensing according to claim 1, characterized in that: The step of converting the time-space aligned sensing data into a signal in the frequency domain as an original signal comprises the following steps: The sensing data after time-space alignment is converted into a signal in frequency domain form as the original signal by fast Fourier transform; The expression of the fast Fourier transform is: Where ω is the angular frequency, k is the wave number vector, and r is the position coordinate; The expression of the angular frequency is: Among them, σ is the surface tension coefficient of seawater, and α is the nonlinear coefficient.

4. The ocean current measurement method based on distributed acoustic sensing according to claim 1, characterized in that: Before removing the tidal signal from the original signal to obtain the ocean current signal, the method further includes a step of determining the tidal signal, and the step of determining the tidal signal includes the following steps: Using multi-beam sonar data, satellite altimeter data and vibration signals in the sensor data as multi-source data, and synchronizing the multi-source data; The synchronized multi-source data are subjected to wavelet packet denoising and time-space alignment and then converted into a signal in the frequency domain, and then feature screening is performed to obtain a target vibration signal; Calculating a phase difference between the target vibration signals corresponding to any two sensors separated by a set distance; smoothing the phase difference by a sliding window to suppress transient noise; Inputting the phase difference, the multi-beam sonar data and the satellite altimeter data into an LSTM network to obtain a tidal phase-amplitude curve output by the LSTM network; The tidal signal is obtained by reconstructing the tidal phase-amplitude curve.

5. The method for measuring ocean currents based on distributed acoustic sensing according to claim 4, characterized in that: The step of converting the synchronized multi-source data into a signal in frequency domain form comprises the following steps: constructing a local water depth gradient matrix based on the multibeam sonar data; Correcting the wave number vector according to the local water depth gradient matrix; The synchronized multi-source data are converted into a signal in frequency domain according to the corrected wave number vector.

6. The method for measuring ocean currents based on distributed acoustic sensing according to claim 1, characterized in that: The method of calculating the Doppler frequency shift according to the ocean current signal comprises the following steps: Calculating the phase difference between adjacent sensors according to the ocean current signal through a cross-correlation function, and calculating the relative flow velocity of the ocean current between adjacent sensors in combination with the sound velocity; The expression of the relative flow velocity is: Wherein, Δν represents the relative flow velocity, f d is the Doppler frequency shift, f0 is the transmitting frequency, and θ is the sound beam installation angle; determining a flow velocity gradient based on the relative flow velocity; Determine a dynamic Doppler frequency shift mapping model in combination with the flow velocity gradient and then calculate the Doppler frequency shift; The dynamic Doppler shift mapping model is: Where v is the speed of the sound source, is the flow velocity gradient, σ bed is the reflectivity of the seabed.

7. The ocean current measurement method based on distributed acoustic sensing according to claim 1, characterized in that: The method of obtaining ocean current data based on the Doppler frequency shift inversion comprises the following steps: Based on the Green function and flow field coupling algorithm, the distributed acoustic sensor-flow field relationship model is established as follows: u(x,t)=∫G(x,x′)·f(x′,t)dx′; Wherein, G is the Green's function, and f is the flow field source term; The Green's function and the distributed acoustic sensor-flow field relationship model are combined to establish the joint inversion equation of the acoustic wave propagation equation and the flow field dynamics as follows: Wherein, P is the sound pressure field, c(x) is the sound velocity profile, and Q is the source term excited by each of the sensors; The ocean current data is obtained by inverting the joint inversion equation of the acoustic wave propagation equation and the flow field dynamics.

8. An ocean current measurement system based on distributed acoustic sensing, characterized in that: The system comprises: A data acquisition unit, used to acquire data collected by a plurality of sensors arranged on the submarine optical fiber as sensing data; A data preprocessing unit, used for performing spatiotemporal alignment on the sensing data collected by each of the sensors to synchronize the sensing data; A time-frequency conversion unit, used to convert the sensing data after time-space alignment into a signal in the frequency domain as an original signal; A tidal removal unit, used to remove the tidal signal from the original signal to obtain an ocean current signal; The ocean current inversion unit is used to calculate the Doppler frequency shift according to the ocean current signal, and then obtain the ocean current data based on the Doppler frequency shift inversion.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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