Ocean current measurement method and system based on distributed acoustic sensing

Abstract

This application discloses a method and system for measuring ocean currents based on distributed acoustic sensing, relating to the field of data processing technology. The method includes: acquiring data from multiple sensors installed on a seabed optical fiber as sensing data; performing spatiotemporal alignment on the sensing data acquired by each sensor to synchronize the sensing data; converting the spatiotemporally aligned sensing data into a frequency domain signal as the original signal; removing tidal signals from the original signal to obtain an ocean current signal; calculating the Doppler frequency shift based on the ocean current signal, and then retrieving ocean current data based on the Doppler frequency shift. This application improves the diversity and reliability of the data by fusing data acquired from multiple sensors and performing time alignment and Doppler frequency shift calculations, thereby improving the accuracy of ocean current retrieval.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to an ocean current measurement method and system based on distributed acoustic sensing. Background Technology

[0002] The existing ocean current monitoring technologies have the following technical problems: (1) Mechanical current meter: The measurement dimension is limited, and it can only measure one-dimensional velocity, and cannot obtain three-dimensional flow field information. Low velocity measurement failure: The mechanical rotor has large inertia and is prone to stop rotating in low velocity or turbulent environment, resulting in data distortion. Susceptible to environmental influence: Contact measurement will interfere with the flow field, and the components are prone to corrosion and jamming, resulting in high maintenance costs. (2) Electromagnetic current meter: The anti-interference ability is weak and it is susceptible to changes in the geomagnetic field and electromagnetic interference. Frequent calibration is required and the operation is complicated. Limited applicable scenarios: It cannot measure the 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 design and long-term operating costs are high. (3) Acoustic Doppler technology (ADCP / ADV): ​​It 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 polar regions and deep seas. Measurement blind zone: 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 economically viable. (4) Satellite remote sensing technology: Two-dimensional planar observation can only monitor parameters such as sea surface height and temperature, and cannot obtain information on water profile flow velocity. Low spatiotemporal resolution: Long revisit cycles (such as Argo buoys which require several weeks to months) make it difficult to capture rapid changes. Data silo problem: Difficulty in integrating multi-source data affects comprehensive analysis capabilities.

[0003] The existing technologies generally have the following technical problems: (1) poor adaptability to extreme environments: equipment is easily damaged in deep sea, polar regions and other areas, and maintenance is difficult; (2) insufficient data real-time performance: traditional equipment needs to transmit data back periodically and cannot respond to environmental changes in real time. Summary of the Invention

[0004] The main objective of this application is to propose an ocean current measurement method and system based on distributed acoustic sensing, so as to improve the accuracy of ocean current measurement.

[0005] To achieve the above objectives, one aspect of this application proposes an ocean current measurement method based on distributed acoustic sensing, the method comprising the following steps:

[0006] Data collected by multiple sensors installed on the submarine optical fiber is used as sensing data;

[0007] The sensing data collected by each of the sensors are spatiotemporally aligned to synchronize the sensing data;

[0008] The spatiotemporally aligned sensing data is converted into a frequency domain signal as the original signal.

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

[0010] The Doppler frequency shift is calculated based on the ocean current signal, and then ocean current data is obtained by inversion based on the Doppler frequency shift.

[0011] In some embodiments, the spatiotemporal alignment of the sensing data collected by each of the sensors to synchronize the sensing data includes the following steps:

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

[0013] In some embodiments, converting the spatiotemporally aligned sensing data into a frequency domain signal as the original signal includes the following steps:

[0014] The time-space aligned sensing data is converted into a frequency domain signal using a fast Fourier transform, which serves as the original signal.

[0015] The expression for the Fast Fourier Transform is:

[0016]

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

[0018] The expression for the angular frequency is:

[0019]

[0020] Where 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, the step of determining the tidal signal including the following steps:

[0022] Multibeam sonar data, satellite altimeter data, and vibration signals from the sensor data are used as multi-source data, and the multi-source data are synchronized.

[0023] The synchronized multi-source data is then subjected to wavelet packet denoising and spatiotemporal alignment before being converted into a frequency domain signal. Feature filtering is then performed to obtain the target vibration signal.

[0024] Calculate the phase difference between the target vibration signals corresponding to two sensors that are arbitrarily separated by a set distance;

[0025] The phase difference is smoothed using a sliding window to suppress transient noise;

[0026] The phase difference, the multibeam sonar data, and the satellite altimeter data are input into the LSTM network to obtain the tidal phase-amplitude curve output by the LSTM network.

[0027] The tidal signal is reconstructed based on the tidal phase-amplitude curve.

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

[0029] A local water depth gradient matrix is ​​constructed based on the multibeam sonar data;

[0030] The wavenumber vector is corrected based on the local depth gradient matrix;

[0031] The synchronized multi-source data is converted into a frequency domain signal based on the corrected wavenumber vector.

[0032] In some embodiments, calculating the Doppler frequency shift based on the ocean current signal includes the following steps:

[0033] The phase difference between adjacent sensors is calculated using a cross-correlation function and based on the ocean current signal, and the relative velocity of the ocean current between adjacent sensors is calculated by combining the sound velocity.

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

[0035]

[0036] Where Δυ represents the relative flow velocity, f d Here, f0 is the transmission frequency, and θ is the beam installation angle;

[0037] Determine the velocity gradient based on the relative velocity;

[0038] The Doppler frequency shift is then calculated by combining the flow velocity gradient with a dynamic Doppler frequency shift mapping model.

[0039] The dynamic Doppler frequency shift mapping model is as follows:

[0040]

[0041] Where v is the velocity of the sound source. Let σ be the velocity gradient. bed This refers to the reflectivity of the seabed sediment.

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

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

[0044] u(x,t)=∫G(x,x′)·f(x′,t)dx′;

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

[0046] Combining the Green's function with the distributed acoustic sensing-flow field relationship model, the joint inversion equation of the acoustic wave propagation equation and the flow field dynamics is established as follows:

[0047]

[0048] Where 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 are obtained by inversion using the joint inversion equation of the sound wave propagation equation and the flow field dynamics equation.

[0050] To achieve the above objectives, another aspect of this application proposes an ocean current measurement system based on distributed acoustic sensing, the system comprising:

[0051] The data acquisition unit is used to acquire data collected by multiple sensors installed on the submarine optical fiber as sensing data.

[0052] A data preprocessing unit is used to perform spatiotemporal alignment on the sensing data collected by each of the sensors in order to synchronize the sensing data.

[0053] The time-frequency conversion unit is used to convert the time-space aligned sensing data into a frequency domain signal as the original signal.

[0054] The tidal removal unit is used to remove the tidal signal from the original signal to obtain the ocean current signal;

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

[0056] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0057] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

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

[0059] This application acquires data from multiple sensors mounted on a submarine optical fiber as sensing data; it performs spatiotemporal alignment on the sensing data collected by each sensor to synchronize the data; it converts the spatiotemporally aligned sensing data into a frequency domain signal as the original signal; it removes tidal signals from the original signal to obtain ocean current signals; it calculates the Doppler frequency shift based on the ocean current signal, and then retrieves ocean current data based on the Doppler frequency shift. This application improves the diversity and reliability of the data by fusing data collected from multiple sensors and performing time alignment and Doppler frequency shift calculations, thereby improving the accuracy of ocean current retrieval. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 A flowchart illustrating an ocean current measurement method based on distributed acoustic sensing, provided for an embodiment of this 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 this application;

[0063] Figure 3 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying 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 those of this application; they are merely examples of systems and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

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

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

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

[0068] Before providing a detailed description of the embodiments of this application, some related technologies involved in the embodiments of this application will be described first, as follows:

[0069] Ocean currents are a core parameter of the marine dynamic environment, directly impacting global climate stability, ecosystem balance, and marine resource development (such as shipping safety and fishery resource distribution). The development of monitoring technologies began with drifting bottle observations in the 17th century, and was gradually improved with advancements in mechanical current meters (1905), acoustic Doppler current profilers (ADCP, 1970s), and satellite remote sensing. Traditional methods, relying on mechanical current meters (such as the Eckermann current meter) and drifting buoys, suffer from low measurement accuracy and susceptibility to interference. Modern technologies combine satellite remote sensing (such as Argo buoys), acoustic Doppler technology, and artificial intelligence to achieve large-scale, high-precision, and multi-parameter integrated monitoring.

[0070] Early methods of drift bottle current measurement estimated flow velocity by tracking the drift path of the drifting object; however, this is now only used for surface current observation. Drift buoy current measurement utilizes GPS / satellite positioning to track the buoy's trajectory. These are categorized into surface buoys (shallow, less than 3m) and neutral buoys (deep, up to 6000m), such as the Argo buoy, which has over 3800 deployed globally. Fixed-point current measurement includes platform / anchored current measurement, using mechanical (Andraco current meter) or acoustic (ADCP) equipment to continuously monitor small-scale flow fields over long periods, suitable for detailed local studies. Submersible mooring current measurement involves suspending a self-contained ADCP or Andraco current meter on the mooring moor, monitoring single or multiple layers of flow velocity, with a deployment cycle of ≥6 months. Mobile current measurement utilizes a shipborne ADCP or a single-point acoustic Doppler current meter (such as an Andraco RCM) for mobile measurements, suitable for large-scale rapid surveys. Remote sensing and numerical modeling 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 aspects:

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

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

[0074] ③ It has strong anti-interference ability, strong sound wave signal penetration, is less affected by environmental interference such as sea waves and wind noise, and has high data stability.

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

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

[0077] ⑥ Potential for multi-parameter fusion: Simultaneously monitor parameters such as temperature, salinity, and internal waves, and combine acoustic tomography technology to invert the three-dimensional flow field.

[0078] Table 1 compares the existing flow measurement technology with the distributed acoustic sensing flow measurement technology of this application.

[0079] Table 1

[0080]

[0081] Therefore, this application provides a method and system for measuring ocean currents based on distributed acoustic sensing, relating to the field of data processing technology. The method and system for measuring ocean currents based on distributed acoustic sensing provided in this application can be applied to a terminal, a server, or software running on a terminal or server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or vehicle terminal, but is not limited thereto; the server can be configured as an independent physical server, a server cluster composed of multiple physical servers, or a distributed system, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application implementing a method for measuring ocean currents based on distributed acoustic sensing, but is not limited to the above forms.

[0082] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: 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, and distributed computing environments including any of the above systems or devices. This 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. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0083] Reference Figure 1 This application provides an ocean current measurement method based on distributed acoustic sensing. This method may include, but is not limited to, steps S100 to S140, as detailed below:

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

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

[0086] S120: Convert the spatiotemporally aligned sensing data into a frequency domain signal as the original signal;

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

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

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

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

[0091] Optionally, converting the spatiotemporally aligned sensing data into a frequency domain signal as the original signal includes the following steps:

[0092] The time-space aligned sensing data is converted into a frequency domain signal using a fast Fourier transform, which serves as the original signal.

[0093] The expression for the Fast Fourier Transform is:

[0094]

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

[0096] The expression for the angular frequency is:

[0097]

[0098] Where 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 includes a step of determining the tidal signal, which includes the following steps:

[0100] Multibeam sonar data, satellite altimeter data, and vibration signals from the sensor data are used as multi-source data, and the multi-source data are synchronized.

[0101] The synchronized multi-source data is then subjected to wavelet packet denoising and spatiotemporal alignment before being converted into a frequency domain signal. Feature filtering is then performed to obtain the target vibration signal.

[0102] Calculate the phase difference between the target vibration signals corresponding to two sensors that are arbitrarily separated by a set distance;

[0103] The phase difference is smoothed using a sliding window to suppress transient noise;

[0104] The phase difference, the multibeam sonar data, and the satellite altimeter data are input into the LSTM network to obtain the tidal phase-amplitude curve output by the LSTM network.

[0105] The tidal signal is reconstructed based on the tidal phase-amplitude curve.

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

[0107] A local water depth gradient matrix is ​​constructed based on the multibeam sonar data;

[0108] The wavenumber vector is corrected based on the local depth gradient matrix;

[0109] The synchronized multi-source data is converted into a frequency domain signal based on the corrected wavenumber vector.

[0110] Optionally, calculating the Doppler frequency shift based on the ocean current signal includes the following steps:

[0111] The phase difference between adjacent sensors is calculated using a cross-correlation function and based on the ocean current signal, and the relative velocity of the ocean current between adjacent sensors is calculated by combining the sound velocity.

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

[0113]

[0114] Where Δυ represents the relative flow velocity, f d Here, f0 is the transmission frequency, and θ is the beam installation angle;

[0115] Determine the velocity gradient based on the relative velocity;

[0116] The Doppler frequency shift is then calculated by combining the flow velocity gradient with a dynamic Doppler frequency shift mapping model.

[0117] The dynamic Doppler frequency shift mapping model is as follows:

[0118]

[0119] Where v is the velocity of the sound source. Let σ be the velocity gradient. bed This refers to the reflectivity of the seabed sediment.

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

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

[0122] u(x,t)=∫G(x,x′)·f(x′,t)dx′;

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

[0124] Combining the Green's function with the distributed acoustic sensing-flow field relationship model, the joint inversion equation of the acoustic wave propagation equation and the flow field dynamics is established as follows:

[0125]

[0126] Where 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 are obtained by inversion using the joint inversion equation of the sound wave propagation equation and the flow field dynamics equation.

[0128] The following section will provide a detailed introduction and explanation of the solutions in the embodiments of this application, using specific application examples.

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

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

[0131] Multi-node distributed deployment: Fiber optic cables are laid along the seabed topography in key sea areas (such as straits and ocean current confluence areas), with node spacing of 500-2000 meters, covering water depths of 0-6000 meters. Repeater optimization: High-frequency signal enhancement modules are installed at the fiber optic repeaters to improve the signal-to-noise ratio of the far-end signal through HLLB (High-Loss Loopback) paths. Tensile and corrosion resistant design: Armored loose-tube optical fibers are used, with a tensile strength >600N and resistance to seawater corrosion (fixed with UV-curing adhesive).

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

[0133] ① Layered structure design.

[0134] Global marine fiber optic cables employ a multi-layered composite structure. Core components include: Fiber optic core: transmits optical signals, protected by an outer layer of polyethylene or polyester resin. Reinforcement system: high-strength steel wire strands (single-armored / double-armored) provide tensile strength; deep-sea sections utilize double armor (DA2) or rock armor (RA2) to withstand high voltage. Sheath system: an aluminum waterproof layer prevents seawater penetration.

[0135] Copper or aluminum tubes are used for power supply to the repeater, and paraffin and alkane layers prevent hydrogen permeation.

[0136] ② Functional module integration.

[0137] Repeaters: Deployed every 40-60 kilometers, powered by submarine cables, amplifying signals and extending transmission distance (e.g., the SAIL submarine cable uses 100G technology). Branch units: Connect networks in different sea areas, supporting multi-country data interoperability (e.g., the 2Africa cable branches to South Africa and India). Junction boxes: Seal and protect optical cable splices, with a pressure resistance rating up to 6000 meters deep.

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

[0139] ① Route planning and surveying.

[0140] Multimodal data fusion: Combining satellite remote sensing (such as Google Earth Engine), multibeam sonar, and historical submarine cable fault data to avoid high-risk areas such as anchorages and volcanic zones. Environmental parameter assessment: Measuring water depth (shallow sea < 500 meters / deep sea > 1000 meters), seabed composition (sand / rock), and ocean current intensity to select the appropriate armor type.

[0141] ② Laying vessel and traction system.

[0142] The dedicated cable-laying vessel is equipped with an automatic tension control system, supporting a daily cable laying distance of 150 kilometers. The cable-laying method is as follows: Shallow sea section (<200 meters): towed laying, buried at a depth of 3 meters (using an underwater robot to flush the trench); Deep sea section (>2000 meters): direct immersion, utilizing its own weight to cover the seabed.

[0143] ③ Relay and maintenance system.

[0144] Dynamic calibration: Repeater performance is verified using Argo buoy data, and the power supply voltage is adjusted quarterly (e.g., within ±3%). Intelligent monitoring: Integrated fiber optic vibration sensors detect abnormal events such as shark bites and fishing boat trawling in real time. A gradient pressure compensation design is employed; for example, the RA2 rock armor can withstand water pressure at a depth of 11,000 meters (approximately 1100 atmospheres). A sharkskin-inspired texture is added to the outer layer to reduce the probability of organism adhesion.

[0145] Step 2: Data acquisition and preprocessing.

[0146] The specific steps of data preprocessing are as follows:

[0147] ① Data cleaning and quality control.

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

[0149] Outlier detection uses statistical methods: the 3σ principle is used to remove data that exceed three times the standard deviation.

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

[0151] ② Spatiotemporal alignment and registration.

[0152] Multi-source data fusion: Remote sensing data (such as Sentinel-3) is converted to the WGS84 coordinate system using RPC parameters; buoy data is spatiotemporally interpolated with DAS data using Kalman filtering. 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] ③ Signal enhancement and feature extraction.

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

[0155] Spectral analysis extracts the 0.2-2Hz gravity wave frequency band features using Morlet wavelet transform;

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

[0157]

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

[0159] ④ Data 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: Storage clusters are built based on Hadoop HDFS, with a single cluster supporting petabyte-level data storage and latency <50ms.

[0162] The key technology implementation includes three parts:

[0163] ①Demodulation of fiber optic sensor signals.

[0164] The vibration amplitude and phase difference are calculated using polarization diversity detection (PDD) technology, employing two orthogonally polarized signals. Signal acquisition frequency: 192kHz (covering the 0.1-100Hz frequency band), dynamic range ±100dB3.

[0165] ② Automated quality control framework.

[0166] Real-time Calibration: The Argo program acquires high-precision seawater temperature (±0.005℃) and salinity (±0.01℃) data through globally distributed Automated Profiling Buoys (ARGO). However, seawater conductivity sensors are susceptible to oil spills, biofouling, and physical deformation, leading to accumulated errors over long periods. Real-time calibration technology addresses the timeliness limitations of traditional offline calibration by dynamically correcting sensor biases, ensuring data availability. The accuracy of flow velocity measurements is verified daily using Argo buoy data, and calibration coefficients are dynamically updated. Anomaly Warning: When data from three consecutive sampling points deviates from the mean by more than 2σ, an alarm is triggered and a backup channel is activated. Real-time calibration implementation process:

[0167] Profile data quality control:

[0168] Temperature / Salinity Threshold Filtration: Temperature limited to -2.5 to 40°C, salinity limited to 2 to 41, pressure range -5 × 10⁻⁵. 4 ~2200×10 4 Pa.

[0169] Density consistency check: The density difference between adjacent layers must not exceed 0.03 kg / m³. 3 To prevent density inversion anomalies. Anomaly profile identification: TS curve verification: Identify bottom temperature and salinity anomalies (such as salinity abrupt changes > 0.08) by comparing historical data. Duplicate profile detection: If two consecutive profiles are completely identical, they are marked as faulty data.

[0170] Trajectory data quality control: Positioning anomaly detection: Distance threshold: An alarm is triggered when the distance between adjacent positioning points exceeds the satellite positioning accuracy (e.g., 200m). Drift speed limit: A maximum limit of 3m / s is set to eliminate extreme motion interference. Trajectory smoothing: Kalman filtering is used to fuse satellite positioning and inertial sensor data to correct drift trajectories.

[0171] Real-time correction algorithm:

[0172] Pressure correction: The pressure value measured during the floating phase at sea surface is used to correct the zero-point drift of the underwater pressure sensor.

[0173] Salinity correction: Based on the conductivity offset parameter determined by time-delay quality control, real-time salinity data is dynamically corrected.

[0174] ③ Multi-parameter joint analysis.

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

[0176] Where T, S, and U represent temperature, salinity, and flow rate, respectively.

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

[0178] ①Signal acquisition and preprocessing.

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

[0180] Preprocessing steps:

[0181] Noise filtering: High-frequency noise is removed using wavelet transform (db4 basis function, decomposed into 5 layers);

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

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

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

[0185] Time-frequency domain conversion:

[0186]

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

[0188] Signal separation: Ocean current signals (low frequency, long wave number) and tidal signals (high frequency, short wave number) are separated by setting a threshold (signal-to-noise ratio > 15dB).

[0189] ③ Modeling of nonlinear dispersion relationships.

[0190] Traditional model correction:

[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 a nonlinear coefficient, retrieved from multibeam sonar data.

[0197] Submarine topography compensation: Construct a local depth gradient matrix by combining DEM data, and correct the spatial distribution of wavenumber vector k.

[0198] ④ Tidal modulation signal removal.

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

[0200] Multivariate input fusion mechanism:

[0201] Multiple environmental parameters, including flow velocity, water level, air pressure, wind speed, and rainfall, were used as input features to construct a multiple-input single-output (MISO) prediction model for LSTM. Wavelet packet decomposition (db8 basis, 6-level decomposition) was employed to denoise the non-stationary tidal signal, retaining key information in the 0.05-0.3Hz frequency band. Sequence data was generated using a sliding window (window length = 12 hours), with a time step size of 6 hours.

[0202] Optimization of dynamic attention mechanism:

[0203] A fusion of short-term and long-term attention is employed: Short-term attention (local window = 1 hour): captures the instantaneous changes during the transition between high and low tides. Long-term attention (global window = 24 hours): captures the long-term periodic patterns of astronomical tides. Dynamic calibration of attention weights: The Tidal Sensitivity Index (TMI) is introduced as an attention gating parameter, as shown in the following formula:

[0204] α t =σ(W1·[h t ]+W2·[TMI t ]+b);

[0205] Where h t In hidden state, TMI t This is the tidal modulation index.

[0206] Real-time enhancement techniques:

[0207] Knowledge distillation technology is employed to compress the pre-trained Seq2Seq LSTM model (1.2M parameters), improving inference speed to 30 frames per second. Deployed on an FPGA platform, combined with fixed-point quantization (INT8) technology, the time required for a single prediction is reduced. Dual-channel synchronous detection achieves accurate separation and suppression of tidal signals through spatial correlation analysis of the two signals, while preserving ocean current characteristics.

[0208] Dual-channel data acquisition and synchronization:

[0209] Two acoustic sensor nodes, spaced 50-200m apart (covering a typical tidal horizontal gradient range), are deployed on the same submarine optical cable and share a BeiDou / GPS clock (synchronization accuracy ±1μs). Tidal-related frequency band signals are preserved through bandpass filtering (0.05-0.3Hz). Time delay is calculated based on the cross-correlation function to correct for signal offset between nodes.

[0210]

[0211] Where S1 and S2 are two original signals, t align This is the optimal latency.

[0212] Dual-channel signal feature extraction:

[0213] Perform an FFT on the preprocessed signal to 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 interference zone.

[0217] Construction and removal of dynamic tidal models:

[0218] By combining historical tidal data (Tides-2000 global model) with real-time water level gauge data, a spatiotemporally enhanced tidal prediction field is generated. Dual-channel joint filtering: By minimizing the sum of squared residuals, the tidal prediction signal is separated from the two signals.

[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] Calculate the signal-to-noise ratio (SNR) improvement using a sliding window (window length = 1 hour):

[0225]

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

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

[0228] Doppler frequency shift formula:

[0229]

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

[0231] 3D flow velocity inversion:

[0232] Combining the wavenumber vector k obtained from the FK transform with the Doppler frequency shift, the velocity vector is solved using the following system of equations:

[0233]

[0234] A graph neural network (GNN) is introduced to optimize the inversion accuracy. Input features include wave number, frequency, tidal residuals, etc.

[0235] ⑥ Automated fitting and result optimization.

[0236] Data-driven fitting: A joint feature space including flow velocity, direction, and dispersion parameters is constructed; Gaussian Process Regression (GPR) is used to achieve nonlinear least-squares fitting, reducing human intervention bias. Real-time calibration: The model is validated daily using Argo buoy data, and the dispersion relationship and flow velocity mapping function are dynamically updated.

[0237] Step 4: Removal of tidal modulation effect.

[0238] ① Data acquisition and preprocessing.

[0239] Multi-source data synchronization:

[0240] Fiber optic vibration signal: sampled at 192kHz per second, covering the 0.1-100Hz frequency band;

[0241] Multibeam sonar data: Seabed topography elevation is acquired every 10 minutes (10cm resolution);

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

[0243] Preprocessing steps:

[0244] Wavelet packet denoising: The sym8 wavelet basis is used, with a decomposition level of 5 layers, and the threshold is set to 1.5 times the global energy mean.

[0245] Spatiotemporal alignment: Based on BeiDou / GPS clock synchronization, the time delay error between nodes is <10μs;

[0246] Feature filtering: Retain signals in the 0.01-1Hz frequency band and remove high-frequency ship noise (>5Hz).

[0247] ② Dual-channel phase difference extraction.

[0248] Signal alignment between adjacent nodes: Select fiber optic 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] Phase difference is smoothed by using a sliding window (window length 1 hour) to suppress transient noise.

[0251] Tidal component identification: Extraction using Fourier transform The spectrum was analyzed to identify the main tidal components (M2, S2, K1, etc.); an initial phase-amplitude relationship model was established based on the tidal stellar period (e.g., M2 wave 12.42h).

[0252] ③LSTM network time series prediction.

[0253] Network architecture:

[0254] Input layer: fusion Multibeam sonar elevation (H), satellite altimeter sea level (η);

[0255] Hidden layers: Two bidirectional LSTM layers (128 units per layer), with an attention mechanism introduced to enhance temporal correlation;

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

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

[0258] ④ Tidal signal reconstruction and removal.

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

[0260]

[0261] Where An, ωn, and φn are the parameters of each tidal constituent.

[0262] Residual signal extraction:

[0263] Original vibration signal minus tidal signal:

[0264]

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

[0266] ⑤ Multibeam sonar data compensation.

[0267] Seabed topography correction:

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

[0269]

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

[0271] Optimization of dispersion relation:

[0272] By introducing the modified wavenumber vector into the nonlinear OSGW model, the accuracy of dispersion calculation in shallow water areas is improved.

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

[0274] Multi-source data fusion preprocessing:

[0275] A hierarchical data fusion framework is adopted: Level 1: raw multibeam echo data (70% weight); Level 2: tidal height data retrieved from real-time satellite altimeters (15% weight); Level 3: seabed sediment echo characteristic parameters (15% weight). The signal-to-noise ratio of each data source is evaluated in real time using Kalman filtering, and the weight allocation is automatically optimized.

[0276] Integrated array self-calibration:

[0277] Focused beamforming technology: Real-time self-calibration of the transducer array (error < 0.5°) is achieved through field experimental data repositioning compensation. Dynamic beam pointing control: Real-time attitude data (bow and roll) is fed back to the transmit / receive array to ensure that the sound beam always points directly downwards.

[0278] Deploy a distributed hydrophone array (≥16 channels) covering a horizontal ±60° solid angle with a sampling rate ≥192kHz, supporting the capture of the spatiotemporal correlation of environmental noise. Integrate MEMS inertial sensors (three-axis gyroscope + accelerometer) to monitor the device's motion status 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 time delay matrix based on the generalized cross-correlation (GCC-PHAT) algorithm, and estimate the signal source azimuth by combining it with 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] Calibration is terminated when the position change between two iterations is less than 0.1 mm. Blocking matrix reconstruction: The interference noise covariance matrix (INCM) is projected onto the signal subspace using a projection matrix to eliminate interference components.

[0282]

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

[0284] An iterative mismatch approximation method is adopted, relying only on prior knowledge of array geometry and signal orientation, thus reducing computational complexity. A multi-level alarm system is implemented: Level 1 alarm (threshold ± 3σ): triggers audible and visual alarms and records data during abnormal periods; Level 2 alarm (threshold ± 5σ): automatically switches to a backup sensor and initiates an emergency calibration process.

[0285] Multiphysics coupling compensation:

[0286] Green's function-fluid-structure interaction model: A joint inversion framework for the acoustic wave propagation equation and flow field dynamics is established, dynamically correcting the sound velocity distribution (lateral resolution ≤ 10 cm); seabed reflectivity parameters (obtained through multi-frequency hybrid imaging) are introduced to correct angular response deviations. Wavelet packet decomposition is used to extract the spatiotemporal features of the sound field, compressing the original data to improve computational efficiency.

[0287] Dynamic abnormal data repair:

[0288] An anomalous noise detection model based on YOLOv7-Tiny was trained, achieving a detection speed of 30 frames per second. Spatial domain restoration of detected noise was performed using Restoration algorithms (such as Total Variation TV). Spatiotemporal consistency verification: The Data Consistency Index (DCI) was calculated using a sliding window (window length = 1 hour), and anomalous stripes with a DCI < 0.8 were automatically removed.

[0289] ⑥ Dynamic calibration and verification.

[0290] Argo buoy real-time verification: Three Argo buoy locations are randomly selected daily, and the predicted tidal phase-amplitude values ​​are compared with the actual observed values; the LSTM network weights are dynamically adjusted based on maximum likelihood estimation (MLE). Visual monitoring: A web-based dashboard is developed to display the tidal signal removal effect in real time (signal-to-noise ratio SNR > 20dB is considered acceptable).

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

[0292] Three-dimensional velocity inversion includes the following two parts:

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

[0294] ① Data acquisition and preprocessing.

[0295] Distributed acoustic sensing deployment: Submarine optical cables are used as a distributed acoustic array with a channel spacing of 4 meters, a sampling rate of 500Hz, and coverage of the 0.05-0.2Hz frequency band. Undersea noise caused by ocean currents is captured using environmental noise interferometry, avoiding active sound source interference with marine life. Preprocessing steps: Wavelet packet denoising: A db8 wavelet basis is used, with a decomposition level of 6 layers, and the threshold is set to 2.5 times the local energy mean. Spatiotemporal alignment: Based on BeiDou / GPS clock synchronization, the inter-node delay error is <5μs.

[0296] ②Doppler frequency shift extraction.

[0297] Frequency domain analysis: A Fast Fourier Transform (FFT) is performed on the preprocessed signal to extract the Doppler shift component within the 0.05-0.2Hz frequency band. The phase difference between adjacent nodes is calculated using the cross-correlation function, and the relative flow velocity is calculated using the speed of sound (1500 m / s).

[0298]

[0299] Where f d f0 is the Doppler frequency shift, f0 is the transmission frequency (200-7000Hz), and θ is the beam installation angle.

[0300] Dynamic Doppler frequency shift mapping model:

[0301]

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

[0303] Adaptive weight optimization: The Tidal Sensitivity Index (TMI) is introduced as a weighting coefficient, as shown in the following formula:

[0304]

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

[0306] A hybrid architecture combining convolutional neural networks (CNN) and recurrent neural networks (RNN) is employed. The input is a noisy time-frequency map (STFT resolution 0.5s × 0.1Hz), and the output is a Doppler frequency shift time series. Wasserstein GAN is used to generate synthetic data from extreme noisy environments (such as typhoons) to improve the model's generalization ability.

[0307] (2) Ocean current inversion process.

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

[0309] Frequency domain beam stretching: A frequency domain waveform stretching method is used to restore the compressed time-frequency data to a high-resolution flow velocity field, achieving a spatial resolution of 10 cm and a temporal resolution of 10 minutes. Feature engineering: Extraction of the flow velocity gradient tensor. We construct a flow field dynamics feature set by combining the vorticity component (ω).

[0310] ② Construction of the inversion model.

[0311] 3D velocity field reconstruction:

[0312] A distributed acoustic sensing-flow field relationship model is established based on the Green's function and flow field coupling algorithm:

[0313] u(x,t)=∫G(x,x′)·f(x′,t)dx′;

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

[0315] By combining the Green's function with the flow field coupling model, a joint inversion framework for the acoustic wave propagation equation and flow field dynamics is established:

[0316]

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

[0318] Dynamic sound velocity profile compensation: by introducing the velocity gradient tensor Correcting the sound velocity distribution eliminates sound velocity deviations caused by temperature / salinity stratification, improving inversion accuracy (lateral resolution ≤10cm).

[0319] Boundary condition optimization: A hybrid boundary condition (HBC) Green's function is adopted, combined with multibeam sonar bathymetry data to construct a seabed topography model, eliminating complex seabed reflection interference. Non-uniform medium correction: Horizontal anisotropic parameters (such as differences in transverse and longitudinal wave velocities) are introduced, and the Green's function weight factors are dynamically adjusted through Bayesian inference to adapt to the switching between deep-sea and shallow-sea environments. Deep learning acceleration: A Green's function approximation network (GF-Net) based on a convolutional neural network (CNN) is designed, reducing the integral computation complexity to O(n log n), supporting 3D flow field reconstruction updated every 10 minutes. Feature space dimensionality reduction: Wavelet packet decomposition is used to extract the spatiotemporal correlation features of the sound field, compressing the original data to improve computational efficiency while retaining key flow field information. Dynamic calibration mechanism: Real-time Argo buoy data (updated every 3 hours) is introduced, and the model parameters are dynamically corrected through the expectation-maximization (EM) algorithm to adapt to extreme weather conditions such as typhoons.

[0320] Adaptive calibration algorithm: Based on Kalman filtering, real-time fusion of multi-source data (sensor measurements, visual features, attitude parameters) dynamically corrects calibration coefficients.

[0321]

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

[0323] The Tidal Sensitivity Index (TMI) is introduced as a calibration trigger threshold. The calibration process is automatically started when TMI > 0.3.

[0324] ③ Verification and visualization of inversion results.

[0325] Cross-validation: Compare with the measurement results of the nearest ADCP (RDI 150kHz) to calculate the root mean square error of the flow velocity and the flow direction error.

[0326] Visualization Platform: Develop a web-based 3D flow field visualization system that supports dynamic playback of flow velocity fields, vortex recognition (Q value > 0.2), and abnormal flow velocity alarms.

[0327] 3D Flow Field Visualization Engine: Employs WebGL technology to build an immersive interface, supporting dynamic playback of flow velocity fields (frame rate ≥ 60fps), vortex recognition (Q value > 0.2), and abnormal alarm heatmaps. Multi-dimensional Data Display: Integrates a real-time dashboard of sensor status, calibration logs, and environmental parameters (air pressure / wind speed / temperature and salinity), supporting custom data subscriptions.

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

[0329] (1) Multimodal signal collaborative processing: Combining FK transform with deep learning to achieve joint analysis of gravity wave signals and ocean current velocity, thereby improving the 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, the dispersion relationship is optimized by seabed topography and multibeam data, and the shallow water error is reduced.

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

[0332] (4) Economical deployment plan: By upgrading existing submarine communication optical cables, the deployment cost per kilometer is reduced, supporting global marine network monitoring.

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

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

[0335] (7) Enhanced interpretability: The SHAP value analysis reveals the tidal-ocean current coupling mechanism (such as the phase lag relationship between the M2 wave and the extension of the Kuroshio Current), 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 preserving the microscale characteristics of ocean currents. Compared with traditional methods, it improves spatial resolution and accelerates dynamic response speed, 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 environment adaptability and ultra-short-term prediction by multivariate input fusion, dynamic attention mechanism and real-time enhancement technology, and provides a high-precision and low-latency tidal prediction solution for marine engineering and other fields.

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

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

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

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

[0342] (14) A three-dimensional velocity field inversion algorithm based on Green's function is proposed, which adopts a three-level architecture of "Green's function-fluid-structure interaction-deep learning" to solve the multi-physics 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 and low-cost three-dimensional velocity field inversion method for global ocean dynamics research.

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

[0344] Reference Figure 2 This application also provides an ocean current measurement system based on distributed acoustic sensing, which can realize the above-mentioned ocean current measurement method based on distributed acoustic sensing. The system includes:

[0345] The data acquisition unit is used to acquire data collected by multiple sensors installed on the submarine optical fiber as sensing data.

[0346] A data preprocessing unit is used to perform spatiotemporal alignment on the sensing data collected by each of the sensors in order to synchronize the sensing data.

[0347] The time-frequency conversion unit is used to convert the time-space aligned sensing data into a frequency domain signal as the original signal.

[0348] The tidal removal unit is used to remove the tidal signal from the original signal to obtain the ocean current signal;

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

[0350] It is understood that the content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0351] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method of this application. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0352] It is understood that the content of the above method embodiments is applicable to the device embodiments. The specific functions implemented by the device embodiments are the same as those of the methods of this application, and the beneficial effects achieved are the same as those achieved by the methods of this application.

[0353] Please see Figure 3 , Figure 3 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0354] The processor 301 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, 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 this application.

[0355] The memory 302 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 302 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 302 and is called and executed by the processor 301.

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

[0357] The communication interface 304 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

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

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

[0360] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method of this application.

[0361] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

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

[0363] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

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

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

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

[0367] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0368] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0369] In the embodiments provided in this 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 merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

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

[0371] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0372] If the integrated unit is implemented as 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 this application, in essence, 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. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0373] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for measuring ocean currents based on distributed acoustic sensing, characterized in that, The method includes the following steps: Data collected by multiple sensors installed on the submarine optical fiber is used as sensing data; The sensing data collected by each of the sensors are spatiotemporally aligned to synchronize the sensing data; The spatiotemporally aligned sensing data is converted into a frequency domain signal as the original signal. Remove the tidal signal from the original signal to obtain the ocean current signal; The Doppler frequency shift is calculated based on the ocean current signal, and then ocean current data is obtained by inversion based on the Doppler frequency shift. The process of obtaining ocean current data based on the Doppler frequency shift inversion includes the following steps: Based on the Green's function and flow field coupling algorithm, the distributed acoustic sensing-flow field relationship model is established as follows: ； Where G is the Green's function and f is the flow field source term; Combining the Green's function with the distributed acoustic sensing-flow field relationship model, the joint inversion equation of the acoustic wave propagation equation and the flow field dynamics is established as follows: ； Where 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 are obtained by inversion based on the joint inversion equation of the sound wave propagation equation and the flow field dynamics equation; Among them, a hybrid boundary condition Green's function is used, combined with multibeam sonar bathymetry data to construct a seabed topography model and eliminate complex seabed reflection interference. By introducing horizontal anisotropy parameters and dynamically adjusting the Green function weighting factor through Bayesian inference, we can adapt to the switching between deep-sea and shallow-sea environments.

2. The ocean current measurement method based on distributed acoustic sensing according to claim 1, characterized in that, The step of synchronizing the sensing data collected by each of the sensors in a spatiotemporal manner includes the following steps: The sensor data collected by each sensor is spatiotemporally aligned based on GPS data and tidal data to synchronize the sensor data; wherein the sensor 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 process of converting the spatiotemporally aligned sensing data into a frequency domain signal as the original signal includes the following steps: The time-space aligned sensing data is converted into a frequency domain signal using a fast Fourier transform, which serves as the original signal. The expression for the Fast Fourier Transform is: ； in, ω Angular frequency, k For wavenumber vectors, r These are the position coordinates; The expression for the angular frequency is: ； Where 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, which includes the following steps: Multibeam sonar data, satellite altimeter data, and vibration signals from the sensor data are used as multi-source data, and the multi-source data are synchronized. The synchronized multi-source data is then subjected to wavelet packet denoising and spatiotemporal alignment before being converted into a frequency domain signal. Feature filtering is then performed to obtain the target vibration signal. Calculate the phase difference between the target vibration signals corresponding to two sensors that are arbitrarily separated by a set distance; The phase difference is smoothed using a sliding window to suppress transient noise; The phase difference, the multibeam sonar data, and the satellite altimeter data are input into the LSTM network to obtain the tidal phase-amplitude curve output by the LSTM network. The tidal signal is reconstructed based on the tidal phase-amplitude curve.

5. The ocean current measurement method based on distributed acoustic sensing according to claim 4, characterized in that, The step of converting the synchronized multi-source data into a frequency domain signal includes the following steps: A local water depth gradient matrix is ​​constructed based on the multibeam sonar data; The wavenumber vector is corrected based on the local depth gradient matrix; The synchronized multi-source data is converted into a frequency domain signal based on the corrected wavenumber vector.

6. The ocean current measurement method based on distributed acoustic sensing according to claim 1, characterized in that, The calculation of the Doppler frequency shift based on the ocean current signal includes the following steps: The phase difference between adjacent sensors is calculated using a cross-correlation function and based on the ocean current signal, and the relative velocity of the ocean current between adjacent sensors is calculated by combining the sound velocity. The expression for the relative flow velocity is: ； in, f represents the relative flow velocity. d Here, f0 is the transmission frequency, and θ is the beam installation angle; Determine the velocity gradient based on the relative velocity; The Doppler frequency shift is then calculated by combining the flow velocity gradient with a dynamic Doppler frequency shift mapping model. The dynamic Doppler frequency shift mapping model is as follows: ； Where v is the velocity of the sound source, ▽u x The velocity gradient is... This refers to the reflectivity of the seabed sediment.

7. An ocean current measurement system based on distributed acoustic sensing, characterized in that, The system includes: The data acquisition unit is used to acquire data collected by multiple sensors installed on the submarine optical fiber as sensing data. A data preprocessing unit is used to perform spatiotemporal alignment on the sensing data collected by each of the sensors in order to synchronize the sensing data. The time-frequency conversion unit is used to convert the time-space aligned sensing data into a frequency domain signal as the original signal. The tidal removal unit is used to remove the tidal signal from the original signal to obtain the ocean current signal; The ocean current inversion unit is used to calculate the Doppler frequency shift based on the ocean current signal, and then obtain ocean current data based on the Doppler frequency shift. The process of obtaining ocean current data based on the Doppler frequency shift inversion includes the following steps: Based on the Green's function and flow field coupling algorithm, the distributed acoustic sensing-flow field relationship model is established as follows: ； Where G is the Green's function and f is the flow field source term; Combining the Green's function with the distributed acoustic sensing-flow field relationship model, the joint inversion equation of the acoustic wave propagation equation and the flow field dynamics is established as follows: ； Where 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 are obtained by inversion based on the joint inversion equation of the sound wave propagation equation and the flow field dynamics equation; Among them, a hybrid boundary condition Green's function is used, combined with multibeam sonar bathymetry data to construct a seabed topography model and eliminate complex seabed reflection interference. By introducing horizontal anisotropy parameters and dynamically adjusting the Green function weighting factor through Bayesian inference, we can adapt to the switching between deep-sea and shallow-sea environments.

8. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 6.

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