A high-power marine acoustic tomography system for marine environmental monitoring

Abstract

This invention relates to the field of marine environmental monitoring technology. Specifically, it relates to a high-power marine acoustic tomography system for marine environmental monitoring. The system includes a regional deployment and clock synchronization module, a signal generation and high-power transmission module, a signal reception and propagation time measurement module, a sound velocity calculation and reference area selection module, a sound velocity parameter weighted correction module, and an environmental parameter inversion and result fusion module. By constructing a standardized end-to-end technical solution from regional deployment, signal transmission and reception, parameter calculation to environmental inversion, it achieves large-area, high-precision, and continuous environmental monitoring of target marine areas. Through standardized gridded regional division, GPS carrier phase positioning node calibration, and high-precision clock synchronization using GPS-acquired atomic clocks, a standardized underwater mooring node network is established, ensuring the basic accuracy of underwater acoustic signal propagation time measurement and sound velocity parameter calculation from a hardware deployment perspective.

Description

Technical Field

[0001] This invention relates to the field of marine environmental monitoring technology, and more specifically, to a high-power marine acoustic tomography system for marine environmental monitoring. Background Technology

[0002] Acoustic tomography, as one of the core technologies for large-scale, non-contact monitoring of the marine environment, can invert key environmental parameters such as temperature, salinity, and current field of the sea area by the propagation characteristics of underwater acoustic signals in the marine medium. It can realize dynamic and continuous monitoring of the marine environment and provide accurate hydrological data support for marine scientific research, marine resource development, marine ecological protection, and marine disaster prevention and mitigation.

[0003] Existing marine acoustic tomography technology has many shortcomings in practical applications, making it difficult to meet the needs of large-area, high-precision marine environmental monitoring. Firstly, the existing acoustic tomography mooring network layout lacks standardized regional division and node calibration methods, and the clock synchronization accuracy of the mooring nodes is low, which easily leads to asynchronous underwater acoustic signal transmission and reception, directly affecting the measurement accuracy of propagation time, and thus causing large errors in the calculation of sound velocity parameters. Secondly, existing systems have low underwater acoustic signal transmission power and lack dedicated spread spectrum coding and anti-interference modulation methods. Crosstalk is easily generated in multiple regions, and the signal attenuates quickly during propagation in the open ocean, making it difficult to achieve effective signal coverage and reception over a wide area in the mid-to-far ocean. To reduce this situation, a high-power marine acoustic tomography system for marine environmental monitoring is proposed. Summary of the Invention

[0004] The purpose of this invention is to provide a high-power marine acoustic tomography system for marine environmental monitoring, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, a high-power marine acoustic tomography system for marine environmental monitoring is provided, including a regional deployment and clock synchronization module, a signal generation and high-power transmission module, a signal reception and propagation time measurement module, a sound velocity calculation and reference area selection module, a sound velocity parameter weighting correction module, and an environmental parameter inversion and result fusion module. The regional deployment and clock synchronization module is used to determine the location of marine targets for marine environmental monitoring, divide the marine target locations into monitoring areas, and simultaneously deploy multiple sets of acoustic tomography moorings in each area to complete the positioning of mooring nodes, spacing calibration, and atomic clock GPS training. The signal generation and high-power transmission module is used to configure a dedicated 12th-order M sequence for each area's underwater buoy. After 2PSK modulation, a code division multiple access spread spectrum modulation signal is generated. Then, after DAC digital-to-analog conversion, 400W Class 4 power amplifier amplification, and 1:17 turns ratio transformer impedance matching, a 192dB sound source level high-power electrical signal is obtained. The underwater buoys in each area are controlled by a synchronous clock to synchronously convert it into an underwater acoustic signal for transmission. After transmission, the underwater buoys switch to receiving mode. The signal reception and propagation time measurement module is used to deploy the underwater glider transducer to receive underwater acoustic signals and convert them into weak analog electrical signals. At the same time, it completes 2PSK demodulation and M-sequence despreading through quadrature coherent demodulation to obtain a high signal-to-noise ratio digital baseband signal. The 12th order M-sequence of the transmitting end is used as a reference to perform time-domain cross-correlation operation with the baseband signal, extracts the autocorrelation function value and identifies the waveform peak time point to determine the bidirectional propagation time of underwater acoustic signals between underwater glider nodes. The sound velocity calculation and reference area selection module is used to calculate the sound velocity parameters of each region based on the calibration distance of the buoy node and the two-way propagation time, taking the region as a unit. At the same time, it constructs a feature vector for each detection region, calculates the similarity and difference values ​​between regions and between adjacent regions, quantifies the correlation and deviation of data, and selects a reference area. The sound velocity parameter weighting correction module is used to simultaneously calculate and determine the confidence level of each detection area and the confidence level of its neighbors. Based on the double confidence level determination results, the sound velocity parameter difference of each area is weighted and corrected to obtain the corrected sound velocity parameter. The environmental parameter inversion and result fusion module is used to substitute the corrected sound speed parameters into the ocean dynamics inversion algorithm for inverse operation. Combined with the seawater sound speed and temperature-salinity correlation model and the ocean current and sound speed difference correlation model, the three-dimensional distribution data of temperature-salinity field and current field in each region are inverted respectively. Then, the inversion results of all detection areas are fused to obtain the multi-parameter distribution data of ocean environment temperature, salinity, current velocity and current direction of the target sea area.

[0006] As a further improvement to this technical solution, the regional deployment and clock synchronization module is connected to the marine environment management terminal, which obtains marine environment monitoring requirements and determines the marine area range for which environmental parameter detection is to be carried out, i.e., the location of the marine target, based on the marine environment monitoring requirements. Based on the topographic features, sea area size, and environmental detection accuracy requirements of the marine target location, a grid-based approach is adopted to divide the marine target location into multiple detection areas. These detection areas are spatially connected and their boundaries do not overlap.

[0007] As a further improvement to this technical solution, in the regional deployment and clock synchronization module, at least two sets of acoustic tomography moorings are deployed in each detection area according to the detection range to form a mooring node network. At the same time, the precise geographical coordinates of each mooring node are obtained through GPS positioning technology, and the straight-line distance between any two mooring nodes is calculated and calibrated based on the geographical coordinates. The atomic clocks at each underwater buoy node are calibrated using the standard time signal from GPS to ensure that the time reference of each atomic clock is consistent. By calibrating with atomic clocks, the clocks of all underwater glider nodes are made error-free, achieving consistency in signal transmission and reception time for each underwater glider node.

[0008] As a further improvement to this technical solution, in the signal generation and high-power transmission module, a unique 12th-order M-sequence is assigned to all acoustic tomography moorings in each detection area. Moorings in different detection areas use different 12th-order M-sequences to distinguish the mooring signals in each area. A 12th-order M-sequence is loaded onto a carrier signal using 2PSK modulation, and the phase of the carrier signal is changed to represent the binary information of the M-sequence. The spread spectrum modulation signal of code division multiple access is obtained by 2PSK modulation and M-sequence spread spectrum, so that the buoy transmission signal of each detection area has a unique code pattern feature; The spread spectrum modulation signal is converted from digital to analog by a DAC to output an analog electrical signal. Then, the analog electrical signal is boosted by a 400W Class D audio power amplifier. Finally, a transformer with a turns ratio of 1:17 is used to achieve impedance matching between the power amplifier and the transducer. Through power amplification and impedance matching, the output electrical signal can drive the transducer to generate a 192dB acoustic signal at the source level. Based on a synchronized clock signal across the entire sea area, all underwater gliders are controlled to convert high-power electrical signals into underwater acoustic signals through transducers at the same time. Simultaneously, underwater gliders in each detection area transmit underwater acoustic signals to the sea area within their respective detection areas. After completing the transmission of underwater acoustic signals, the underwater gliders quickly switch their working modes via a transmit / receive switch, entering the underwater acoustic signal receiving state and waiting to receive underwater acoustic signals transmitted by other nodes.

[0009] As a further improvement to this technical solution, the signal receiving and propagation time measurement module converts the underwater acoustic signal propagating in the ocean into a weak analog electrical signal based on the transceiver integrated on the underwater glider. By filtering out environmental noise and clutter in weak electrical signals using a bandpass filter, and then amplifying the filtered signal in stages using a fixed gain amplifier and a programmable gain amplifier to increase the signal amplitude; The carrier signal in the 2PSK modulated signal is removed by using orthogonal coherent demodulation to restore the M-sequence signal, thereby realizing the despreading and demodulation of the signal. Through demodulation and despreading processing, noise and interference in the signal are eliminated to obtain a high signal-to-noise digital baseband signal. The original 12th-order M-sequence signal from the transmitting end is used as a reference signal, and cross-correlation calculation is performed between it and the received and processed digital baseband signal in the time domain. Since the environmental noise is a random signal, it has extremely low correlation with the M-sequence signal from the transmitting end. The cross-correlation operation can effectively suppress the noise and extract the autocorrelation function value corresponding to the transmitted signal. The signal waveform after cross-correlation operation will show a significant peak at the arrival time of the underwater acoustic signal. By identifying the time point corresponding to this peak, the propagation time of the underwater acoustic signal from the transmitting node to the receiving node can be obtained, and the reverse propagation time, i.e., the bidirectional propagation time, can also be obtained.

[0010] As a further improvement to this technical solution, in the sound velocity calculation and reference area selection module, each detection area is used as an independent calculation unit. The straight-line distance between the calibrated nodes in the area and the bidirectional underwater acoustic signal propagation time between the nodes measured in the signal reception and propagation time measurement module are used as the calculation basis to calculate the corresponding sound velocity parameters for each detection area separately. Among them, the sound speed parameters include the bidirectional propagation speed between nodes, the regional average propagation speed, and the difference in reciprocating propagation speed between regional nodes; The correlation calculation method and the deviation calculation method are used to calculate the similarity and difference values ​​of feature vectors between any two detection regions and between adjacent detection regions, respectively. Then, the numerical results are used to characterize the correlation and deviation values ​​between the sound velocity parameters of each detection region. A larger numerical result likely indicates a stronger correlation and a smaller bias. Based on the similarity and difference values ​​obtained from quantification, the detection area with the smallest deviation between its own measured sound velocity parameter and the average value of multiple measurements, and the highest average similarity value with all surrounding areas, is selected as the benchmark reference area for weighted correction of the sound velocity parameter.

[0011] As a further improvement to this technical solution, the sound velocity parameter weighted correction module calculates the self-confidence of each detection area and the adjacent confidence of the area and the adjacent reference area. The self-confidence is an index that characterizes the validity of the measurement data of the detection area based on the stability of the measurement data, the signal reception quality and the absence of interference. Set a self-confidence threshold and an adjacent confidence threshold, and combine the self-confidence and the adjacent confidence of the region with the adjacent benchmark reference regions to perform a double reset confidence determination; When the self-confidence of the detection area is greater than its own confidence threshold, and the adjacent confidence of the area and the adjacent benchmark detection area is greater than the adjacent confidence threshold, a weighted correction method is adopted with the original sound velocity data as the main factor and the sound velocity data of the adjacent benchmark area as the auxiliary factor to calculate the corrected average propagation velocity of the area and the corrected inter-node reciprocating propagation velocity difference. When the self-confidence of the detection area is less than its own confidence threshold, but the adjacent confidence of the area and the adjacent benchmark detection area is greater than the adjacent confidence threshold, a weighted correction method is adopted with the sound velocity data of the adjacent benchmark area as the main data and the original sound velocity data of the area as the reference. The corrected average propagation velocity and the corrected inter-node reciprocating propagation velocity difference of the area are calculated. When the adjacent confidence level between the detection area and the adjacent benchmark detection area is less than the adjacent confidence level threshold, no weighted correction is performed regardless of whether the confidence level of the area itself meets the standard. The original average propagation speed and the original inter-node back-and-forth propagation speed difference of the area are directly retained.

[0012] As a further improvement to this technical solution, in the environmental parameter inversion and result fusion module, the average propagation speed and the difference between the reciprocating propagation speeds after weight correction by the sound speed parameter weight correction module are used as input data and substituted into the established ocean dynamics inversion algorithm for inverse operation processing. The algorithm restores the original ocean environment parameters corresponding to the sound speed parameters. Using a correlation model that reflects the quantitative relationship between seawater sound velocity and temperature and salinity, the three-dimensional spatial distribution data of temperature and salinity fields in each detection area are inverted based on the corrected average propagation velocity. At the same time, using a correlation model that reflects the quantitative relationship between ocean current parameters and the difference in underwater acoustic reciprocating propagation velocity, the vector three-dimensional distribution data of the flow field in each detection area are inverted based on the corrected reciprocating propagation velocity difference. Spatial data fusion technology is used to stitch, integrate and optimize the three-dimensional distribution data of temperature, salinity and flow fields in each independent detection area, eliminate the stitching deviation between areas, and obtain multi-parameter distribution data of marine environment covering the entire target sea area through data fusion; Among them, the multi-parameter distribution data of the marine environment includes temperature, salinity, current velocity, and current direction.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This high-power marine acoustic tomography system for marine environmental monitoring constructs a standardized technical solution covering the entire chain from regional deployment, signal transmission and reception, parameter calculation to environmental inversion. This enables large-area, high-precision, and continuous environmental monitoring of target marine areas. The system establishes a standardized underwater buoy node network through standardized gridded regional division, GPS carrier phase positioning node calibration, and high-precision clock synchronization using GPS-acquired atomic clocks. This hardware deployment ensures the basic accuracy of underwater acoustic signal propagation time measurement and sound velocity parameter calculation, solving the problem of large measurement errors caused by non-standard networking and clock asynchrony in existing technologies. This lays a precise foundation of data support for subsequent full-chain data processing and environmental inversion.

[0014] 2. This high-power marine acoustic tomography system for marine environmental monitoring constructs a two-dimensional feature vector with the difference between the average propagation speed and the reciprocating propagation speed as its core. It uses Pearson correlation coefficient and Euclidean distance to quantify the correlation and deviation of data between regions. A benchmark reference region with high stability and high correlation is selected. A multi-dimensional self-confidence is constructed by combining the stability of measurement data, signal reception quality and non-interference. It forms a dual judgment standard with the adjacent confidence of the benchmark region. Differentiated weighted correction strategies are adopted for different judgment results, which effectively eliminates random errors and outliers in the measurement data, improves the accuracy of sound velocity parameters, and solves the problems of no correction and error amplification in the calculation of sound velocity parameters in the existing technology. It provides high-precision core input data for marine environmental parameter inversion. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the process of a high-power marine acoustic tomography system for marine environmental monitoring according to the present invention; Figure 2 This is a schematic diagram illustrating the process of deploying the clock synchronization module in the region according to the present invention; Figure 3 This is a flowchart illustrating the signal generation and high-power transmission module of the present invention. Figure 4 This is a flowchart illustrating the signal reception and propagation time measurement module of the present invention. Figure 5 This is a flowchart illustrating the sound velocity calculation and reference region selection module of the present invention. Figure 6 This is a flowchart illustrating the sound velocity parameter weighting correction module of the present invention. Figure 7 This is a flowchart illustrating the environmental parameter inversion and result fusion module of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Please see Figures 1-7 As shown, the purpose of this embodiment is to provide a high-power marine acoustic tomography system for marine environmental monitoring, including a regional deployment and clock synchronization module, a signal generation and high-power transmission module, a signal reception and propagation time measurement module, a sound velocity calculation and reference area selection module, a sound velocity parameter weighting correction module, and an environmental parameter inversion and result fusion module. The regional deployment and clock synchronization module is used to determine the location of marine targets for marine environmental monitoring, divide the marine target locations into monitoring areas, and simultaneously deploy multiple sets of acoustic tomography moorings in each area to complete the positioning of mooring nodes, spacing calibration, and atomic clock GPS training. In the regional deployment and clock synchronization module, it connects to the marine environment management terminal, obtains marine environment monitoring requirements from the marine environment management terminal, and determines the marine area range to be monitored for environmental parameters, i.e., the location of marine targets, based on the marine environment monitoring requirements. A stable data connection is established between the module and the marine environment management terminal through a wireless communication protocol. Communication link testing is completed to ensure that demand commands and geographic data can be exchanged bidirectionally without transmission loss. Then, structured marine environment monitoring requirements are extracted from the marine environment management terminal, which must include mandatory indicators, the approximate geographic range of the monitoring sea area, the detection accuracy of environmental parameters (temperature / salinity / current field), and the requirements for monitoring coverage integrity. The extracted monitoring requirements are broken down and their rationality is verified. Invalid and contradictory requirements are eliminated. The geographical boundary thresholds (longitude / latitude range) and the quantitative standards for detection accuracy (such as temperature field error ≤0.5℃, current velocity error ≤0.1m / s) of the sea area to be monitored are defined. Then, based on the verified requirements and combined with the marine geographic database, the marine area to be monitored for environmental parameters is delineated. The four boundaries of the marine target location are defined by latitude and longitude coordinates, forming a standardized geographic file of the target location. Based on the topographic features, sea area size, and environmental detection accuracy requirements of the marine target location, a grid-based approach is adopted to divide the marine target location into multiple detection areas. These detection areas are spatially connected and their boundaries do not overlap.

[0018] Retrieve full-dimensional geographic feature data of the marine target location, including seabed topography data (distribution of shoals / islands / trenches), total sea area, overall sea shape, and key hydrological boundaries (ocean current boundaries, thermocline, cold water mass distribution areas). Then, in combination with environmental monitoring accuracy requirements, calculate the gridding parameters (grid cell side length and area numbering rules, coded sequentially from west to east and from north to south according to latitude and longitude). The method of dividing the area into regular quadrilateral grids is adopted. Based on the latitude and longitude of the location of the marine target, the grid is divided into grids one by one according to the calculated side length of the grid cells, forming multiple independent detection areas. In the regional deployment and clock synchronization module, at least two sets of acoustic tomography moorings are deployed in each detection area according to the detection range to form a mooring node network. At the same time, the precise geographical coordinates of each mooring node are obtained through GPS positioning technology, and the straight-line distance between any two mooring nodes is calculated and calibrated based on the geographical coordinates. At least two sets of acoustic tomography moorings are deployed in each area. The moorings are deployed in a way that avoids obstacles such as shallow waters and reefs to ensure that the signal propagation path is not significantly obstructed. The spacing between the moorings is set in conjunction with the system observation distance (1~60km), with equidistant deployment being preferred to form a dual-node / multi-node detection network. Each mooring node is assigned a unique equipment number that is associated with the detection area number to which it belongs. The GPS carrier phase positioning method is used to collect the geographic coordinates of each mooring node. By activating the GPS carrier phase positioning module integrated in the mooring, at least 4 GPS satellite signals are received to complete the positioning calculation. Each mooring node is positioned multiple times (≥5 times), and the average value is taken as the accurate geographic coordinates of the node, which are recorded as the three-dimensional coordinates of the mooring node. The coordinate data of all mooring nodes are associated with the equipment number and the area number to which they belong to form a mooring node coordinate database. Based on the three-dimensional coordinates of the mooring nodes, the straight-line distance between any two mooring nodes is calculated and calibrated using spatial straight-line distance as a fixed reference value for subsequent sound speed calculation. After the calculation is completed, the spacing data is associated with the corresponding node pairs and stored in the coordinate database. The atomic clocks at each underwater buoy node are calibrated using the standard time signal from GPS to ensure that the time reference of each atomic clock is consistent. Using GPSUTC standard time as a reference, the current time of the atomic clocks at each mooring node is read, the initial time deviation of the atomic clocks is calculated, and the atomic clocks are calibrated based on the initial deviation to correct the deviation value, so that the time reference of the atomic clocks at each mooring node is initially consistent, and the initial calibration error is controlled within ≤0.1μs; By calibrating with atomic clocks, the clocks of all underwater glider nodes are made error-free, achieving consistency in signal transmission and reception time for each underwater glider node.

[0019] The signal generation and high-power transmission module is used to configure a dedicated 12th-order M sequence for each area of ​​underwater buoys. After 2PSK modulation, a code division multiple access spread spectrum modulation signal is generated. Then, after DAC digital-to-analog conversion, 400W Class 4 power amplifier amplification and 1:17 turns ratio transformer impedance matching, a 192dB sound source level high-power electrical signal is obtained. The underwater buoys in each area are controlled by a synchronous clock to synchronously convert it into underwater acoustic signal for transmission. After transmission, the underwater buoys switch to receiving mode. In the signal generation and high-power transmission module, a unique 12th-order M-sequence is assigned to all acoustic tomography moorings in each detection area. Moorings in different detection areas use different 12th-order M-sequences to distinguish the mooring signals in each area. A library of 12th-order M sequences that meets the system requirements is pre-generated (M sequences are the longest linear shift register sequences, which have good autocorrelation and cross-correlation). The sequence library contains at least one independent 12th-order M sequence that matches the number of detection regions, and the cross-correlation value of any two sequences approaches 0. According to the detection area number (e.g., area 1, area 2... area N), a unique 12th-order M sequence is assigned to each detection area, and a mapping table between area number and M sequence is established to ensure that the buoys in different detection areas use completely different 12th-order M sequences; A 12th-order M-sequence is loaded onto a carrier signal using 2PSK modulation, and the phase of the carrier signal is changed to represent the binary information of the M-sequence. The system generates a preset carrier signal (optimal carrier frequency 5~20kHz, adapted to the underwater acoustic propagation characteristics), and simultaneously converts the 12th-order M-sequence into a binary digital signal (the M-sequence takes values ​​of 0 / 1, mapped to a bipolar binary sequence of -1 / +1). Then, using 2PSK binary phase keying modulation, the M-sequence binary signal is loaded onto the carrier signal. By changing the carrier phase, the binary information of the M-sequence is represented. The 2PSK modulated signal (spread spectrum modulated signal) is calculated as follows: ; in, The instantaneous value of the carrier signal. The amplitude of the carrier signal. The center frequency of the carrier. For time, The initial phase of the carrier. This represents the phase transition value; ; in, The instantaneous value of the spread spectrum signal after 2PSK modulation. It is a bipolar binary sequence mapped from the M sequence; The spread spectrum modulation signal of code division multiple access is obtained by 2PSK modulation and M-sequence spread spectrum, so that the buoy transmission signal of each detection area has a unique code pattern feature; The spread spectrum modulation signal is converted from digital to analog by a DAC to output an analog electrical signal. Then, the analog electrical signal is boosted by a 400W Class D audio power amplifier. Finally, a transformer with a turns ratio of 1:17 is used to achieve impedance matching between the power amplifier and the transducer. The spread spectrum modulation signal in digital form is input into a high-precision DAC digital-to-analog converter module (sampling rate ≥ 4 times the carrier frequency, such as sampling rate ≥ 40kHz when the carrier is 10kHz), which converts the discrete digital signal into a continuous analog electrical signal. The distortion of the analog signal after conversion is ≤ 0.1%. At the same time, the analog electrical signal is input into a 400WD Class 4 audio power amplifier (optimal power amplifier type, efficiency ≥ 90%, suitable for the low power consumption requirements of underwater equipment) to boost the power of the analog electrical signal. A transformer with a turns ratio of 1:17 is used to achieve impedance matching between the power amplifier output (typical impedance 8Ω) and the transducer (typical impedance 136Ω). Through power amplification and impedance matching, the output electrical signal can drive the transducer to generate a 192dB acoustic signal at the source level. Based on a synchronized clock signal across the entire sea area, all underwater gliders are controlled to convert high-power electrical signals into underwater acoustic signals through transducers at the same time. Simultaneously, underwater gliders in each detection area transmit underwater acoustic signals to the sea area within their respective detection areas. After completing the transmission of underwater acoustic signals, the underwater gliders quickly switch their working modes via a transmit / receive switch and enter the underwater acoustic signal receiving state, waiting to receive underwater acoustic signals transmitted by other nodes. All underwater mooring nodes are connected to a GPS clock signal synchronized across the entire sea area (synchronization error ≤ 0.5μs). The signal transmission module receives the synchronization transmission command issued by the central control unit. When the timestamp of the synchronization transmission command arrives, each underwater mooring node synchronously inputs a high-power electrical signal into the transducer. The transducer completes the conversion of electrical and acoustic energy, converting the electrical signal into a water acoustic mechanical wave signal. Control the transducer's transmission direction and power to direct the underwater acoustic signal to the sea area of ​​the detection zone to which the buoy belongs. After the buoy completes the transmission of the underwater acoustic signal (the transmission duration matches the M-sequence period, taking 4095 carrier periods), the transducer switches from transmission mode to reception mode.

[0020] The signal reception and propagation time measurement module is used to deploy the underwater glider transducer to receive underwater acoustic signals and convert them into weak analog electrical signals. At the same time, it completes 2PSK demodulation and M-sequence despreading through quadrature coherent demodulation to obtain a high signal-to-noise ratio digital baseband signal. The 12th order M-sequence of the transmitter is used as a reference to perform time-domain cross-correlation operation with the baseband signal, extract the autocorrelation function value and identify the peak time point of the waveform to determine the bidirectional propagation time of the underwater acoustic signal between underwater glider nodes. In the signal reception and propagation time measurement module, the underwater acoustic signal propagating in the ocean is converted into a weak analog electrical signal based on the transceiver integrated on the underwater buoy. After the underwater buoy completes the switch from transmit mode to receive mode, it activates the integrated transceiver transducer (piezoelectric underwater acoustic transducer, receiving sensitivity ≥-180dBre1V / μPa, adapted to weak underwater acoustic signals in the ocean), sets the transducer to receive mode, and is in a low-noise ready state. The transducer receives underwater acoustic mechanical wave signals propagating in the ocean and converts the underwater acoustic mechanical energy into weak analog electrical signals through the piezoelectric effect.

[0021] By filtering out environmental noise and clutter in weak electrical signals using a bandpass filter, and then amplifying the filtered signal in stages using a fixed gain amplifier and a programmable gain amplifier to increase the signal amplitude; The weak analog electrical signal is input into a bandpass filter (an active RC bandpass filter with a center frequency that matches the transmitted carrier frequency) to filter out environmental noise and clutter, retaining only the effective signal that matches the carrier frequency. The filtered signal is then input into a fixed-gain amplifier (gain value 20dB, i.e., amplification factor of 10) to complete the initial amplification of the signal. The signal is then input into a programmable gain amplifier (a digitally controlled variable gain amplifier with a gain range of 0~40dB). The gain is automatically adjusted according to the real-time amplitude of the signal (e.g., if the signal amplitude is still low, it is adjusted to 40dB, i.e., the amplification factor is 100 times), and finally a signal with a stable amplitude is output.

[0022] The carrier signal in the 2PSK modulated signal is removed by using orthogonal coherent demodulation to restore the M-sequence signal, thereby realizing the despreading and demodulation of the signal. Through demodulation and despreading processing, noise and interference in the signal are eliminated to obtain a high signal-to-noise digital baseband signal. The original 12th-order M-sequence signal from the transmitting end is used as a reference signal, and cross-correlation is performed between it and the received and processed digital baseband signal in the time domain. Since environmental noise is a random signal with extremely low correlation to the transmitted M-sequence signal, the cross-correlation operation effectively suppresses noise, extracting the autocorrelation function value corresponding to the transmitted signal, as shown in the following formula: ; in, The cross-correlation function value, For time delay, The reference M-sequence signal for the transmitting end (the nth sampling point) is used. For the receiving end digital baseband signal (n+th) (sampling points) The signal waveform after cross-correlation operation will show a significant peak at the arrival time of the underwater acoustic signal. By identifying the time point corresponding to this peak, the propagation time of the underwater acoustic signal from the transmitting node to the receiving node can be obtained, and the reverse propagation time, i.e., the bidirectional propagation time, can also be obtained.

[0023] The cross-correlation function values ​​are plotted as waveforms with time delay, with the horizontal axis representing time delay and the vertical axis representing the cross-correlation function values. The threshold peak detection method (with the threshold set to 80% of the peak value) is used to identify obvious peak points in the waveform and extract the timestamp corresponding to the peak value, which is the underwater acoustic signal propagation time from the transmitting node to the receiving node. Then, the receiving node is switched to the transmitting node, and the original transmitting node is switched to the receiving node. The above steps are repeated to obtain two reverse propagation times, which together constitute the bidirectional propagation time.

[0024] The sound velocity calculation and reference area selection module is used to calculate the sound velocity parameters of each region based on the calibration distance of the buoy node and the two-way propagation time. At the same time, it constructs feature vectors for each detection region, calculates the similarity and difference values ​​between regions and between adjacent regions, quantifies the correlation and deviation of data, and selects a reference area. In the sound velocity calculation and reference area selection module, each detection area is used as an independent calculation unit. The straight-line distance between the calibrated nodes in the area and the bidirectional underwater acoustic signal propagation time between the nodes measured in the signal reception and propagation time measurement module are used as the calculation basis to calculate the corresponding sound velocity parameters for each detection area separately. Among them, the sound speed parameters include the bidirectional propagation speed between nodes, the regional average propagation speed, and the difference in reciprocating propagation speed between regional nodes; The bidirectional propagation speed between nodes is calculated, specifically the propagation speed of underwater acoustic signals from the first mooring node to the second mooring node and from the second mooring node to the first mooring node. The regional average propagation speed is obtained by taking the arithmetic mean of the bidirectional propagation speeds between all underwater mooring nodes within the detection area, thus eliminating the additional influence of ocean currents on the unidirectional propagation speed. The difference in reciprocating propagation velocity between regional nodes is calculated by measuring the difference in bidirectional propagation velocity between underwater mooring nodes within the detection area. This difference is caused by the velocity and direction of the ocean current. Each detection region is treated as an independent feature unit, and the average propagation speed of the region and the difference in reciprocating propagation speed between nodes are used as feature components to construct a two-dimensional feature vector. Using a single detection region as an independent feature unit, two types of feature components (average propagation speed and the difference in reciprocating propagation speed between nodes) are extracted. The two types of components are combined according to a fixed dimension to construct a two-dimensional feature vector. The vector dimension corresponds to the component, ensuring that the vector format of each region is uniform. Then, the min-max normalization method is used to map the feature component values ​​to the [0,1] interval to eliminate the difference in the units of average speed and speed difference. The correlation calculation method and the deviation calculation method are used to calculate the similarity and difference values ​​of feature vectors between any two detection regions and between adjacent detection regions, respectively. Then, the numerical results are used to characterize the correlation and deviation values ​​between the sound velocity parameters of each detection region. A larger numerical result likely indicates a stronger correlation and a smaller bias. Retrieve standardized feature vectors from any two detection regions from the feature vector library, ensuring that the vector dimensions and format are consistent. Use the Pearson correlation coefficient method to calculate the correlation coefficient between the feature vectors of the two regions, quantifying the degree of correlation of the sound speed parameters between regions. It is necessary to calculate the similarity between any two regions and between adjacent regions. The Euclidean distance method is used to calculate the spatial distance between the feature vectors of two regions and quantify the degree of deviation of the sound speed parameters between regions. The difference values ​​between any two regions and adjacent regions are calculated simultaneously. Based on the similarity and difference values ​​obtained from quantification, the detection area with the smallest deviation between its own measured sound velocity parameter and the average value of multiple measurements, and the highest average similarity value with all surrounding areas, is selected as the benchmark reference area for weighted correction of the sound velocity parameter.

[0025] For each detection area, at least 5 sound velocity parameter measurements are performed. The deviation between a single measurement value and the average of multiple measurements is calculated. High-stability areas with a deviation value ≤ deviation threshold (e.g., 0.5 m / s) are selected. Then, for high-stability candidate areas, the average similarity between them and all surrounding areas is calculated. High-correlation areas with an average similarity ≥ similarity threshold (e.g., 0.8) are selected. At the same time, the original data of the signal transmission and reception links in the candidate area can be retrieved to check the signal amplitude, signal-to-noise ratio and other indicators, confirm that there is no signal obstruction or interference from ships / marine life, and retain the candidate area without interference; Finally, from the final candidate regions, the detection region with the smallest self-deviation value (optimal stability) and the highest average similarity to its surrounding areas (strongest correlation) is selected as the benchmark reference region for the weighted correction of the sound velocity parameter, as shown in the following formula: ; in, The deviation value of the sound velocity parameter in region k. To measure the number of times, Let be the average propagation velocity of region k measured in the j-th time. The mean propagation velocity is the average of multiple measurements taken in region k. ; in, Let k be the average similarity between region k and its surrounding p regions. Let k be the similarity between region k and its surrounding q-th region.

[0026] The sound velocity parameter weighting correction module is used to simultaneously calculate and determine the confidence level of each detection area and the confidence level of its neighbors. Based on the double confidence level determination results, the sound velocity parameter difference of each area is weighted and corrected to obtain the corrected sound velocity parameter. In the sound velocity parameter weighted correction module, the self-confidence of each detection area and the adjacency confidence of the area with the adjacent reference area are calculated. The self-confidence is an index that characterizes the validity of the measurement data of the detection area based on the stability of the measurement data, the signal reception quality and the absence of interference. The self-confidence level is broken down into three evaluation dimensions: measurement data stability (weight 0.5), signal reception quality (weight 0.3), and interference-free operation (weight 0.2). The stability of the measurement data is quantified as 0-1 points, with higher scores given for smaller deviations. Signal reception quality is based on the signal-to-noise ratio (SNR) of the signal reception stage. 1 point is awarded for SNR≥10dB and 0 points are awarded for SNR<5dB. The quantization is from 0 to 1 point. No interference: Based on interference records in the signal transmission and reception stages, 1 point is awarded for no interference, and 0 points are awarded for obstruction / ship interference. The scores of the three dimensions are weighted and summed according to the preset weights to obtain the self-confidence value in the range of 0 to 1; The similarity (Pearson correlation coefficient) of the feature vectors of this region and the adjacent reference region is retrieved, and the similarity value is directly used as the adjacent confidence level (value 0~1). No additional weighting is required, ensuring that the correlation with the reference region directly represents the adjacent confidence level.

[0027] Set a self-confidence threshold and an adjacent confidence threshold (self-confidence ≥ 0.7, adjacent confidence ≥ 0.8), and combine the self-confidence and the adjacent confidence of the region with the adjacent benchmark reference region to perform a double reset confidence determination; When the self-confidence of the detection area is greater than its own confidence threshold, and the adjacency confidence of this area and the adjacent benchmark detection area is greater than the adjacency confidence threshold, a weighted correction method is adopted, with the original sound velocity data as the main factor and the sound velocity data of the adjacent benchmark area as the auxiliary factor. The corrected average propagation velocity and the corrected inter-node reciprocating propagation velocity difference of this area are calculated, as follows: ; ; in, This is the corrected regional average propagation speed. To correct the difference in average reciprocating speed in the region, The original average propagation speed of the region itself. Due to the difference in the original reciprocating speed of the region itself, The average propagation velocity of adjacent reference regions. This represents the average reciprocating speed difference between adjacent reference regions. When the self-confidence of the detection area is less than its own confidence threshold, but the adjacency confidence of the area and the adjacent reference detection area is greater than the adjacency confidence threshold, a weighted correction method is adopted, using the sound velocity data of the adjacent reference area as the main factor and the original sound velocity data of the area itself as a reference. The corrected average propagation velocity and the corrected inter-node reciprocating propagation velocity difference of the area are calculated, as follows: ; ; When the adjacent confidence level between the detection area and the adjacent benchmark detection area is less than the adjacent confidence level threshold, no weighted correction is performed regardless of whether the confidence level of the area itself meets the standard. The original average propagation speed and the original inter-node back-and-forth propagation speed difference of the area are directly retained.

[0028] The environmental parameter inversion and result fusion module is used to substitute the corrected sound speed parameters into the ocean dynamics inversion algorithm for inverse operation. Combined with the seawater sound speed and temperature-salinity correlation model and the ocean current and sound speed difference correlation model, the three-dimensional distribution data of temperature-salinity field and current field in each region are inverted respectively. Then, the inversion results of all detection areas are fused to obtain the multi-parameter distribution data of ocean environment temperature, salinity, current velocity and current direction of the target sea area.

[0029] In the environmental parameter inversion and result fusion module, the average propagation speed and the difference between the reciprocating propagation speeds after weight correction by the sound speed parameter weight correction module are used as input data and substituted into the established ocean dynamics inversion algorithm for inverse operation. The algorithm restores the original ocean environment parameters corresponding to the sound speed parameters. Call the pre-established ray tracing ocean dynamics inversion algorithm (the best method, adapted to environmental inversion using acoustic tomography, with high computational efficiency and accuracy suitable for large-scale ocean detection), and load the algorithm parameters (such as sea area depth, seabed reflectivity, and initial values ​​of sound velocity profile). The corrected average propagation speed and the difference between the reciprocating propagation speeds are input into the algorithm. With the sound speed distribution as an intermediate variable, the original marine environmental parameters (initial distribution values ​​of temperature, salinity, and ocean current velocity / direction) corresponding to the sound speed parameters are restored through inverse operation. Using a correlation model that reflects the quantitative relationship between seawater sound velocity and temperature and salinity, the three-dimensional spatial distribution data of temperature and salinity fields in each detection area are retrieved based on the corrected average propagation velocity. The model uses the Mackenzie empirical formula for sound speed as the basis for sound speed inversion of temperature and salinity. Then, the corrected average propagation speed of each grid point in the detection area is used as input. The temperature and salinity of the corresponding points are obtained by inverse calculation through the correlation model. Then, the Kriging interpolation method is used to perform three-dimensional interpolation on the temperature and salinity values ​​of sparse sampling points in the area to complete the temperature and salinity field data of the entire area and form a continuous three-dimensional distribution. Simultaneously, by utilizing the correlation model that reflects the quantitative relationship between ocean current parameters and the difference in underwater acoustic reciprocating propagation speed, the vector three-dimensional distribution data of the flow field in each detection area are inverted based on the corrected difference in reciprocating propagation speed. The model that correlates sound velocity difference with ocean current velocity is invoked. The logic is that the sound velocity difference is proportional to the ocean current velocity. The corrected reciprocating velocity difference of each grid point in the detection area is used as input. The ocean current velocity at the corresponding point is calculated by the model and combined with the sound propagation direction to obtain the ocean current direction. Then, the velocity and direction are combined into a three-dimensional flow field vector to represent the ocean current state at each spatial point.

[0030] Spatial data fusion technology is used to stitch, integrate and optimize the three-dimensional distribution data of temperature, salinity and flow fields in each independent detection area, eliminate the stitching deviation between areas, and obtain multi-parameter distribution data of marine environment covering the entire target sea area through data fusion; Among them, the multi-parameter distribution data of the marine environment includes temperature, salinity, current velocity, and current direction; The weighted average fusion method (the best method, regional splicing adapted to marine environmental parameters) is adopted. The temperature, salinity / current field data of the overlapping boundaries of adjacent regions are weighted and averaged according to the principle of "the closer the distance, the higher the weight" to eliminate splicing bias. Non-overlapping regions are directly spliced, and then the global error of the fused data is calculated. The least squares method is used to correct the overall bias so that the mean and variance of the fused data match the overall environmental characteristics of the marine area. The fused temperature, salinity, and flow field data are integrated into a unified three-dimensional dataset and stored in a coordinate-temperature-salinity-flow velocity-flow direction format, forming multi-parameter distribution data of the marine environment covering the entire target sea area.

[0031] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A high-power marine acoustic tomography system for marine environmental monitoring, characterized in that: It includes a regional deployment and clock synchronization module, a signal generation and high-power transmission module, a signal reception and propagation time measurement module, a sound velocity calculation and reference area selection module, a sound velocity parameter weighting correction module, and an environmental parameter inversion and result fusion module; The regional deployment and clock synchronization module is used to determine the location of marine targets for marine environmental monitoring, divide the marine target locations into monitoring areas, and simultaneously deploy multiple sets of acoustic tomography moorings in each area to complete the positioning of mooring nodes, spacing calibration, and atomic clock GPS training. The signal generation and high-power transmission module is used to configure a dedicated 12th-order M sequence for each area's underwater buoy. After 2PSK modulation, a code division multiple access spread spectrum modulation signal is generated. Then, after DAC digital-to-analog conversion, 400W Class 4 power amplifier amplification, and 1:17 turns ratio transformer impedance matching, a 192dB sound source level high-power electrical signal is obtained. The underwater buoys in each area are controlled by a synchronous clock to synchronously convert it into an underwater acoustic signal for transmission. After transmission, the underwater buoys switch to receiving mode. The signal reception and propagation time measurement module is used to deploy the underwater glider transducer to receive underwater acoustic signals and convert them into weak analog electrical signals. At the same time, it completes 2PSK demodulation and M-sequence despreading through quadrature coherent demodulation to obtain a high signal-to-noise ratio digital baseband signal. The 12th order M-sequence of the transmitting end is used as a reference to perform time-domain cross-correlation operation with the baseband signal, extracts the autocorrelation function value and identifies the waveform peak time point to determine the bidirectional propagation time of underwater acoustic signals between underwater glider nodes. The sound velocity calculation and reference area selection module is used to calculate the sound velocity parameters of each region based on the calibration distance of the buoy node and the two-way propagation time, taking the region as a unit. At the same time, it constructs a feature vector for each detection region, calculates the similarity and difference values ​​between regions and between adjacent regions, quantifies the correlation and deviation of data, and selects a reference area. The sound velocity parameter weighting correction module is used to simultaneously calculate and determine the confidence level of each detection area and the confidence level of its neighbors. Based on the double confidence level determination results, the sound velocity parameter difference of each area is weighted and corrected to obtain the corrected sound velocity parameter. The environmental parameter inversion and result fusion module is used to substitute the corrected sound speed parameters into the ocean dynamics inversion algorithm for inverse operation. Combined with the seawater sound speed and temperature-salinity correlation model and the ocean current and sound speed difference correlation model, the three-dimensional distribution data of temperature-salinity field and current field in each region are inverted respectively. Then, the inversion results of all detection areas are fused to obtain the multi-parameter distribution data of ocean environment temperature, salinity, current velocity and current direction of the target sea area.

2. The high-power marine acoustic tomography system for marine environmental monitoring according to claim 1, characterized in that: The regional deployment and clock synchronization module is connected to the marine environment management terminal. The marine environment monitoring requirements are obtained from the marine environment management terminal. Based on the marine environment monitoring requirements, the marine area range for which environmental parameter detection is to be carried out is determined, i.e., the location of the marine target. Based on the topographic features, sea area size, and environmental detection accuracy requirements of the marine target location, a grid-based approach is adopted to divide the marine target location into multiple detection areas. These detection areas are spatially connected and their boundaries do not overlap.

3. A high-power marine acoustic tomography system for marine environmental monitoring according to claim 1, characterized in that: In the aforementioned regional deployment and clock synchronization module, at least two sets of acoustic tomography moorings are deployed in each detection area according to the detection range to form a mooring node network. At the same time, the precise geographical coordinates of each mooring node are obtained through GPS positioning technology, and the straight-line distance between any two mooring nodes is calculated and calibrated based on the geographical coordinates. The atomic clocks at each underwater buoy node are calibrated using the standard time signal from GPS to ensure that the time reference of each atomic clock is consistent. By calibrating with atomic clocks, the clocks of all underwater glider nodes are made error-free, achieving consistency in signal transmission and reception time for each underwater glider node.

4. A high-power marine acoustic tomography system for marine environmental monitoring according to claim 1, characterized in that: In the signal generation and high-power transmission module, a unique 12th-order M-sequence is assigned to all acoustic tomography moorings in each detection area. Moorings in different detection areas use different 12th-order M-sequences to distinguish the mooring signals in each area. A 12th-order M-sequence is loaded onto a carrier signal using 2PSK modulation, and the phase of the carrier signal is changed to represent the binary information of the M-sequence. The spread spectrum modulation signal of code division multiple access is obtained by 2PSK modulation and M-sequence spread spectrum, so that the buoy transmission signal of each detection area has a unique code pattern feature; The spread spectrum modulation signal is converted from digital to analog by a DAC to output an analog electrical signal. Then, the analog electrical signal is boosted by a 400W Class D audio power amplifier. Finally, a transformer with a turns ratio of 1:17 is used to achieve impedance matching between the power amplifier and the transducer. Through power amplification and impedance matching, the output electrical signal can drive the transducer to generate a 192dB acoustic signal at the source level. Based on a synchronized clock signal across the entire sea area, all underwater gliders are controlled to convert high-power electrical signals into underwater acoustic signals through transducers at the same time. Simultaneously, underwater gliders in each detection area transmit underwater acoustic signals to the sea area within their respective detection areas. After completing the transmission of underwater acoustic signals, the underwater gliders quickly switch their working modes via a transmit / receive switch, entering the underwater acoustic signal receiving state and waiting to receive underwater acoustic signals transmitted by other nodes.

5. A high-power marine acoustic tomography system for marine environmental monitoring according to claim 1, characterized in that: In the signal reception and propagation time measurement module, the underwater acoustic signal propagating in the ocean is converted into a weak analog electrical signal based on the transceiver integrated on the underwater buoy. By filtering out environmental noise and clutter in weak electrical signals using a bandpass filter, and then amplifying the filtered signal in stages using a fixed gain amplifier and a programmable gain amplifier to increase the signal amplitude; The carrier signal in the 2PSK modulated signal is removed by using orthogonal coherent demodulation to restore the M-sequence signal, thereby realizing the despreading and demodulation of the signal. Through demodulation and despreading processing, noise and interference in the signal are eliminated to obtain a high signal-to-noise digital baseband signal. The original 12th-order M-sequence signal from the transmitting end is used as a reference signal, and cross-correlation calculation is performed between it and the received and processed digital baseband signal in the time domain. Since the environmental noise is a random signal, it has extremely low correlation with the M-sequence signal from the transmitting end. The cross-correlation operation can effectively suppress the noise and extract the autocorrelation function value corresponding to the transmitted signal. The signal waveform after cross-correlation operation will show a significant peak at the arrival time of the underwater acoustic signal. By identifying the time point corresponding to this peak, the propagation time of the underwater acoustic signal from the transmitting node to the receiving node can be obtained, and the reverse propagation time, i.e., the bidirectional propagation time, can also be obtained.

6. A high-power marine acoustic tomography system for marine environmental monitoring according to claim 1, characterized in that: In the sound velocity calculation and reference area selection module, each detection area is used as an independent calculation unit. The straight-line distance between the calibrated nodes in the area and the bidirectional underwater acoustic signal propagation time between the nodes measured in the signal reception and propagation time measurement module are used as the calculation basis to calculate the corresponding sound velocity parameters for each detection area separately. Among them, the sound speed parameters include the bidirectional propagation speed between nodes, the regional average propagation speed, and the difference in reciprocating propagation speed between regional nodes; The correlation calculation method and the deviation calculation method are used to calculate the similarity and difference values ​​of feature vectors between any two detection regions and between adjacent detection regions, respectively. Then, the numerical results are used to characterize the correlation and deviation values ​​between the sound velocity parameters of each detection region. A larger numerical result likely indicates a stronger correlation and a smaller bias. Based on the similarity and difference values ​​obtained from quantification, the detection area with the smallest deviation between its own measured sound velocity parameter and the average value of multiple measurements, and the highest average similarity value with all surrounding areas, is selected as the benchmark reference area for weighted correction of the sound velocity parameter.

7. A high-power marine acoustic tomography system for marine environmental monitoring according to claim 1, characterized in that: In the sound velocity parameter weighted correction module, the self-confidence of each detection area and the adjacency confidence of the area with the adjacent reference area are calculated. The self-confidence is an index that characterizes the validity of the measurement data of the detection area based on the stability of the measurement data, the signal reception quality and the absence of interference. Set a self-confidence threshold and an adjacent confidence threshold, and combine the self-confidence and the adjacent confidence of the region with the adjacent benchmark reference regions to perform a double reset confidence determination; When the self-confidence of the detection area is greater than its own confidence threshold, and the adjacent confidence of the area and the adjacent benchmark detection area is greater than the adjacent confidence threshold, a weighted correction method is adopted with the original sound velocity data as the main factor and the sound velocity data of the adjacent benchmark area as the auxiliary factor to calculate the corrected average propagation velocity of the area and the corrected inter-node reciprocating propagation velocity difference. When the self-confidence of the detection area is less than its own confidence threshold, but the adjacent confidence of the area and the adjacent benchmark detection area is greater than the adjacent confidence threshold, a weighted correction method is adopted with the sound velocity data of the adjacent benchmark area as the main data and the original sound velocity data of the area as the reference. The corrected average propagation velocity and the corrected inter-node reciprocating propagation velocity difference of the area are calculated. When the adjacent confidence level between the detection area and the adjacent benchmark detection area is less than the adjacent confidence level threshold, no weighted correction is performed regardless of whether the confidence level of the area itself meets the standard. The original average propagation speed and the original inter-node back-and-forth propagation speed difference of the area are directly retained.

8. A high-power marine acoustic tomography system for marine environmental monitoring according to claim 1, characterized in that: In the environmental parameter inversion and result fusion module, the average propagation speed and the difference between the reciprocating propagation speeds after weight correction by the sound speed parameter weight correction module are used as input data and substituted into the established ocean dynamics inversion algorithm for inverse operation. The algorithm restores the original ocean environment parameters corresponding to the sound speed parameters. Using a correlation model that reflects the quantitative relationship between seawater sound velocity and temperature and salinity, the three-dimensional spatial distribution data of temperature and salinity fields in each detection area are inverted based on the corrected average propagation velocity. At the same time, using a correlation model that reflects the quantitative relationship between ocean current parameters and the difference in underwater acoustic reciprocating propagation velocity, the vector three-dimensional distribution data of the flow field in each detection area are inverted based on the corrected reciprocating propagation velocity difference. Spatial data fusion technology is used to stitch, integrate and optimize the three-dimensional distribution data of temperature, salinity and flow fields in each independent detection area, eliminate the stitching deviation between areas, and obtain multi-parameter distribution data of marine environment covering the entire target sea area through data fusion; Among them, the multi-parameter distribution data of the marine environment includes temperature, salinity, current velocity, and current direction.

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