1819 results about "Underwater acoustics" patented technology

The invention discloses a submarine cable pipeline detection method based on underwater acousto-optic fusion, and relates to the related field of underwater detection.The method comprises the steps that underwater sonar image data and underwater optical image data are acquired through underwater sensor equipment; carrying out preprocessing extraction to obtain sonar rectangular image data and standard optical image data; performing feature extraction by using a parallel double-branch convolution-Transform network to obtain an acoustic mode embedded feature set and an optical mode embedded feature set; carrying out attention perception and weighted fusion to obtain acousto-optic multi-modal fusion level features; decoding is carried out through a multi-mode decoder structure, and acousto-optic fusion pixel-level features are obtained; and carrying out detection classification by using a detection regression head and a classification head to obtain a submarine cable pipeline detection result. The technical problem that existing submarine cable pipeline detection is insufficient in detection precision and robustness is solved, and the technical effects of improving the detection precision and enhancing the detection robustness are achieved.

The invention discloses an underwater acoustic signal denoising method based on a time-frequency adaptive dual-path Conformer network, and the method comprises the steps: carrying out the preprocessing of real marine environment noise and underwater acoustic target signals, and generating a multi-signal-to-noise-ratio noisy data set; performing short-time Fourier transform on the noisy data, and extracting real part and imaginary part features to form a feature tensor; extracting features by using a feature encoder, and generating intermediate feature representation; respectively extracting a time path feature and a frequency path feature through a time-frequency adaptive dual-path Conformer network; performing multi-scale convolution processing and weighted fusion on the extracted time-frequency features by adopting a multi-scale fusion dynamic gating network; performing nonlinear mapping on the fused features by using a feature decoder to generate a mask matrix; and restoring the complex frequency spectrum based on the mask matrix, and restoring the denoised underwater acoustic time domain signal. According to the method, time-frequency information is fully mined in combination with underwater sound noise characteristics, and the underwater sound signal denoising effect is improved.

The invention discloses an acoustic-optical imaging semantic fusion detection method for underwater defects of a hydraulic structure, and the method comprises the steps: obtaining underwater acoustic and optical images of the hydraulic structure at the same time through underwater acoustic-optical imaging sensing for the underwater defects of the hydraulic structure; and on the basis, acoustic semantic features in the acoustic image are extracted through a semantic feature extraction module, optical image features in the optical image are processed by taking the acoustic semantic features as guidance, and a defect detection result is obtained when an underwater defect target exists in the image, so that underwater defect sound-light imaging semantic fusion detection is realized. The technology is suitable for health monitoring of various dam system hydraulic structures, and operation safety and water safety of hydraulic engineering are guaranteed.

An underwater acoustic sensor network node positioning error correction method combined with acoustic velocity inversion, which method relates to the technical field of acoustic velocity inversion. The method comprises: deploying an acoustic source point and underwater acoustic sensor nodes which are at fixed positions, the underwater acoustic sensor nodes collecting acoustic velocity data sent by means of the acoustic source point, and by means of acoustic velocity data at different underwater acoustic sensor nodes, using an acoustic velocity field inversion algorithm to construct an acoustic velocity field model; collecting real-time acoustic velocity data at the different underwater acoustic sensor nodes, and obtaining acoustic velocity characteristic information at the different underwater acoustic sensor nodes and water characteristic information at the different underwater acoustic sensor nodes; performing comprehensive analysis on the acoustic velocity characteristic information and the water characteristic information, and evaluating the positioning accuracy of the acoustic velocity field model in respect of the underwater acoustic sensor nodes; and comparing quantized positioning accuracy results for the underwater acoustic sensor nodes with a preset threshold, and when there is a relatively large error in the positioning accuracy, generating an error signal. The method is conducive to improving the precision of underwater acoustic wave propagation and the accuracy of underwater acoustic sensor network node positioning, and evaluating the positioning accuracy of an acoustic velocity field model in respect of underwater acoustic sensor nodes.

The invention relates to the technical field of underwater acoustic navigation, and provides an underwater acoustic navigation method and system, a medium, equipment and a computer program. The underwater acoustic navigation method comprises the following steps: calculating a sound ray total displacement value by adopting a sound ray tracking algorithm based on underwater carrier position information, transponder position information, and one-way propagation time and a sound velocity profile of a signal between an underwater carrier and a transponder; constructing an acoustic navigation observation model based on the underwater carrier position information, the transponder position information, the total displacement value of the sound rays, and depth and elevation anomalies at the underwater carrier; defining a weighting function according to the one-way propagation time and the dynamic value of the signal between the underwater carrier and the transponder; and solving the acoustic navigation observation model and the weighting function to obtain position information and speed information of the underwater carrier for acoustic navigation. According to the method, the navigation algorithm can be optimized from the two aspects of the acoustic navigation observation model and the weighting function, and the accuracy of acoustic navigation is greatly improved.

The invention relates to the related field of underwater acoustic signal processing and noise recognition, in particular to an underwater acoustic radiation noise recognition method, and provides an underwater acoustic radiation noise real-time recognition system based on combination of a convolutional neural network (CNN) and a long short-term memory network (LSTM). The system can perform efficient data preprocessing, feature extraction and optimization processing after collecting underwater acoustic signals in real time, and performs rapid identification of noise types through the trained mixed deep learning model. Specifically, the CNN can extract local features from a spectrogram, the LSTM is good at capturing time sequence information, complex underwater acoustic signals can be better understood and distinguished through combination of the CNN and the LSTM, and the recognition precision is remarkably improved. In addition, the system is further provided with an automatic updating mechanism, the model can be retrained regularly according to newly-collected data, it is ensured that the model is always in the optimal state, and the system adapts to the constantly-changing underwater environment.

The invention discloses an intelligent underwater sound signal processing system and method based on multi-sensor data fusion, and belongs to the field of underwater signal processing. A plurality of sensors are used for collecting underwater sound signals, linear acceleration, angular velocity and other information of an underwater target and outputting continuous analog signals; then the differential signals are transmitted to a signal conditioning module, gain is dynamically adjusted according to target intensity, amplified differential signals are output to an analog-to-digital conversion module, high-precision digitalization is carried out through a 24-bit delta-sigma ADC, digital signals are output to a main control unit, and the main control unit carries out multi-sensor data fusion and target orientation calculation; and finally, the main control unit uploads the underwater acoustic signal and the equipment attitude information data to an upper computer through an RS485 bus for man-machine interaction. The scheme is based on multi-sensor data fusion, can realize high-precision target detection and positioning in a dynamic underwater environment, and has wide popularization and application values.

The invention relates to the technical field of submarine engineering and information processing, in particular to a submarine cable positioning method and system, and the method comprises the steps: synchronously collecting magnetic field, structured light and underwater acoustic signal data, and fusing inertial navigation information to construct a unified space-time reference system; precise registration of heterogeneous data is realized by adopting sliding time window screening, multi-modal noise reduction and rigid body transformation; constructing a graph structure model for expressing cable space-signal features based on the fused data, and reasoning cable path topology by using a graph convolutional network; reconstructing a cable center line through three-dimensional visualization, embedding the cable center line into submarine topography, and realizing risk area dynamic clustering by combining signal integrity score and burial depth estimation; historical time sequence data are fused, a long short-term memory network is adopted to predict cable drift, detectable attenuation and burial depth change trends, risk levels are divided based on prediction results, and spatial distribution early warning and disposal suggestions are output. According to the invention, the cable positioning precision and the risk assessment intelligent level in a complex seabed environment are improved.

The invention discloses a bimodal neural network and spectral subtraction combined underwater acoustic signal noise suppression method. Comprising the following steps: firstly, constructing a simulation underwater sound data set containing self-noise; thirdly, constructing a bimodal neural network, then training the bimodal neural network until training is completed, and obtaining a platform self-noise estimation model; inputting a to-be-processed noise-containing underwater acoustic signal into the platform self-noise estimation model, and outputting estimation platform self-noise by the model; and finally, based on the self noise of the estimation platform, noise reduction processing is carried out on the noise-containing underwater acoustic signal to be processed by using the improved spectral subtraction method, and then a noise-reduced underwater acoustic signal is obtained. The method combines an advanced deep learning method and a classical spectral subtraction algorithm, has the capabilities of autonomous learning and efficient noise reduction, remarkably improves the signal-to-noise ratio of the underwater acoustic signal, is suitable for noise reduction of the underwater acoustic signal in a non-stationary and complex marine environment, remarkably improves the low-frequency noise suppression capability and the target signal fidelity, and has a good application prospect. The method is suitable for underwater acoustic target detection, underwater acoustic communication and other scenes.

Disclosed in the present invention is an underwater acoustic target recognition method based on effective receptive field regulation. A proposed AEU-Net model has a plurality of resolution branches, with each resolution branch having independent convolution kernels, which adaptively adjust their sizes during training while aligning with their respective resolutions. The AEU-Net model comprises an ERF-Server, which can regulate an effective receptive field during forward propagation, and has two operations of increasing or decreasing the effective receptive field of a feature map of any designated module. In the present invention, acoustic physical information of an underwater acoustic target sample can be captured in a plurality of interaction dimensions, and feature fusion of information from effective receptive fields across a plurality of scales is performed, so as to adapt to sonar image targets of different sizes, thereby improving the accuracy and recognition speed in the recognition of an underwater acoustic target.

The invention provides an underwater acoustic target recognition method based on autonomous task perception, and belongs to the field of underwater acoustic signal processing and artificial intelligence. S3, performing feature extraction on the time-frequency spectrogram by the task type extraction network to obtain a task embedding vector, calculating a similarity score, when the maximum similarity score is greater than a set threshold value, outputting a task representation vector, and entering S3, otherwise, inputting features output by the last Transform layer into a classifier; s3, selecting a router according to the task representation vector, selecting a trained expert network by the router, calculating a door control weight, processing universal acoustic features in parallel by the expert network, fusing output of the expert network, fusing deep features and fused features to obtain enhanced features, and enabling the enhanced features output by the last Transform layer to enter a classifier; the invention further provides a system. The problems that the task category autonomous recognition capability is insufficient and the correlation between tasks is ignored are solved.

The invention provides a system for improving positioning precision under a complex underwater acoustic channel and an optimization method, and relates to the field of underwater positioning. The system for improving the positioning precision under the complex underwater acoustic channel comprises a time delay estimation module, a sound velocity profile data module, a sound ray correction module, a slope distance value precision module and a precision improvement module, and the time delay estimation module is mainly used for obtaining more accurate time delay data; the sound velocity profile data module is mainly used for acquiring sound velocity profile data of a positioning area. According to the method, a real sound ray path is estimated by using a sound ray correction method, measurement errors of bubbles in water, sound velocity change and position and propagation time are considered, the positioning precision is further improved, and the comprehensive optimization method enables the system to accurately reflect the real position of a target in a complex underwater environment, so that the positioning accuracy is improved. The overall performance of underwater target positioning is remarkably improved, and the actual requirement of high-precision positioning is met.

The invention belongs to the technical field of pollution source detection, and discloses a pollution source detection method and system for water environment treatment. Comprising the steps that acoustic disturbance data are collected through an underwater acoustic sensor, real-time edge preprocessing is carried out, water environment data of different depths are synchronously collected, water environment gradient data are calculated, and then data synchronization is achieved through a unified timestamp. Fourier transform is adopted to extract frequency domain characteristics of the sound disturbance signals, the main frequency, the energy difference and the frequency spectrum skewness are calculated, and disturbance sound wave indexes are generated according to the main frequency, the energy difference and the frequency spectrum skewness. In combination with disturbance sound wave indexes and water body environment gradient data, a sliding window is adopted to construct a time sequence sample set, a recurrent neural network model for predicting future pollution release risks is trained, differentiated pollution release threshold values are set according to different water body types, and when model output exceeds the threshold values, sediment pollution early warning instructions of corresponding levels are automatically generated. And efficient and graded pollution event intelligent identification and response are realized.

The invention relates to the technical field of submarine cable fault positioning, in particular to a submarine cable fault positioning system based on underwater beacons. The method comprises the steps that an underwater acoustic beacon array unit monitors and collects submarine cable fault sound wave signals in real time based on underwater acoustic beacons; the fault signal processing analysis unit extracts submarine cable fault sound wave signal parameters based on a non-point source space sound source waveform analysis model, and generates multi-parameter fault sound source feature vectors; the sound source positioning analysis algorithm unit performs three-dimensional inversion positioning based on the multi-parameter fault sound source feature vector and the arrival time difference of the submarine cable fault sound wave signal to the underwater acoustic beacon, and obtains the spatial position information of the submarine cable fault point; and the data communication alarm management unit sends the spatial position information of the submarine cable fault point to a shore-based operation and maintenance system. The submarine cable fault positioning method is used for realizing a submarine cable fault positioning technology for quickly and accurately positioning and identifying spatial extension characteristics in a complex submarine environment.

The invention discloses a polar region adaptive underwater acoustic positioning correction method based on a multi-dimensional environment coupling model, belongs to the technical field of underwater acoustic positioning, is used for polar region underwater acoustic positioning, and comprises the steps of deploying a heterogeneous sensor network, constructing an environment sensing layer and eliminating a sensor drift error. Establishing a three-dimensional sound velocity model, fusing a vortex kinetic equation, a Kriging interpolation algorithm and an adaptive Runge-Kutta method to track a sound ray trajectory; designing a time-frequency double-domain joint detection algorithm, and combining a lattice IIR filter bank to realize multipath suppression; a 9-dimensional robust UKF algorithm framework is constructed, and multi-source data fusion is realized in combination with federated filtering; and terrestrial magnetism matching and a strain energy density early warning mechanism are integrated. Compared with the prior art, the method improves the positioning accuracy and stability, can more accurately predict the sound propagation path, improves the positioning precision, achieves the dynamic real-time calculation, can quickly and accurately process the continuously changing environment data, and is suitable for the complex polar region environment.

The invention discloses an off-network block sparse orthogonal matching pursuit underwater acoustic time delay Doppler domain channel estimation method, and belongs to the technical field of underwater acoustic channel estimation. The method solves the problems that a channel estimation method based on traditional orthogonal matching pursuit cannot estimate frequency shift parameters on non-grid points and is high in calculation complexity. According to the method, channel parameters of integer grid points are obtained based on a BOMP algorithm, and then residual Doppler factors on non-grid points are subjected to further fine estimation based on maximum likelihood criterion fitting. The problem that a traditional OMP channel estimation method in underwater OTFS communication can only estimate channel parameters on grid points is solved, and the estimation precision of channel frequency shift parameters is further improved by estimating frequency shift parameters on non-grid points; and meanwhile, a fractional order dictionary is prevented from being established in a channel parameter process, so that the system complexity is reduced to a certain extent. The method can be applied to underwater sound time delay Doppler domain channel estimation.

The invention relates to the technical field of underwater communication and positioning, in particular to a multi-mode underwater wireless communication system based on dynamic link calibration, which integrates underwater acoustic positioning guide, attitude adaptive tracking control, optical link enhancement and an error code correction mechanism. Comprising a central control core, an acoustic communication unit, an optical communication transmitting unit, an optical signal receiving unit, an optical control unit, a multi-axis attitude adjusting unit, a data fusion and verification processing unit and a high-speed modulation-demodulation and error code correction unit. And by using a double-shaft servo motor and automatic gain control, real-time target tracking and accurate alignment of optical signals are realized, FPGA and OFDM modulation are combined to optimize data throughput, and a data cross validation mechanism is introduced to correct error codes, so that the data integrity is improved.

The invention provides an underwater acoustic target recognition system and method based on multi-modal joint representation. The system comprises a multi-modal feature extractor, a feature fusion module, a fusion feature extractor and a full connection layer. The processing process of the system comprises the steps that multi-modal features based on waveforms and spectrograms are extracted through a waveform network and a spectrogram network of a multi-modal feature extractor respectively, the two-way structural design allows the network to capture features of an underwater acoustic target from different angles, and understanding of signal features is enhanced; the two kinds of feature information of a spectrogram and a waveform are fused through a feature fusion module, and the two kinds of features are fused through a multi-head attention mechanism; and finally, constructing a fusion feature extractor to extract global features. The system disclosed by the invention obtains a very competitive result on two widely used ship radiation noise data sets. Experimental results show that the performance of the system exceeds that of a traditional waveform-based and spectrogram-based model on the premise of keeping a relatively low parameter quantity.

The invention belongs to the technical field of underwater positioning, and discloses an underwater single-beacon Gaussian Newton positioning method and system under a linear sound profile condition. According to the method, underwater acoustic signal propagation time observation noise is modeled into Gaussian distribution, a maximum likelihood estimation problem is constructed, and then the maximum likelihood estimation problem is converted into a nonlinear least square estimation problem; and solving the gradient of the observed quantity to the position coordinates of the AUV based on a chain rule, iteratively solving a nonlinear least square estimation problem based on a Gauss-Newton method, and obtaining the position coordinates of the AUV after iterative convergence. Compared with other single beacon positioning methods which do not consider sound propagation refraction, the method of the invention can obtain a more accurate position calculation result under a linear sound profile condition, and enhances single beacon positioning, especially the practical application capability of the method in deep sea areas.

The invention belongs to the technical field of underwater positioning, and discloses an underwater single beacon virtual long baseline positioning method and system capable of estimating an unknown sound velocity. The method comprises the following steps: constructing a virtual long baseline array by combining stored dead reckoning data; constructing a virtual long baseline positioning problem by combining the relationship between the underwater acoustic signal propagation time and the position and sound velocity of the AUV; auxiliary variables are introduced, a pseudo-linear positioning equation is obtained based on algebraic conversion, and an AUV position initial solution is obtained based on a weighted least square method; and constructing a second-step linear estimation problem by exploring the relationship between the auxiliary variable and the AUV position, and solving by using weighted least squares to obtain a more accurate AUV position estimation solution. Compared with a single-beacon positioning method which does not consider sound velocity unknown, the method provided by the invention can obtain more ideal position estimation performance, and the practical application capability of an underwater single-beacon positioning system is enhanced.

The invention provides an underwater positioning system combining sound ray propagation modeling and multipath interference suppression, which relates to the technical field of marine equipment and comprises a self-adaptive underwater sound sensing and environment cognition module, an environment self-adaptive sound ray propagation modeling module, a multipath suppression and direct wave extraction module and a multi-dimensional information fusion and self-adaptive positioning calculation module. The self-adaptive underwater sound sensing and environment cognition module is used for realizing multi-mode sensing and real-time environment feature recognition of underwater sound signals, and the environment self-adaptive sound ray propagation modeling module is used for constructing a dynamically updated sound wave propagation model and providing a propagation path and time constraint for positioning calculation; the multi-path suppression and direct wave extraction module is used for carrying out multi-path signal separation and extracting direct wave features with confidence marks, and the multi-dimensional information fusion and self-adaptive positioning calculation module is used for completing high-precision positioning calculation; according to the system, deep fusion of an environment model and signal processing can be realized, and the positioning precision and robustness of the underwater vehicle in a complex underwater sound environment are remarkably improved.

The invention relates to the technical field of data fusion, and discloses an underwater-based multi-source data feature fusion method, which comprises the following steps: acquiring ocean underwater sound data acquired by a multi-source sensor, preprocessing the ocean underwater sound data, inputting the preprocessed ocean underwater sound data into a trained denoising auto-encoder, extracting initial feature representation, and performing feature fusion on the initial feature representation; optimizing the K-means initial clustering center by adopting an energy valley optimization algorithm to obtain optimal initial clustering center configuration; operating a K-means algorithm based on the optimal initial clustering center configuration, performing clustering analysis on the initial feature representation, and selecting a feature fusion strategy according to a clustering result to obtain a comprehensive feature representation; and outputting a target recognition result through a classification algorithm according to the comprehensive feature representation. And the accuracy, the robustness and the efficiency of underwater target recognition are remarkably improved.

The invention provides an anti-interference sound signal data extraction method based on energy data perception, and belongs to the technical field of underwater sound signal processing, and the method comprises the steps: carrying out the A / D sampling and digital orthogonal demodulation of a received sound signal, and obtaining a baseband signal; performing low-pass filtering and downsampling on the baseband signal to obtain a preprocessed signal; performing matched filtering on the pre-processed signal by using a reference signal; the key improvement lies in that a local energy estimation function of a preprocessed signal is calculated, and normalization processing is carried out on a matched filtering result and the energy estimation function to obtain a normalized detection signal. The method effectively suppresses the shielding effect of a strong interference signal on a weak target signal under a non-orthogonal reference signal and a strong near-end and weak far-end scene, remarkably reduces the misrecognition probability, and improves the accuracy and reliability of the extraction of a long-distance and low-signal-to-noise-ratio target beacon signal in a complex underwater acoustic environment. The method is suitable for underwater navigation, positioning and communication systems.

The invention relates to an underwater acoustic sensor network routing method based on fuzzy logic, which comprises the following steps that: a Sink node periodically broadcasts a HELLO packet to perceive topological change, and each node locally and dynamically maintains a parameter Hcur representing the hop count of the node for representing the hop count from the node to the Sink node; in the relay selection process, the sending node broadcasts a PING packet; after the neighbor node monitors the PING packet, the neighbor node whose hop count to the Sink node is less than or equal to that of the sending node becomes a candidate node, and the candidate node calculates the link quality between the sending node and the node and replies the PONG packet in a point-to-point manner; (4) within the waiting time, the sending node receives the PONG packet replied from the candidate node, and constructs a candidate node routing table; and (5) after the timing of the waiting time is finished, selecting an optimal forwarding node based on a candidate node routing table and a fuzzy system.

The invention provides a visual underwater sensor network simulation method, and relates to the technical field of underwater sensor networks, and the method comprises the steps: configuring network simulation parameters which comprise topological structure parameters, channel parameters and simulation parameters; inputting the channel parameters into an underwater acoustic calculation tool to generate an underwater acoustic multipath channel for simulation; the bit error rate BER of the multi-carrier MFSK underwater acoustic communication algorithm under the underwater acoustic multipath channel condition is calculated, and the bit error rate BER is input into the OTcl script; performing network simulation on the configured OTcl script by using an underwater network simulator; and displaying a network simulation result through a visual interface. The underwater sensor network simulation result can be displayed through the visual interface, the defect that current NS2-based underwater sensor network simulation software does not have a visual interface is overcome, a good man-machine interaction interface can be provided for a user, the development efficiency is improved, and the user is helped to better evaluate the performance of the underwater sensor network.

The invention belongs to the technical field of underwater acoustic engineering and digital signal processing, and particularly relates to a fractional delay FIR (Finite Impulse Response) phase calibration method and system of a multichannel sonar system. Comprising the steps of signal acquisition and reference channel selection; high-precision relative delay estimation; calculating a cross-correlation function of other channel signals and the reference channel signal, performing coarse estimation by searching a peak point of the amplitude of the cross-correlation function, and performing fine estimation on the peak position by using a parabola interpolation method to obtain the delay time of each channel relative to the reference channel; designing an FIR fractional delay filter; digital filtering and phase calibration; verifying a calibration effect; and calculating the phase of each channel signal at the target frequency point after calibration, and counting the standard deviation of each channel signal so as to evaluate the improvement degree of phase consistency. The method aims at overcoming the defect that traditional integral multiple sampling delay correction precision is insufficient, high-precision phase compensation of a sub-sampling point level is achieved, and therefore the performance of algorithms such as beam forming of a multi-channel sonar system is remarkably improved.

The invention discloses a sound compatibility test and evaluation method, device and equipment for an underwater acoustic system of an ocean platform and a storage medium, and relates to the technical field of ocean acoustics, and the sound compatibility test and evaluation method for the underwater acoustic system of the ocean platform comprises the steps: obtaining target noise data of the ocean platform; platform parameters of the ocean platform are obtained, a simulation model is constructed according to the platform parameters and the target noise data, and the simulation model comprises an environment noise model, a sound propagation model and a platform self-noise model; obtaining to-be-tested equipment parameters of the ocean platform, and obtaining target data according to the to-be-tested equipment parameters and the simulation model; and performing performance evaluation and comprehensive analysis on the ocean platform according to the target data so as to complete acoustic compatibility test and evaluation of the acoustic system. According to the invention, the accuracy of acoustic compatibility testing and evaluation of the underwater acoustic system of the ocean platform can be improved.

The invention discloses an optical fiber silicon-based microstructure underwater acoustic sensor which is a non-intrinsic F-P air microcavity probe with optical signal interference formed by an optical fiber end face and a silicon-based thin film, the characteristic that the silicon-based thin film is sensitive to sound pressure signals is used for picking up sound pressure changes in a liquid sound field environment, the sound pressure changes are converted into the variable quantity of an optical signal interference phase, and the optical fiber silicon-based microstructure underwater acoustic sensor is obtained. Reducing a sound pressure signal in the liquid sound field through a detection demodulation algorithm; the microcavity probe is packaged by using an organic material thin shell structure, so that the hydrostatic pressure resistance of the underwater acoustic sensor is improved to adapt to application in a deep water environment; the optical fiber silicon-based microstructure underwater acoustic sensor comprises an acoustic sensing silicon microstructure, a microstructure sensing probe and a sensor pressure-resistant structure, according to the acoustic characteristic design of the acoustic sensing silicon microstructure, silicon oxide / silicon nitride and other materials are used as sensing materials, and membrane external stress recovery is achieved through internal stress residues. The method is easy to implement in design process, flexible in application and high in environmental adaptability, and can be applied to noise monitoring and target detection in various complex scenes.

The invention discloses an underwater acoustic target identification method and system based on an expert hybrid model, and the method comprises the steps: introducing an unbalanced bandwidth Mel spectrogram for solving the problems that the low-frequency features of underwater acoustic signals are not fully extracted and the frequency distribution is unknown because the distribution rules of the underwater acoustic signals of different frequency bands are different; therefore, the learning ability for different frequency band features is improved, and the time-frequency feature enhancement for an underwater acoustic target recognition task is realized. In order to solve the problem that the learning ability of a model for different frequency band features is limited when different sub-band features are identified, an expert hybrid model for different sub-band features is designed, and the learning ability of a UBM-MoER network for different frequency band features is improved. In order to further explore how to improve the feature extraction capability of the model under the expert hybrid model, a hybrid domain attention mechanism module is designed, and the sensitivity of each expert hybrid model to key features is expected to be improved through a channel and space attention mechanism, so that the overall performance of the model is improved.

The invention belongs to the field of underwater acoustic networks, and relates to underwater Internet of Things, underwater environment monitoring, resource development and marine ecological protection. In order to provide an overall architecture of a simulation platform and an implementation technology of a core module of the overall architecture, and further enhance the simulation capability of an underlying protocol and a physical channel on the basis of being compatible with a standard network protocol stack and supporting seamless deployment of an existing application program, the invention provides an underwater Internet of Things semi-physical simulation system based on a container. Comprising a terminal host, a gateway node with underwater sound and wired network communication capabilities, and a plurality of real nodes which can be deployed underwater and have underwater sound communication capabilities, and an MAC simulation module and a channel simulation module are deployed on the terminal host and are used for constructing a simulation environment; and the semi-physical simulation interfaces are simultaneously deployed on the terminal host and the gateway node, so that traffic mapping in a simulation environment of the gateway node and the terminal host is realized. The method is mainly applied to design and manufacturing occasions of an underwater Internet of Things simulation system.