915 results about "Underwater acoustics" patented technology

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 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 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 high-frequency shallow water bottom reverberation signal modeling method, and belongs to the technical field of underwater acoustic engineering and signal processing. The method comprises the following steps: setting a modeling hypothesis; a cylindrical array geometric structure is constructed, scattering units are divided, and channel parameters and scattering functions are obtained in combination with a Bellhop tool and a GABIM model; generating a reverberation signal time domain expression, deducing an array receiving signal spatial domain expression and calculating a spatial spectrum; the model can also adjust underwater environment parameters to realize simulation under different conditions, and performs time domain and space domain theoretical analysis on reverberation signals based on a cylindrical array. Multi-dimensional underwater environment parameters are fused, and the modeling precision is improved; a receiving and transmitting combined cylindrical array is adapted, and spatial gain is embodied; experiments prove that the average error is within 3dB, the practicability is outstanding, support can be provided for design and optimization of the sonar system, and the method is suitable for underwater reverberation signal simulation and characteristic analysis of the sonar system in the high-frequency shallow water environment.

The invention discloses an open architecture system for an underwater acoustic signal processor, and belongs to the technical field of underwater acoustic signal processors. The system comprises a hardware resource pool, a standardized algorithm container group, a visual processing link construction module, a containerized deployment and scheduling platform and a data processing and communication plane. According to the system, special hardware resources are pooled, an underwater sound processing algorithm is packaged into a standardized container, a user is allowed to arrange a processing flow in a graphical mode, an intelligent scheduling platform dynamically deploys the algorithm container to matched hardware for execution according to real-time resource conditions, and meanwhile, an efficient and reliable data communication channel is provided. According to the method, the problems that a traditional system is closed, poor in expansibility and low in resource utilization rate are solved, elastic sharing of hardware resources, agile deployment of an algorithm and flexible construction of a flow are achieved, and openness, flexibility and overall efficiency of an underwater acoustic signal processing system are remarkably improved.

The invention relates to the field of underwater acoustic communication and detection, in particular to a waveform optimization method and system for an underwater acoustic detection and communication integrated system. According to the method, a constant modulus phase is taken as a constraint, a reserved subcarrier phase is taken as an optimization variable, and a multi-objective function for joint optimization of a peak-to-average power ratio and a non-periodic autocorrelation integral sidelobe ratio is constructed; a population is initialized, an objective function is evaluated, and a parent population is obtained through non-dominated sorting and crowding distance calculation; generating filial generations through selection, crossover and variation, combining the filial generations with the parent generations, and updating sorting and crowding distance to maintain population diversity; introducing a local gradient descent refinement mechanism, carrying out gradient iteration adjustment on an optimized individual under a constant modulus phase condition, and adaptively adjusting a crossover probability, a mutation probability and a phase variation scale according to a diversity evaluation result; and outputting a Pareto optimal solution set after the maximum number of iterations is reached, and obtaining an optimized sounding and communication integrated orthogonal frequency division multiplexing signal, so that the underwater communication and detection comprehensive performance can be effectively improved.

The invention relates to the technical field of underwater acoustic engineering and ocean exploration, in particular to a multi-channel underwater acoustic signal transmitting and measuring system and a signal processing method, which are used for solving the problems that the existing multi-channel underwater acoustic signal processing generally depends on fixed threshold detection or simple time sequence alignment, and the signal processing efficiency is low. The problem that residual vibration, local noise abrupt change or multipath reverberation is easily misjudged as an effective first arrival signal in a complex underwater environment due to the fact that the influence of the physical response characteristic of a transducer on emission time calibration is not considered and effective verification on whether a candidate arrival structure accords with a sound wave propagation physical rule is lacked is solved. According to the invention, a multi-channel energy time sequence and hydrophone space geometrical relationship is fused through an underwater sound propagation initial physical judgment module, a judgment mechanism is constructed based on distance-time consistency of sound propagation, and self-consistent verification is realized by combining a cross-channel arrival sequence and sound velocity constraint. And the accuracy, the robustness and the physical interpretability of the judgment of the effective launching time of the underwater sound are obviously improved.

The invention relates to the field of bionic hidden underwater acoustic communication, in particular to an underwater acoustic communication hidden performance evaluation method, device and equipment based on a neural network, and the method comprises the steps: carrying out the feature extraction of a to-be-evaluated underwater acoustic signal, and obtaining a feature sequence of the to-be-evaluated underwater acoustic signal; converting the feature sequence into a two-dimensional matrix with a preset size; the two-dimensional matrix is predicted according to a pre-trained underwater acoustic signal classification neural network model, a probability matrix is obtained, the probability matrix comprises a first probability and a second probability, the first probability represents the probability that the to-be-evaluated underwater acoustic signal belongs to the bionic modulation signal, and the second probability represents the probability that the to-be-evaluated underwater acoustic signal belongs to the bionic modulation signal; the second probability represents the probability that the underwater acoustic signal to be evaluated belongs to the real marine organism signal, and the underwater acoustic signal classification neural network model comprises an input layer, a first convolutional layer, a first pooling layer, a second convolutional layer, a second pooling layer, a global pooling layer and a full-connection output layer which are connected in sequence. The dependence on artificial feature design in a traditional recognition method is reduced, and the recognition stability in a complex underwater environment is improved.

The invention discloses a sparse regularization direction of arrival estimation method based on a risk minimization principle, and belongs to the technical field of array signal processing and underwater acoustic signal processing. The method comprises the steps of receiving array signals and establishing an observation model; constructing a sparse representation and over-complete dictionary; establishing and initializing a regularization optimization model; carrying out adaptive weight updating and risk-driven parameter selection; after regularization parameters are determined, a fast iterative shrinkage threshold algorithm FISTA is adopted to carry out optimization solution, and dictionary refinement is carried out on the detected direction after each iteration convergence so as to reduce off-grid errors; and after a small amount of outer layer iteration is repeated, outputting a final DOA estimation result and corresponding power. According to the method, self-adaptive selection of regularization parameters and noise levels can be realized, and the problem of precision degradation under complex conditions of low signal-to-noise ratio, limited snapshot number, signal source correlation, power imbalance and the like is effectively solved without manual parameter adjustment, so that the robustness and practicability of estimation are remarkably improved.

The invention discloses an underwater acoustic processing CPU-GPU dynamic load balancing method based on a task flow model, and belongs to the technical field of underwater acoustic processing. The method comprises the following steps: deconstructing an underwater acoustic processing application into a task flow model represented by a directed acyclic graph; establishing a feature portrait including calculation complexity, parallelism and data throughput for each task node, and constructing a cost prediction model; the CPU / GPU utilization rate and the data transmission performance are monitored in real time; a processor is distributed to each task node by using a dynamic programming algorithm in combination with a cost prediction model and a real-time system state with the goal of minimizing the total task flow completion time; and dynamically scheduling tasks through a central scheduler according to a decision result, and periodically updating a strategy. According to the method, the defects that static task division lacks adaptability and neglects task dependence and communication overhead are overcome, dynamic and efficient utilization of CPU and GPU resources is achieved, and the efficiency and real-time performance of underwater sound processing are remarkably improved.

The invention provides a motion compensation method and device for passive sonar detection. The compensation method comprises the following steps: acquiring underwater acoustic signals acquired by a passive sonar in real time within a preset period; performing segmentation processing and Fourier transform on the underwater acoustic signal to obtain an underwater acoustic frequency domain signal segment; constructing a parameterized search matrix of each water audio frequency domain signal segment, and determining a motion compensation parameter of each water audio frequency domain signal segment through similar energy spectrum search; based on the motion compensation parameter, performing phase compensation on the water audio frequency domain signal segment to obtain a compensation frequency domain signal segment; and performing inverse discrete Fourier transform on the compensation frequency domain signal segment to obtain a compensation time domain signal segment so as to determine a long-time coherent integration result corresponding to the underwater acoustic signal. Through the method, the gain of passive sonar detection processing is improved under the condition of low signal-to-noise ratio, and the accuracy and stability of passive sonar detection are improved.

The invention relates to the field of underwater acoustic detection, and discloses a high-resolution target orientation estimation method and device based on adjacent grid re-optimization, and the method comprises the steps: carrying out the expansion of an array manifold into a virtual array manifold, taking the virtual array manifold as a covariance matrix of an over-complete dictionary construction receiving signal, and carrying out the iterative updating of a noise signal component and a signal component; re-optimizing the adjacent grids to update the dictionary, and performing orientation estimation again in the re-divided intervals; and obtaining a direction-of-arrival estimation result through spectrum peak search. By applying the method, the orientation estimation performance of the underwater vehicle can be improved, and the DOA estimation performance with higher robustness and higher estimation precision can be obtained on a small array.

The invention discloses an underwater acoustic target classification and identification method based on multi-domain feature deep fusion and multi-platform sonar data learning. The method comprises the following steps: firstly, preprocessing target tracking beam data collected by a multi-platform sonar, and constructing a time domain waveform sample set and a time-frequency spectrogram sample set; secondly, constructing an underwater acoustic target classification and recognition network model which comprises a primary feature extraction parallel double-branch network module and a feature deep fusion and classification recognition module, the primary feature extraction parallel double-branch network module extracts time domain and time frequency features respectively, and the feature deep fusion and classification recognition module realizes feature deep fusion and classification through a bidirectional cross attention mechanism; training the model by adopting a multi-stage training strategy, wherein the training comprises pre-training, special training, end-to-end training and fine tuning and incremental training; finally, unknown target data is preprocessed and then input into the trained model for forward reasoning, and a classification recognition result is output according to the majority voting principle. According to the method, target multi-domain information can be fully mined, multi-source data are effectively utilized, and the model generalization ability and the recognition precision are improved.

The invention discloses an underwater acoustic signal communication and positioning integrated method. The existing method does not jointly consider the problem of channel resource occupation caused by integration of communication and positioning. According to the method, mutually orthogonal Chirp subcarriers are used as communication signals, meanwhile, long Chirp signals are used as pilot frequency, and the pilot frequency and the communication signals are overlapped together to be sent at a sender, so that extra channel overhead caused by additional pilot frequency is saved, and the signal sending time interval is shortened. A receiver uses a plurality of pilot signals to perform joint Doppler estimation and compensation and time of arrival estimation, and the result of the time of arrival estimation is used for a positioning system and a communication system on the one hand. According to the demodulation mode, correlation calculation is carried out by using different Chirp subcarriers, and symbol demodulation is carried out according to correlation values. According to the communication and positioning integrated waveform design method, the signal sending time interval can be shortened, the system complexity can be reduced, and good communication performance is achieved.

The invention discloses a distributed file backup method for a sonar database system, which belongs to the technical field of sonar data processing and comprises the following steps: acquiring file data containing underwater acoustic signal data from the sonar database system; fragmenting the file data to generate data blocks and dynamically adjusting the sizes of the data blocks; generating a unique identifier for the data block based on the content hash value and the metadata; selecting distributed storage nodes based on availability, storage capacity, network delay and geographical distribution factors; the transmission data block uses an encryption protocol; updating the storage position and state of the backup metadata record; periodically verifying the integrity and availability of the backup data; executing a data recovery process to recover the original file data; the dynamically adjusted backup policy is based on real-time operation requirements and data importance levels. According to the method, through distributed storage and dynamic optimization, the backup efficiency, the data security and the system reliability are improved, and the method is suitable for sonar data management in an underwater detection system.

The invention discloses a method for detecting, positioning and classifying underwater gas leakage through multiple beams based on deep learning, and relates to the field of underwater acoustic detection and deep learning. And carrying out azimuth correction on the multi-beam data with attitude offset to obtain multi-beam data without attitude offset. And through operations of seabed detection, median filtering, morphological expansion and the like, a high-quality multi-beam sonar image is generated. And constructing a data set for training an instance segmentation model and a flux classification model. And performing gas plume detection and segmentation on the preprocessed sonar image, and outputting mask information. And extracting a target contour based on a mask, determining plume coordinates through minimum circumscribed rotating rectangle fitting, and calculating the position of a seabed leakage point in combination with a conversion factor. And selecting complete plume data near the position right below the sonar equipment, and classifying leakage flux levels based on plume morphological characteristics by adopting a flux classification model. Multi-beam sonar and a deep learning algorithm are integrated, and detection, positioning and classification of underwater gas leakage are realized.

The present invention belongs to the field of marine underwater positioning, navigation and timing. Disclosed are an underwater navigation and communication integrated position service system and method. The system comprises a buoy base station, a submerged buoy base station, a seabed base station and a shore-based operation and control unit, and the system utilizes a three-dimensional base station network observation mode combining the buoy base station, the submerged buoy base station and the seabed base station. The position service system and method in the present invention have the capabilities of underwater acoustic navigation and communication, satellite positioning and communication, atomic clock timekeeping, hydrological measurement and responding to instructions of a shore-based operation and control unit, have the functions of autonomous, real-time and online base station position and clock calibration and sound velocity field correction, have the functions of base station position and clock calibration, and sound velocity field correction and control based on the shore-based operation and control unit, and can expand the range and functions of underwater position services, and provide accurate positioning, navigation and timing services for water-surface / underwater users.

The invention discloses a vector hydrophone array orientation estimation method for performing tensor decomposition by using a propagation operator, relates to the technical field of vector hydrophone array orientation estimation, and discloses a vector hydrophone array orientation estimation method for performing tensor decomposition by using a propagation operator. The method comprises the following steps: firstly, constructing a three-dimensional array manifold tensor composed of an array direction matrix and a vector hydrophone output matrix; respectively expanding received signal tensors according to three modes, solving a propagation operator based on a column block covariance matrix, constructing a normalized signal subspace, establishing a spatial spectrum function with a noise subspace, and obtaining a pitch angle and an azimuth angle of a sound source through spectrum peak search; according to the method, high-order singular value decomposition is avoided, the operand is greatly reduced, meanwhile, high resolution and low sidelobe direction finding performance are kept, and the method is suitable for a real-time underwater acoustic direction finding system of a ship-borne platform, a buoy platform and an unmanned platform.

The invention discloses a multi-modal underwater acoustic target identification method based on matrix entropy improvement. The method comprises the following steps: firstly, acquiring actual sonar signal data, calculating time-frequency characteristics of a beam forming signal and spatial characteristics of a matched filtering signal, and further generating an underwater acoustic signal data set; constructing a multi-modal deep recognition network based on multi-attention optimization, training the multi-modal deep recognition network to obtain a trained multi-modal deep recognition network, and fusing a target class with a feature center; and finally, calculating a time-frequency feature and a spatial feature corresponding to the sonar signal data to be detected, taking the time-frequency feature and the spatial feature as the input of the trained multi-modal deep recognition network, and generating a signal recognition result according to a feature vector output by the network and the target class fusion feature center. According to the method, the abnormal detection and metric learning method is used for replacing traditional probability judgment for recognition, the problems that in underwater target recognition, the number of target class samples is small, and the samples are unbalanced can be remarkably solved, and therefore high-accuracy and high-generalization underwater target recognition is achieved.

The invention relates to the technical field of underwater target detection, in particular to an underwater image target detection method and system based on multi-modal fusion. The method comprises the following steps: acquiring an underwater optical image and sonar image pair and a trained underwater multi-mode target detection network; inputting an image pair into the network, and respectively extracting multi-scale features of an optical mode and a sonar mode through a double-branch backbone network; a cross-modal feature offset is calculated by using an attention mechanism and a confidence mask through a multi-modal feature alignment module, and spatial adaptive alignment of acousto-optic features is realized; a multi-head zoom dot product attention and parallel double-gating structure is adopted through a cross-modal feature fusion module, bimodal complementary features are adaptively screened and fused, and target feature representation is enhanced; and the fused features are sent to the detected neck and head to complete target classification and positioning. According to the method, the problems of underwater acousto-optic mode space dislocation and imaging difference are solved, and the precision and robustness of target detection in a complex underwater environment are improved.

The invention discloses a multi-scale self-distilled underwater acoustic signal identification method and system based on saliency mask modeling, and belongs to the field of artificial intelligence and underwater acoustic signal processing. Comprising the following steps: (1) introducing a saliency mask mechanism to construct a self-supervised contrast learning framework for pre-training, and shielding a key information region in a time-frequency domain through an attention-guided saliency map mask mechanism; (2) designing a multi-scale self-distillation architecture, and aligning global and local information of the weak target on category and block mark levels through global and local distillation mechanisms; and (3) pixel-level reconstruction loss is introduced for collaborative optimization, and joint optimization of semantic perception and fine-grained feature modeling is realized by recovering signal details of a masking area. And (4) a fine tuning stage: only training a classification head by freezing the backbone network. According to the method, robust and high-generalization-ability audio representation can be obtained without depending on large-scale annotation data, and the accuracy and adaptability of underwater sound signal recognition are improved.

The invention provides an underwater target detection system and method for an AUV-carried synthetic aperture sonar, and belongs to the technical field of underwater acoustic detection. The method comprises the following steps: transmitting an ultra-wideband sound wave signal to an underwater target, and collecting an echo signal by using receiving array elements which are non-uniformly and sparsely arranged along a track direction; preprocessing the echoes and decomposing the echoes into a plurality of non-overlapping sub-band signals, obtaining an equivalent space response by calculating inter-channel phase differences under each sub-band, and further constructing a joint observation matrix; and high-resolution azimuth scattering intensity distribution is recovered in combination with compressed sensing, and motion compensation is performed by using platform motion data to realize underwater imaging. According to the invention, the dependence of the traditional synthetic aperture technology on long-distance uniform-speed motion is broken through, and high-resolution and high-precision underwater target detection and imaging can still be realized under the condition of high-speed navigation of an AUV (Autonomous Underwater Vehicle).

The invention relates to an active sonar echo signal phase jump compensation method and device based on a synchronous serial number, and belongs to the technical field of underwater acoustic signal processing. The method comprises the following steps: adding a synchronous serial number into a wet end signal to form an expansion matrix; transmitting the extended signal to a signal processor and windowing the extended signal; traversing a synchronous serial number in the windowing signal, and detecting the continuity of the synchronous serial number to judge whether packet loss exists or not; if packet loss exists, positioning a packet loss position and performing zero padding operation; and finally, carrying out fast Fourier transform on the zero-filled signal to obtain a spectral analysis result. According to the method, accurate identification and phase compensation of the packet loss position are achieved through the synchronous serial number, the method is suitable for underwater acoustic single-frequency signals, and when the packet loss rate does not exceed 2.5%, spectrum sidelobes can be effectively restrained, and the peak value of the spectrum sidelobes is reduced to be within 10% of the packet loss state. The method has the advantages of being clear in principle, easy and convenient to operate and high in calculation efficiency, is suitable for real-time signal processing, and remarkably improves the reliability and efficiency of target detection.

The invention discloses an echo signal processing method based on a multi-order FRFT domain feature fusion sparse dictionary, and belongs to the technical field of underwater acoustic signal processing and target detection, and the method comprises the steps: screening out a plurality of differential fractional orders through employing the differential features of a target echo and a reverberation signal in different fractional order transform domains; constructing a sparse sub-dictionary based on each differential fractional order, and training a fusion weight of each sub-dictionary through an adaptive gradient optimization strategy; and finally, target signal extraction and strong reverberation suppression are realized through weighted fusion and sparse reconstruction. According to the method, the accuracy and stability of a sparse reconstruction result can be improved, the signal-to-mixing ratio of signals can be effectively improved, target echoes and strong reverberation are further distinguished, good robustness and engineering application value are achieved, and an effective technical means is provided for underwater weak signal detection and strong interference.

The invention discloses a reinforcement learning routing optimization method considering a dynamic underwater sound environment and node mobility. The method comprises the following steps: performing parameter initialization on an underwater sensor network; monitoring and updating underwater environment parameters and node positions in real time, and dynamically updating a network topology connection state; during data transmission, constructing a multi-dimensional state vector, selecting a routing action by adopting an epsilon-greedy strategy, and generating a routing decision; if the data packet transmission fails when the routing action is executed, ending the iteration, otherwise, calculating a multi-target reward value; updating the Q value table to optimize the routing decision; and monitoring network performance indexes in real time, adaptively adjusting reinforcement learning parameters of the intelligent agent for next iteration, and outputting an adaptive routing selection model until an iteration termination condition is met. According to the method, time-varying channel and node mobility challenges can be intelligently dealt with, and multi-objective optimization balance of energy consumption, delay and stability is realized while the data delivery rate is remarkably improved.

The invention discloses an ultra-short baseline positioning method based on a binary short baseline, and belongs to the technical field of underwater acoustic target navigation positioning. The method comprises the following steps: S1, designing a spread spectrum underwater acoustic communication signal for underwater acoustic communication between an ultra-short baseline transceiver and a transponder; s2, solving ultra-short baseline time delay information by adopting a frequency domain correlation method, and obtaining high-precision time delay information through an interpolation method; s3, two sets of transceivers with known positions are installed at the ship end at a preset interval distance, and the two sets of transceivers form a binary short baseline framework; installing at least one group of transponders at the mobile terminal, and obtaining the distance information of the two groups of transceivers and the transponders and the initial position information of the mobile terminal; and S4, based on the two groups of distance information and the initial position information, performing binary short baseline positioning calculation by adopting an iterative optimization algorithm to obtain high-precision position information of the mobile terminal. The ultra-short baseline positioning method based on the binary short baseline is high in positioning precision and high in stability, and can still realize high-precision target positioning in a low signal-to-noise ratio environment.

The invention discloses a land-based data automatic receiving and preprocessing management system and method, and relates to the technical field of ocean observation data processing, and the system comprises a communication service subsystem, a data preprocessing subsystem, an intelligent perception linkage subsystem, and a data management subsystem. And full-process automatic processing of underwater acoustic real-time communication subsurface buoy observation data is realized through modular integration. Automatic receiving, safe storage and efficient preprocessing of deep sea power and biogeochemical element data are achieved, reliable data support is provided for marine scientific research and environment monitoring, and the long-term observation requirement of a 6000-meter water depth subsurface buoy system is met.

The invention discloses an underwater intelligent cable spatial form reconstruction system based on fusion of fiber bragg grating sensing and underwater acoustic positioning, and belongs to the technical field of underwater measurement and sensing. A plurality of fiber bragg grating sensors are inscribed on a three-core fiber in the intelligent cable of the system along the length direction to measure multi-point strain data along the length direction. According to the strain distribution, the bending curvature and the torsion of each measuring point are calculated, a continuous curvature and torsion function is constructed, the tangent vector, the normal vector and the sub-normal vector of each point of the cable are solved in combination with a Freen-Serret algorithm, and the tangent vector is integrated to obtain the space curve form of the cable. And determining the absolute position of the cable terminal in the three-dimensional space according to the acoustic ranging result. And realizing spatial form and attitude recognition of the whole underwater cable through constraint fusion of a curve reconstruction result and an end point position. According to the system, an inertial measurement unit is arranged on a cable to obtain local attitude information, so that the accuracy and the stability of curve reconstruction are improved. The method can be widely applied to space state monitoring of underwater long cable operation.

The invention discloses an underwater Internet of Things destroy-resistant link optimization method combining self-guided graph representation and AMF. The method comprises the following steps: 1) generating a scale-free topology and a candidate link set according to node positions, residual energy and underwater acoustic channel quality; 2) inputting the topology into a self-guided graph representation learning model, and applying contrast constraint through unsupervised training to obtain robust node embedding representation; 3) on the basis of candidate link two-end embedding, combining node degree, betweenness centrality, bridge edge coefficient and link success rate to construct feature representation, and generating an existence probability matrix through a probability mapping function; 4) inputting the matrix into adaptive multi-layer filtering (AMF), reserving edges according to Top-k to form a connected skeleton, setting a hierarchical threshold according to node hierarchy and load, and maintaining approximate power law degree distribution; and 5) applying a global connectivity constraint, and outputting an optimized link structure. According to the method, link optimization can be realized without supervision labels, network connectivity and survivability are improved in an attack environment, and the method is suitable for ocean monitoring and security scenes.

The invention discloses a dynamic configuration management method for underwater acoustic components under an underwater acoustic open architecture, which belongs to the technical field of underwater acoustic systems and comprises the following steps: establishing an underwater acoustic component warehouse for performing full-life-cycle tracking management on the underwater acoustic components; establishing a dynamic access control model, dividing the underwater acoustic component into different security levels and operation categories based on the component identifier and the version number, and configuring a dynamically adjustable permission rule for each underwater acoustic component instance; when an operation request for a target underwater acoustic component is received, performing permission verification on the operation request through the dynamic access control model; recording an operation log in real time when the operation on any underwater acoustic component is executed; and when it is monitored that the version of the currently deployed underwater acoustic component causes system abnormality, automatically triggering rollback operation. According to the method, the rollback operation can be automatically triggered when the component defect is monitored, the system is quickly recovered to the previous stable state, and the robustness and the service continuity of the underwater acoustic system are remarkably improved.