33 results about "Acoustical oceanography" patented technology

The invention discloses a method and a system for constructing a characteristic of influence of a solitary wave on underwater sound propagation loss in an ocean. The method comprises the following steps: acquiring acoustic environment parameters of a specified simulated ocean area to construct an ocean acoustic environment; constructing a plurality of internal solitary wave structures according to the internal solitary wave characteristic parameters; combining the marine acoustic environment and the internal solitary wave structure to construct a plurality of internal solitary environment models; setting a sound source and a receiver, and calculating a sound field by adopting a kraken model based on the internal isolated environment models and the set sound source and receiver to obtain sound propagation loss of each internal isolated environment model; and comparing and analyzing the sound propagation loss of various internal isolated environment models to obtain the influence of the internal isolated waves on the sound propagation loss characteristics. The invention aims to improve the accuracy of the characteristic data of the underwater sound propagation loss influenced by the marine internal solitary wave based on the influence of the internal solitary wave characteristic parameters on the sound propagation loss.

The invention provides a marine acoustic environment monitoring system based on a noise map. The marine acoustic environment monitoring system comprises a buoy network module, a data fusion processing module, a noise map generation module, a noise source identification module and an edge response module, the buoy network module serves as a sensing tail end, multi-source information is gathered to the data fusion processing module through the first data bus for standardization processing, and then the noise map generation module is driven through the second data bus to construct a three-dimensional sound field model. The output of the model is transmitted to a noise source identification module through a third data bus for intelligent analysis and traceability; the alarm decision of the noise source identification module is output to the edge response module, and the edge response module dynamically regulates and controls the communication and acquisition strategy of each buoy node through a distributed control circuit to form a complete closed loop. According to the invention, high-precision and automatic monitoring of the marine acoustic environment is realized, the problems of limited monitoring range and single data dimension of the traditional means are solved, and the reliability and emergency capability of the system are improved.

The invention relates to a wind noise modeling method and system based on machine learning and application, and belongs to the field of marine acoustics and environmental noise modeling, and the method comprises the steps of data preprocessing and feature extraction, hybrid network model building, hybrid network model training and verification. According to the method, a hybrid network model of multiple linear regression and a multi-layer perceptron is constructed based on a known physical mechanism of wind noise and two main noise generation mechanisms of surface turbulence and bubble oscillation, a linear relation and a non-linear relation are modeled respectively, outputs of the two models are integrated through a weighted fusion strategy, and a multi-layer perceptron model is constructed. Smooth transition modeling from a low-wind-speed linear relation to a high-wind-speed nonlinear relation is achieved.

The invention discloses a sonar video target detection method and device based on a domestic sublimation AI processor, and belongs to the technical field of marine acoustic detection, and the method comprises the following steps: S1, building an edge intelligent hardware platform based on an Atlas200I A2 acceleration module; s2, designing a forward-looking sonar video target detection network, introducing a motion excitation module to enhance inter-frame motion features, and designing a self-attention feature aggregation module to aggregate time context features; s3, training the sonar video target detection network by using a two-stage training method, storing the weight and the structure of the trained network as an ONNX reasoning framework diagram file, and quantifying the weight process of the model by using an asymmetric quantization method; and S4, deploying a video target detection network by using a mercuric chloride CANN heterogeneous reasoning framework. According to the method, the challenge that a single-frame detector has poor detection on conditions such as motion blur and target hiding in a forward-looking sonar video is overcome, the detection precision is improved, and application and development of a localized AI processor in a forward-looking sonar detection technology are promoted.

The invention discloses a quantitative evaluation model and system for underwater sonar detection efficiency by mesoscale vortexes, and belongs to the technical field of marine acoustic detection. The method comprises the following steps: acquiring marine environment data; identifying vortex types (cold vortex / warm vortex / mixed vortex), positions, radiuses R and amplitudes A based on a velocity gradient method of optimized parameters and a deep learning model; utilizing an ROMS numerical mode to simulate a vortex three-dimensional thermohaline structure; generating a sound velocity disturbance coefficient in combination with a Wilson sound velocity model and the vortex amplitude A; calculating propagation loss PL (z) through a Bellhop sound field model; an acoustic detection efficiency index ADEI is constructed; and outputting the detection probability matrix. According to the method, the technical bottleneck that a traditional model cannot dynamically couple the vortex acoustic effect and the detection probability is solved, full-process automatic evaluation is realized, and a sonar deployment strategy is remarkably optimized.

The invention relates to the technical field of underwater towing, in particular to an underwater towed body towing performance design method, which comprises the following steps of: performing static dynamic analysis on a towed body; carrying out dragging dynamics analysis on the towed body; constructing a towed body structure model, and performing hydrodynamic simulation calculation; establishing a dynamic coupling numerical simulation model of the towing system; and carrying out dragging attitude simulation, and obtaining an optimal design scheme. According to the invention, key indexes such as trim and rolling of the towed body in the towing system are ensured to meet the stability standard, so that the urgent requirements of marine acoustic detection on high precision and high reliability are met.

The invention relates to the technical field of marine acoustics, in particular to a multi-criterion selection method for a complex marine environment sound field calculation model, which comprises the following steps: S1, acquiring environmental parameters of a target sea area; s2, judging whether the terrain is horizontally changed, whether the seabed sediment is horizontally changed and whether the sound velocity profile gradient is horizontally changed according to the environmental parameters, if the terrain is horizontally unchanged, the seabed sediment is horizontally unchanged and the sound velocity profile gradient is horizontally unchanged, judging that the sound field environment level is unchanged, otherwise, judging that the sound field environment level is changed; and S3, selecting a model according to whether the sound field environment level is changed or not, if the sound field environment level is unchanged, selecting a shallow sea smooth average sound field model or a deep sea generalized phase integral model WKBZ according to a sea depth threshold value, and if the sound field environment level is changed, selecting a parabolic equation model RAM or a ray model BELLHOP according to a terrain type and a frequency threshold value. Through a multi-criterion selection method, scientization and engineering of complex marine environment sound field calculation model selection can be realized.

A sonar video target detection method and device based on a domestic Ascend AI processor belongs to the field of marine acoustic detection technology. The method includes the following steps: S1, building an edge intelligent hardware platform based on the Atlas200I A2 acceleration module; S2 designing a forward-looking sonar video target detection network, introducing a motion excitation module to enhance inter-frame motion features, and designing a self-attention feature aggregation module to aggregate temporal context features; S3, using a two-stage training method to train the sonar video target detection network, saving the trained network weights and structures as ONNX reasoning framework graph files, and using an asymmetric quantization method to quantize the model weight process; S4, using the Ascend CANN heterogeneous reasoning framework to deploy the video target detection network. The present invention overcomes the challenge of poor detection of motion blur and target concealment in forward-looking sonar videos by single-frame detectors, improves detection accuracy, and promotes the application and development of domestic AI processors in forward-looking sonar detection technology.

The present invention discloses a method and system for constructing the characteristics of the underwater sound propagation loss of the influence of the internal solitary wave in the ocean. The present invention comprises the following steps: obtaining the acoustic environment parameters of the designated simulated ocean area to construct the ocean acoustic environment; constructing a variety of internal solitary wave structures according to the characteristic parameters of the internal solitary wave; combining the ocean acoustic environment and the internal solitary wave structure to construct a variety of internal isolated environment models; setting a sound source and a receiver, and using the kraken model to calculate the sound field based on the internal isolated environment model and the set sound source and receiver to obtain the sound propagation loss of various internal isolated environment models; and comparing and analyzing the sound propagation loss of various internal isolated environment models to obtain the influence of the internal solitary wave on the sound propagation loss characteristics. The present invention aims to improve the accuracy of the characteristic data of the underwater sound propagation loss influenced by the internal solitary wave in the ocean based on the influence of the characteristic parameters of the internal solitary wave on the sound propagation loss.

The invention belongs to the field of marine acoustics, and discloses a wind speed-based multi-stage modeling regression marine environmental noise model modeling method, which comprises the following steps of: firstly, acquiring and matching marine noise and wind speed data, and extracting a noise power spectrum; fitting the noise spectrum into gamma distribution, and extracting shape and scale parameters as physical statistical characteristics; in the first stage, Gaussian process regression is utilized to establish mapping from wind speed to distribution parameters; in the second stage, the distribution parameters and the wind speed serve as input, a mixed kernel support vector regression model fusing a Wenz empirical physical kernel and an RBF data kernel is adopted, and a full-band noise spectrum is predicted. According to the method, a complex prediction problem is decomposed through a framework of'physical statistical feature guidance ', physical priori and data-driven learning are effectively fused, prediction precision, generalization ability and robustness of the model under the condition of data scarcity are remarkably improved, and a high-precision noise prediction tool is provided for applications such as ocean wind field monitoring and the like.

This invention relates to an adaptive correction method for multi-beam backscattering based on statistical change point detection, belonging to the field of marine acoustic detection and seabed sediment information processing technology. The method includes acquiring multi-beam data and constructing a proxy signal; adaptively grouping the proxy signal sequence using a bisection change point detection algorithm; statistically processing the backscattering intensity data within each group according to the beam incident angle; smoothing the average angle response curve using a smoothing filter; unsupervised partitioning based on curvature and clustering; and constructing a piecewise angle response reference model, using this model to calculate the correction value. This invention effectively weakens the central highlighting, edge attenuation, and striped structure caused by angle response effects, reduces the block effect or mosaic phenomenon common in fixed window methods, and effectively weakens the systematic correlation between backscattering intensity and incident angle, providing a more stable and reliable data foundation for subsequent seabed sediment classification, seabed geomorphological identification, and related marine survey applications.

This invention relates to the field of underwater acoustic positioning and topographic detection in marine environments, providing a rapid calculation method and system for the incident angle and propagation time of a direct sound ray between two points. The method includes: dividing seawater into layers based on a sound velocity profile; adaptively dividing the incident angle interval according to a preset comprehensive error threshold; calculating the horizontal displacement and propagation time of the sound ray corresponding to different incident angles; and constructing an interpolation coefficient table through third-order polynomial fitting; combining the piecewise displacement of the water depth intervals where the sound ray originates and terminates, obtaining the third-order polynomial fitting coefficients of the horizontal displacement with respect to the incident angle from the polynomial interpolation coefficient table, constructing a cubic equation with respect to the incident angle, solving it using the Cardano method, and iteratively obtaining the incident angle correction value according to a preset angle error threshold; calculating the propagation time; and finally outputting the incident angle and propagation time of the sound ray. This method replaces layer-by-layer calculation with interpolation modeling, achieving high accuracy, rapid convergence, and coefficient table reuse.

This invention relates to the field of marine acoustic modeling and underwater acoustic environment forecasting, and in particular to an integrated method for dynamic extraction of Kuroshio frontal features and sound field modeling based on publicly available data. Through the integrated design of dynamic encryption of publicly available data, frontal feature quantization and PINNs modeling, this invention greatly reduces the sound field forecasting error of Kuroshio frontal regions and reduces the cost of CTD measured data.

This invention relates to the field of marine acoustic detection technology and discloses a dynamic suppression algorithm for real-time elimination of the influence of tidal motion on sonar signals. The method collects sonar signal sequences and tidal motion parameter sequences for the current sea area and divides the sonar signal into multiple time windows. Within each window, the phase offset is calculated based on the tidal parameters and converted into time-domain compensation coefficients through an established dynamic coupling model, performing preliminary phase correction on the sonar signal. Spectral analysis is performed on the initially suppressed signal to extract residual interference features, and the parameter weights of the dynamic coupling model are adjusted accordingly to generate an optimized model. This optimized model is applied to the processing of the next time window, forming an iterative suppression closed-loop process. This method can adaptively track the dynamic changes of tidal motion, effectively weakening its periodic modulation interference on sonar signals and improving the accuracy of sonar detection data in strong tidal areas such as nearshore and estuaries.

The application belongs to the field of ocean acoustics, and discloses a marine environmental noise modeling method based on multi-stage modeling regression of wind speed, which firstly collects and matches marine noise and wind speed data, and extracts noise power spectrum; then fits the noise spectrum into a gamma distribution, and extracts shape and scale parameters as physical statistical characteristics; further, a two-stage prediction model is constructed: the first stage uses Gaussian process regression to establish a mapping from wind speed to distribution parameters; the second stage uses a hybrid kernel support vector regression model that fuses Wenz empirical physical kernel and RBF data kernel to predict the noise spectrum in the full frequency band. Through the framework of "physical statistical characteristic guidance", the application decomposes complex prediction problems, effectively fuses physical priori and data-driven learning, significantly improves prediction accuracy, generalization ability and model robustness under data scarcity conditions, and provides a high-precision noise prediction tool for marine wind field monitoring and other applications.

The invention discloses a multi-channel whale-dolphin sound detection method and a passive sonar system, and relates to the technical field of marine acoustic monitoring. The method comprises the following steps: setting a plurality of channels for receiving sound wave signals of whale guinea chirps of different frequency bands; setting a noise acquisition mode and a detection mode which are sequentially carried out in each cycle period; determining an amplitude threshold of each channel in a noise acquisition mode; in the detection mode, for each channel receiving the to-be-detected sound wave, down-sampling the to-be-detected sound wave, and calculating an envelope; identifying the number of envelope sample points which continuously exceed an amplitude threshold; if the number of the enveloping sample points is greater than or equal to the preset pulse width threshold, determining that the to-be-detected sound wave of the frequency band received by the channel is a sound wave signal of whale-guinea sound, and taking the to-be-detected sound wave as a target channel; and when a plurality of target channels exist, fusing the down-sampled sound wave signal frequency bands of the plurality of target channels, and determining the frequency range of the whale-dolphin whistling sound. According to the invention, the operand can be reduced, and the detection of various whale-guinea-pig sounds can be realized at the same time.

The invention discloses an in-situ acoustic parameter calculation method for a detection device incompletely penetrating into a sedimentary layer, and belongs to the field of marine acoustic characteristic detection. Comprising the steps that after consistency calibration is conducted on sedimentary layer sound wave data according to an underwater reference sound field, corresponding envelope curves are obtained through envelope fitting, and travel time and amplitude of a sedimentary layer sound field are further obtained; calculating to obtain travel time corresponding to each receiving point of the detection device, connecting lines according to the sequence of the receiving points to obtain travel time curves under different scanning sound velocities, and performing least square matching on the travel time curves and the travel time of the sound field of the sedimentary layer to obtain the sound velocity of the sedimentary layer; calculating to obtain the amplitude corresponding to each receiving point, and connecting according to the sequence of the receiving points to obtain an amplitude curve under different scanning sound attenuation; and performing least square matching on all the amplitude curves and the sound field amplitude of the sedimentary layer to obtain the attenuation coefficient of the sedimentary layer. The method disclosed by the invention effectively makes up the blank of related research when the measurement condition that the stratum is hard or the detection device is incompletely penetrated is encountered in the actual construction process.

The present application belongs to the technical field of marine acoustic equipment, and particularly relates to a multi-beam sounding system error correction method based on sounding non-conformity value angle correlation, to solve the technical problems that the existing multi-beam sounding system sounding data error elimination algorithm does not consider the formation mechanism and characteristics of system error, and the error suppression effect is poor. The present application provides a multi-beam sounding system error correction method based on sounding non-conformity value angle correlation, which is derived from the sounding point three-dimensional coordinate reduction principle and error propagation theory, and according to the correlation between the system error caused by multi-element random error to the sounding and the beam angle, an error-angle correlation model is constructed in the multi-beam cross line area, and then the system error in the sounding data is weakened in the whole area, the applicability of the method to weaken the system error and the sounding precision are improved, the authenticity of the multi-beam sounding data reflecting the underwater topography is improved, and it has important practical significance.

The application discloses a kind of ice layer parameter inversion system and method based on photoacoustic effect inversion, it is related to marine acoustic technology field, realize non-contact, high efficiency, end to end, high accuracy, physically interpretable detection sea ice parameter, solve the problems of poor real-time in prior art, artificial dependence, complex calculation, the system includes: laser excitation module, for emitting intensity modulated laser to the measured area of sea ice surface;Signal acquisition module is used to receive the target sound wave associated with the time-frequency characteristics of laser;The target sound wave is generated by the photoacoustic effect of the measured area irradiated by laser;Data processing module is used to restore the ice layer parameter of the measured area based on the target sound wave according to the preset inversion process.

The present invention relates to the field of ocean acoustics, and in particular to a method, device, equipment and medium for wide-area ubiquitous perception of ocean sound fields. The method comprises: calculating the predicted water acoustic signal at the second space-time coordinate according to the autocorrelation function value of the water acoustic signal collected at the first space-time coordinate, the cross-correlation function value between the predicted water acoustic signal at the second space-time coordinate corresponding to the first space-time coordinate and the water acoustic signal collected at the first space-time coordinate; and reconstructing the channel impulse response vector corresponding to the collected water acoustic signal at the first space-time coordinate and the second space-time coordinate respectively based on the absolute value norm or the logarithmic norm. The present application predicts the water acoustic signal at the second space-time coordinate through the water acoustic signal at the first space-time coordinate collected by the distributed optical fiber acoustic sensing system, thereby forming effective observations that are continuous in time and space in a wide range of the ocean, and realizing the acquisition of wide-area ubiquitous perception signals of the ocean sound field and robust prediction in terms of timing at uncovered points.

This invention belongs to the field of marine acoustic detection and remote sensing technology, and discloses a method and system for inverting the abundance of deep-sea mixed sediments based on bottom echo data. The method involves preprocessing the bottom echo waveforms of multibeam bathymetry data to obtain a bottom echo sequence; extracting features from the bottom echo sequence waveforms and concatenating these features to the corresponding bottom echo waveforms to form a new waveform sequence; fusing the enhanced waveform sequences to construct an observation matrix Y; decomposing the observation matrix Y into an endmember waveform matrix H′ and an abundance distribution matrix W′, and introducing a minimum volume constraint during the decomposition process; adding a spectral reconstruction error term based on dynamic time warping to the loss function; iteratively optimizing H′ and W′ using gradient descent or multiplicative update rules until convergence; and obtaining the endmember waveform H′, which represents the acoustic response waveform of typical sediment types, and the abundance matrix W′, which reflects the mixing ratio of each endmember at different sampling points, demonstrating high classification accuracy.

The invention belongs to the technical field of crossing of marine acoustics and machine vision, and particularly relates to a sound field region segmentation method based on improved ResNet-34, which comprises the following steps of: 1, performing sound propagation loss image data set and sound field simulation setting; 2, designing a segmentation network and improving the structure; constructing a segmentation backbone network based on ResNet-34, extracting multi-layer semantic and fine-grained texture features by adopting residual connection and convolution stacking, and outputting a multi-level feature map required by a segmentation head as input of a subsequent improvement module; a deformable convolution self-adaptive sampling sound linear density mutation region is introduced into the Stage3, multi-scale feature fusion is carried out across to improve the sensing capability of convergence / shadow region structures with different sizes, and a global statistical feature module is added behind the Stage4 to carry out remarking and enhancement on multi-scale statistics, so that local boundaries and global distribution are considered; and step 3, evaluating standard definition based on a result of gray calculation. According to the invention, the accuracy and generalization ability of the segmentation result in different sea areas, different seasons and various sound channel structures are improved.

The present invention belongs to the technical field of high-performance computing and computational ocean acoustics, and particularly relates to an MPI dynamic scheduling parallel acceleration method for an underwater acoustic propagation ray model. The method includes the following steps: performing parallelism analysis on the serial code of the underwater acoustic propagation ray model to establish an MPI dynamic scheduling parallel strategy; decomposing static tasks and dynamic scheduling tasks; completing ray calculations and the dynamic scheduling process. When the dynamic scheduling tasks are completed; in the global communication domain, all processes add and reduce the sound pressure matrix, and the final result is stored in the main process. The main process writes the sound pressure matrix into the result file. Compared with OpenMP parallelism, the present invention can run on a multi-node cluster system, greatly improving the upper limit of the parallel acceleration ratio, overcoming the problem of extremely easy load imbalance in MPI static task allocation, greatly reducing the communication time between processes, and ensuring load balance during static task allocation.

The application relates to the technical field of data acquisition, in particular to a measurement system and a measurement method of marine physical acoustics parameters. The measurement system comprises an upper computer module, a sensor module, an interface module, a waveform transceiving module and a communication control module. Compared with the prior art, the measurement system can realize double excitation of sinusoidal and pulse wave signals, can simultaneously measure various marine acoustics parameters based on control instructions, can synchronously monitor marine environmental physical parameters such as the salinity and temperature of seawater, the pressure and temperature of seabed sediments and acoustics parameters such as the sound velocity and sound attenuation, and can correct the results, meanwhile, the measurement system supports external power amplification and linear sweep of the emitted signal, the multifunctionality and practicability of the marine physical acoustics parameter measurement system are improved, the seamless switching operation of multiple frequency bands and multiple sensors is realized, portability is realized, and the function integration of in-situ measurement and laboratory measurement is realized.

The present invention discloses a method and system for predicting the acoustic characteristics of seabed sediments based on a physical neural network. The method comprises: obtaining seabed samples from a target sea area; obtaining a seabed sample data set based on seabed sample measurements; training a pre-constructed neural network architecture based on the seabed sample data set, and then optimizing the construction using a loss function to obtain a physical information neural network; wherein the constraints of the loss function include information about a wave equation; and predicting the acoustic characteristic parameters of the seabed sediment to be predicted using a physical information neural network based on the physical parameters and environmental parameters of the seabed sediment to be predicted. The present invention has the beneficial effects of constructing a method for predicting the acoustic characteristics of seabed sediments based on a physical neural network by substituting the wave equation of the acoustic characteristics of the sediment into the physical information neural network and combining it with the constraints of the seabed sediment, thereby being able to accurately predict the acoustic characteristics of the sediment and being widely applicable to the field of marine acoustic technology.

The invention provides a buoyancy adjusting propelling type underwater unfolding device and method, relates to the technical field of marine acoustic measurement, and aims to solve the problem that the tensioning efficiency of a propeller is low in the current floating process, a limiting cylinder and a buoyancy cylinder which are matched with each other are arranged at the tail end of a shell, and the buoyancy cylinder slides relative to the limiting cylinder under the action of a driving element; one end, facing the impeller of the propeller, of the buoyancy cylinder is a spherical crown surface, and the spherical crown surface of the buoyancy cylinder is close to the impeller in the floating process so as to guide the operation wake flow of the impeller, so that the working efficiency of the propeller is improved, and the floating and tensioning efficiency is improved; and the requirement of quickly adjusting the position of the sensor linear array is met.

The invention belongs to the technical field of ocean acoustics, and particularly relates to an ocean sound channel feature recognition method based on improved Faster R-CNN, which comprises the following steps: step 1, sound velocity data preprocessing and imaging; step 2, an improved Faster R-CNN algorithm adopts a Res-Net 101 network to extract image features of the input image, and an image feature map is obtained; step 3, selecting candidate regions through a region suggestion network; step 4, performing detection frame screening by a non-maximum suppression method; the method comprises the following steps: introducing a Soft-NMS strategy into an improved Faster R-CNN algorithm, and screening out a detection frame with relatively high confidence coefficient; 5, after the sizes of the candidate regions are improved to be uniform, the sizes of the candidate regions output by the RPN network are fixed, and the candidate regions are directly input to a full connection layer for classification and regression prediction; and processing the vectors of the full connection layer through a Soft-max classifier, and selecting the type corresponding to the maximum probability as the classification of the target. According to the method, the recognition accuracy and the space-time generalization capability are improved, and the sound channel feature recognition capability in a complex marine environment is enhanced.

A two-dimensional low-frequency shallow sea sound field calculation method based on a physical information neural network relates to the technical field of marine acoustic calculation and artificial intelligence, and comprises the following steps: step 1, obtaining shallow sea area sound velocity profile data; 2, carrying out data preprocessing on the time sequence sound velocity profile data; 3, designing a physical information loss function; 4, constructing a dual-network parallel PINN physical information neural network model; and step 5, using the trained dual-network PINN model to calculate the modal function and the corresponding horizontal wave number of the shallow sea area, embedding the calculation result into the coupling frame, and calculating the low-frequency shallow sea sound field with the sound velocity changing horizontally. According to the method, a loss function is designed by using a modal equation in a normal wave, corresponding boundary conditions and the like, so that the physical interpretability of the neural network is enhanced, and the black box problem of deep learning is solved.

This invention discloses an optical hydrophone structure based on Fresnel zone plate sensitization, belonging to the field of underwater acoustic signal detection technology. It includes a single-mode optical fiber, an elastic cylinder, a Fresnel zone plate, and a metal cavity. The single-mode optical fiber is uniformly and tightly wound around the elastic cylinder, which is located inside the metal cavity. One end of the metal cavity has an acoustic wave incident aperture. The Fresnel zone plate is fixed at the acoustic wave incident aperture of the metal cavity and has several concentric acoustic transmission rings and blocking rings. This invention employs the above-mentioned optical hydrophone structure based on Fresnel zone plate sensitization, which is simple in structure, has few components, and low cost. It possesses good water pressure resistance and electromagnetic interference resistance, making it suitable for complex underwater environments such as marine acoustic exploration and underwater weak target monitoring.

The application provides a method and device for determining a floating photovoltaic platform offshore construction period. The method collects noise data generated by various types of construction machinery in the operating state in real time in the construction sea area of the floating photovoltaic platform; inputs the noise data into a pre-constructed marine sound propagation model, combines the environmental parameters of the construction sea area, and calculates the predicted noise influence value of at least one preset focus point in the construction sea area; after obtaining the corresponding time-division environmental protection noise limit value of the construction sea area, based on the comparison result of the predicted noise influence value and the environmental protection noise limit value of the corresponding period, a compliant construction period table is generated. The method combines real-time noise monitoring with marine acoustic model prediction to automatically generate a construction period table that meets environmental protection regulations, and intelligently and dynamically plans the high-noise operation time of the floating photovoltaic platform offshore.