1338 results about "Submarine" patented technology

The invention discloses a submarine scoured pipeline stability early warning method based on artificial intelligence, and relates to the technical field of submarine pipeline protection, and the method comprises the steps: collecting and sorting submarine pipeline historical reconnaissance data and monitoring data of a target region; and constructing a seabed-submarine pipeline local fine three-dimensional hydrodynamic-sediment numerical model on the basis of a solution Navier-Stokes equation and a sediment transportation model. According to the method, the historical reconnaissance data, the monitoring data and the numerical model result are integrated, the coupling effect of various factors can be comprehensively considered, then the dynamic characteristics of seabed scouring are more comprehensively captured, key scouring factors influencing the stability of the pipeline are more accurately recognized, and meanwhile, the scouring process and result simulated by the numerical model are combined, so that the stability of the pipeline is improved. According to the method, the stress distribution and deformation conditions of the pipeline under different scour depths can be analyzed more accurately, then an early warning signal is sent out in advance, the risk that the pipeline is damaged due to scour is effectively reduced, and the reliability and accuracy of early warning are improved.

The invention discloses a submarine pipeline state early warning method and system based on scour-vibration coupling sensing, and the method comprises the steps: obtaining the historical marine environment information of a target submarine pipeline region, laying a monitoring sensor array according to the historical marine environment information, collecting multi-source monitoring data, and carrying out the preprocessing of the multi-source monitoring data, thereby forming a multi-source monitoring data set. And constructing a multi-physical field coupling analysis model, inputting the data set into the model to analyze the response condition of the pipeline in the current seabed environment, and obtaining pipeline response simulation information. And analyzing resonance influence caused by environmental scouring in different sections, generating resonance derivative characteristics, and constructing pipeline multi-modal fusion characteristics in combination with monitoring data. Predicting the future response state of the pipeline through the submarine pipeline state analysis model, and carrying out risk early warning prompt; meanwhile, the fatigue life of each section is analyzed based on resonance derivative characteristics, fatigue-prone sections are identified, and maintenance suggestions are generated and pushed. Therefore, the accuracy and timeliness of sensing the state of the submarine pipeline are improved, and the early warning intelligence of the submarine pipeline is effectively improved.

The invention relates to the technical field of ocean exploration, in particular to a non-buried submarine pipeline identification method and system based on multi-beam point clouds, and the method comprises the steps: obtaining three-dimensional point cloud data of a submarine pipeline region, and carrying out the preprocessing of the obtained point cloud data; performing neighborhood construction based on the preprocessed point cloud data, setting a mask attention mechanism according to a neighborhood structure, performing geometric coding and feature enhancement by using an output result of the mask attention mechanism, performing decoding operation on the enhanced features, recovering feature representation of the complete point cloud, and performing suspended span height calculation and verification based on a segmentation result. According to the method, a decoding strategy combining reverse distance weighted interpolation and jump connection is adopted, enhanced features are accurately propagated to the original point cloud, the seabed datum plane is fitted through the reverse distance weighted interpolation, and the high-risk suspended cross area can be rapidly positioned.

The invention discloses a seabed target sonar detection method based on an adversarial network, and the method comprises the steps: S1, collecting deep sea sonar original data, and carrying out the preprocessing of the data, and obtaining a standardized sonar tensor; s2, taking the standardized sonar tensor as training input, and training an improved StyleGAN-3 model in combination with an environmental parameter vector; s3, calling the improved StyleGAN-3 model, and outputting a weak target feature tensor; s4, constructing a sonar detection network; s5, outputting an optimal detection parameter set; and S6, loading the optimal detection parameter set to the sonar detection network, performing target judgment on the weak target feature tensor, and outputting a target confidence value, a three-dimensional space positioning result and a target motion track. Compared with a traditional method, the method has the advantages that in a deep sea scene, the positioning error and the target trajectory loss rate are greatly reduced, the method has excellent online tracking and dynamic situation awareness capabilities, and a solid technical support is provided for ocean monitoring, security and emergency command application.

The invention discloses an energy consumption optimization path planning method for an underwater robot. According to the method, an ocean current model is established by using a Navier-Stokes equation, and complex water flow in the ocean is simulated; a mountain peak model is used for simulating complex submarine topography, and a sphere model is used for simulating random obstacles in the ocean; the outer layer of the path planner adopts a particle swarm optimization algorithm, the inner layer of the path planner adopts a sequential quadratic programming algorithm, and the optimal speed of each section of path can be obtained while path points are obtained; fitting path points by using a B-spline curve to generate a smooth path; and a special fitness function calculation mode is adopted, and a re-planning strategy is combined, so that the calculation efficiency of the algorithm is improved. According to the method, the optimal navigation speed of the robot in different environments can be calculated, the energy consumption of the planned path is reduced on the basis of ensuring the navigation time and safety of the robot, and the efficiency and robustness of path planning are effectively improved through a multi-algorithm combined double-layer path planner and a dynamic re-planning mechanism.

The invention discloses a seabed sonar image processing method based on semantic segmentation. The method comprises the following steps: S1, obtaining preprocessed sonar image blocks; s2, generating a submarine landform boundary priori map corresponding to the preprocessed sonar image blocks pixel by pixel; s3, an improved U-Net double-branch semantic segmentation network is constructed, the improved U-Net double-branch semantic segmentation network comprises a semantic branch and a boundary branch, and a landform mutual feedback gating module is configured in each scale jumper connection layer; s4, obtaining a fusion feature map; s5, deploying to the autonomous underwater vehicle to form an onboard reasoning model; and S6, performing online semantic segmentation reasoning on the preprocessed sonar image block acquired in real time by using the onboard reasoning model, and outputting a semantic category diagram and a boundary vector diagram corresponding to the preprocessed sonar image block. According to the method, the average mIoU index of the model is improved in a small-target and multi-category hybrid scene.

The invention relates to the field of civil engineering structure health monitoring, in particular to a subsea tunnel damage intelligent monitoring and identification method based on acousto-optic collaborative perception. The method sequentially comprises the steps of deploying a three-dimensional acousto-optic sensing network, actively detecting sound waves, reconstructing a local sound velocity distribution cloud picture, identifying a sound velocity abnormal region, performing optical fiber response focusing sampling and establishing an intelligent damage diagnosis model. According to the method, the three-dimensional acoustic array and the distributed optical fiber grid are simultaneously deployed in the subsea tunnel structure, functional complementation is realized between the three-dimensional acoustic array and the distributed optical fiber grid, seamless cooperative sensing of crack coarse positioning and strain fine positioning is further realized, acoustic active detection, trigger type optical fiber high-frequency sampling and a temperature-strain decoupling algorithm are adopted, and the positioning precision of the subsea tunnel structure is improved. Seawater noise and thermal expansion interference are effectively eliminated, 'dry cracks' and'water seepage cracks' are accurately distinguished, real-time monitoring is carried out on a subsea tunnel structure, whole-process identification of micro-damage-expansion-failure is achieved, and an active early warning mechanism is established.

The invention relates to the technical field of earthquake prediction, in particular to a submarine seismic oscillation prediction method and device based on earthquake exploration, and the method comprises the following steps: selecting a monitoring region, collecting the submarine topographic data of the monitoring region, and drawing a topographic map; selecting a plurality of monitoring points according to the topographic map; geomagnetic field information, electric wave information, micro-seismic activity information and seabed deformation information of each monitoring point are collected and recorded; converting the collected information into acoustic signals and transmitting the acoustic signals to a sea surface buoy, and converting the acoustic signals into electric signals by the sea surface buoy and transmitting the electric signals to a ground data center; by optimizing site selection of monitoring points, integrating geomagnetic-electric wave-microseismic-deformation multi-source data, realizing high-efficiency data transmission by using an acoustic-electric staged communication chain, and finally realizing early recognition of earthquake precursor in combination with threshold analysis, the method can significantly improve the coverage range, data precision and early warning timeliness of seafloor earthquake monitoring, and has good application prospects. And a technical support is provided for marine geological disaster prevention and control.

The invention discloses a multi-field sensing fusion deep sea geological environment dynamic monitoring system and an application method. The system comprises a buoy measuring platform, a multi-beam sounding system, a high-precision marine gravimeter, an external camera, a GPS, a data acquisition and transmission control unit and a power supply system. By designing a modularized and integrated multi-sensor buoy platform, various sensors are fused and integrated. A plurality of sensors are connected to the data acquisition control terminal, the steps of sensor calibration, data synchronization and clock calibration, correction and compensation, data acquisition, real-time processing, fine post-processing, data transmission and the like are completed, and multi-sensor collaborative operation is realized, so that submarine topography and ocean gravity data of a deep sea ditch area are synchronously acquired in real time; and thus, diving erosion information experienced by the area can be accurately monitored. The method can provide important support for submarine topography and landform detection and application and marine geographic information system construction, and has application value in the aspects of geological disaster early warning, resource development and the like.

The invention discloses an ocean induced polarization and natural potential combined acquisition device and a data processing method, and solves the technical problem that the sulfide ore form cannot be accurately represented in the prior art. The system comprises a first underwater autonomous robot and a second underwater autonomous robot, the first underwater autonomous robot is provided with an induced polarization emission device, and the second underwater autonomous robot is provided with a natural potential acquisition device; the induced polarization emission device comprises an induced polarization control bin, a first watertight cable, an emission end positive pole and an emission end negative pole, and the natural potential acquisition equipment comprises a natural potential acquisition bin, a second watertight cable and a plurality of pairs of Ag / AgCl non-polarized electrodes. According to the method, the distribution characteristics of submarine sulfides can be more accurately explained, the multiplicity of solutions is reduced, and the exploration accuracy and reliability are improved.

The invention discloses a detection method of an ocean magnetic field sensor based on a diamond NV color center. The detection method comprises the following steps: S1, calibrating a base line; s2, alternately measuring fluorescence between two single quantum resonance frequencies, and obtaining a difference to realize DQ reading; s3, three-axis compensation current is adjusted based on closed-loop control, and the NV working point is maintained at zero magnetization bias; s4, obtaining background magnetic field change through digital demodulation and adaptive filtering; and S5, calculating a gradient based on the spatial difference of the multi-point light spots, judging the existence and trajectory of the underwater vehicle in combination with a threshold value and a spectrum matching method, calibrating the threshold value according to the magnetic field characteristics of the existing underwater vehicle, adopting short-time Fourier transform signal spectrum, comparing the threshold value with known submarine characteristic spectrum data, and prompting if the similarity exceeds a set threshold value. According to the detection method, the measurement precision and the anti-interference capability can be improved, and the method is suitable for real-time monitoring of weak magnetic anomalies (such as magnetic moment disturbance of an underwater vehicle).

The invention belongs to the technical field related to tunnels, and provides a subsea tunnel cross-fault water inrush catastrophe early warning decision-making method and system in order to solve the problems of inaccurate water inrush recognition, lack of interpretability and the like when an existing subsea tunnel passes through a fault zone high-risk scene. Full-process response control from weighting, prediction to explanation of the input features is realized; driving force analysis is carried out by adopting an interpretable model, so that a transparent and credible risk assessment result is provided; and by combining key driving force in the multi-source heterogeneous data and through numerical simulation of the water inrush catastrophe condition, real-time identification and early warning of the tunnel water inrush risk are comprehensively realized, the identification accuracy of the water inrush risk in the tunnel construction process and the completeness of an early warning mechanism are effectively improved, and the method has important engineering application value.

The invention relates to the field of submarine structure detection, in particular to a submarine water intake structure hidden danger detection method based on acousto-optic joint detection. According to the method, through a multi-source data acquisition and intelligent fusion technology, including centimeter-level high-resolution sonar sounding, water body layer structure acoustic imaging, ROV optical camera shooting observation and laser calibration, scouring, crack and blockage information of the seabed grating is comprehensively obtained, and the seabed grating is comprehensively identified by adopting adaptive enhancement, threshold segmentation and spatial interpolation algorithms. The problems that a traditional sonar imaging blind area exists, the target boundary is fuzzy and the biological adhesion thickness is difficult to quantify are solved, and high-precision and multi-dimensional detection of seabed structure damage, blockage and scour forms is achieved. According to the overall scheme, the detection integrity and accuracy are improved, the submarine environment safety monitoring capacity is enhanced, and reliable technical support is provided for operation and maintenance of submarine facilities.

The invention discloses a submarine topography super-resolution reconstruction method based on window displacement multi-source fusion, and aims to solve the problem of precision attenuation caused by absence of multi-beam truth value evaluation and resolution improvement of an existing neural network submarine topography reconstruction method. The method comprises the following steps of: obtaining gravity anomaly, gravity vertical gradient, vertical line deviation component, submarine topography background and ship survey water depth multi-source data of a target sea area; constructing a residual U-Net attention neural network model, and performing training by taking multi-source data extracted by a 11 * 11 window and position codes as input; moving the trained model sampling window in a staggered manner according to a target resolution step length; extracting data points in the window during movement and injecting position codes; and outputting a high-resolution water depth predicted value based on the position code and the window data. The reconstruction precision is improved by fusing physical quantities, the multi-resolution robustness is guaranteed by a window displacement mechanism, and the medium-frequency feature reconstruction capability is enhanced by a residual U-Net attention model.

The invention relates to a large-scale offshore engineering geologic model modeling system and method, and relates to the technical field of offshore engineering geologic investigation and modeling. According to the method, through the steps of multi-source data acquisition, preprocessing, fusion, three-dimensional geological modeling, model optimization, dynamic updating, visual analysis and the like, large-scale geological survey data can be efficiently processed, a high-precision three-dimensional geological model is generated, and dynamic updating and visual analysis are supported. The method has the advantages of high efficiency, high precision, real-time performance, engineering applicability and the like, and is suitable for geological modeling and geological condition analysis of engineering such as offshore wind power, submarine pipelines, ocean platforms and the like.

The invention discloses a submarine searching sonar buoy array position optimization method based on a non-dominated sorting genetic algorithm NSGA-III, and the method comprises the steps: introducing a time scale factor in combination with the initial and maneuvering positions of a submarine, and obtaining a position probability distribution function of the submarine after maneuvering; a buoy position is generated in the whole search area according to probability distribution, more initial array position individuals are generated near a high-probability area, and the exploration tendency of a population to a key area is improved; when reference lines are selected during non-dominated sorting, fusion density estimation is added, so that the reference lines at individual sparse positions are denser; when the Pareto front is selected to be reserved, a reduced-proportion front-edge reservation method is selected, and except for the first front edge, subsequent individual reservation is gradually reduced in a reduced-proportion mode until the population scale is reached; introducing a gene adaptation entropy crossover threshold in the crossover part, adopting a priority retention strategy for the parent with high fitness, and then performing crossover operation on the remaining part; and the potential sonar buoy array position optimization is realized.

The invention relates to a seabed sand wave and pipeline interaction numerical simulation method and system and a readable medium. The method comprises the following steps: selecting a sub-model under an environment dynamic simulation model according to environment data; selecting a sub-model under the sediment transportation simulation model according to the seabed sediment data; whether a fatigue stability simulation model needs to be carried out is selected according to the pipeline type, if the pipeline is a rigid pipeline, the fatigue stability simulation model is not started, and otherwise, the fatigue stability simulation model is started; starting a sub-model of the protection and treatment simulation model; determining a computational domain of a numerical simulation model, adjusting grid boundary conditions through an environment dynamic simulation model, and combining all selected models to form a final numerical simulation model; and inputting to-be-tested material parameters and environmental parameters into the final numerical simulation model to obtain a seabed sand wave and pipeline interaction numerical simulation result. According to the method, a numerical simulation result which better conforms to the dynamic change process of the sand waves in a real environment can be obtained, and the dynamic change of the seabed sand waves can be better understood and predicted.

The invention relates to the technical field of construction of seabed linear target detection models, in particular to a construction method of a self-adaptive semantic segmentation seabed linear target detection model. The method comprises the following steps: obtaining original image data of a seabed scene, extracting a first feature point and a second feature point, and analyzing a structural response difference between the first feature point and the second feature point; calculating a linear rotation angle and a curvature change trend of a connection path based on the structure response difference, constructing a spatial distribution change diagram, extracting a cable continuous trend, and marking a stable section and an abnormal variation section; designing a deep neural network comprising a multi-level convolution structure and a cyclic memory structure, and extracting structure extension and repair features; the depth features of the coherent region and the incoherent region are fused, and a seabed target detection model based on DeepLabv3 + is constructed; semantic segmentation is carried out on the waterfall plot, and the segmentation precision of the model is evaluated to realize optimization; according to the invention, through the construction of the target detection model, the target detection is more accurate and efficient.

The invention provides a method of a seabed mining plume diffusion monitoring device based on an acoustic-optical-electric technology, and multi-dimensional and high-precision monitoring of a deep sea mining plume diffusion process is realized by combining multiple monitoring technologies of acoustics, optics and electricity. The specific target comprises multi-source data fusion: acoustic, optical and electrical data are effectively fused, and the accuracy and comprehensiveness of plume diffusion monitoring are improved. And the concentration conversion precision is improved: through multi-source data calibration, high-precision conversion from acoustic data to particle concentration data is realized. And real-time monitoring capability: providing real-time monitoring and data analysis capability, and reflecting the dynamic change of plume diffusion in time. And environment impact assessment: providing reliable data support for deep-sea mining environment impact assessment, and promoting sustainable development.

The invention relates to the technical field of mobile edge computing, in particular to an ocean communication processing method and system based on mobile edge computing, and the method comprises the following steps: obtaining submarine topography information and node capability parameters, computing node deployment adaptation indexes, extracting signal intensity and angle changes, obtaining a link offset trend, and identifying power and packet loss abnormal regions. Generating path reconstruction parameters, adjusting a network structure in combination with tide level changes, and obtaining a communication task scheduling result according to entity behavior characteristics. According to the method, the adaptability of the node site selection environment is evaluated by analyzing the topographic features and the node capacity, the deviation trend is jointly recognized through signals and angle data, the pre-judgment capacity of link abnormity is improved, the network structure is dynamically adjusted in combination with the tide level change, and the stability of the communication network structure is optimized; and task scheduling grading is driven by adopting communication entity behavior characteristics, so that the accuracy of resource allocation and the regional regulation and control efficiency are improved, and the self-adaptive regulation and control capability in a dynamic environment is realized.

The invention discloses a node trajectory prediction method and device for an air-sky-sea-diving unmanned cluster, and belongs to the technical field of mobile ad hoc networks, and the method comprises the steps: collecting data in real time, and carrying out the preprocessing of the data; constructing an unmanned node trajectory prediction model based on a gating circulation unit, a Transform and a width learning system, and inputting data into a gating circulation unit and Transform parallel cascade module; performing weighted fusion on module output results, and dynamically adjusting the weight by using a back propagation algorithm; and finally, the weighted fusion result and the dynamic environment index are input into a width learning system, the output of the width learning system outputs a final unmanned node trajectory prediction result through a full connection layer, and the width learning system can dynamically adjust the network width according to the data change. Based on the method provided by the invention, the gating circulation unit and the Transform are cascaded in parallel, the combination of real-time response and long-term prediction is realized, a wide learning system is introduced to quickly adapt to data distribution change through incremental expansion, and the environmental adaptability of a prediction model is improved.

The invention provides an intelligent collection method for deep sea polymetallic nodules. The method comprises the following steps: S1, capturing a tuberculosis image through a seabed camera of a mining vehicle; s2, carrying out graying processing on the acquired image; s3, carrying out binarization processing on the preprocessed tuberculosis image; s4, expanding the processed tuberculosis image based on a morphological reconstruction method; s5, determining the particle size and shape of the tuberculosis; s6, comparing with existing tuberculosis data, and determining the quality of the tuberculosis; s8, selecting the optimal advancing route of the mining vehicle, and determining the working condition of the advancing route; s9, further adjusting jet parameters according to the determined information; s10, collecting the tuberculosis washed out by the jet flow; s11, lifting the collected tuberculosis to a collection ship; s12, carrying out collection result detection on the mining area; and S13, if the collection result does not reach the standard, continuing collection, and if the collection result reaches the standard, ending collection. Through the steps, the intelligent and efficient collection system for the deep sea polymetallic nodules is formed.

The invention relates to the technical field of optical signal processing and analysis, and particularly provides a submarine optical fiber vibration event detection method based on credibility weighting, and the method comprises the steps: obtaining an optical pulse echo matrix according to a received signal, and carrying out the decomposition of the signal through a low-rank and sparse decomposition model. A credibility coefficient is introduced to measure the reliability of the data, an optimization model is established, and the credibility coefficient is set to be a tide credibility coefficient or a natural disaster credibility coefficient according to the marine environment. Solving the optimization model to obtain a sparse item estimation matrix corresponding to the sparse item; and detecting a natural disaster event and a vibration event in the received signal according to the sparse term estimation matrix. According to the method, reliability evaluation of the signals can be dynamically adjusted according to different marine environments, noise and real vibration events are effectively distinguished, high robustness is kept in the complex and multi-interference marine environment, the method is not prone to being affected by interference sources, and event detection precision and accuracy are good.

The invention relates to the technical field of submarine oil and gas pipeline leakage risk assessment, in particular to a submarine oil and gas pipeline leakage risk assessment and early warning method, an electronic device and a storage medium. The submarine oil and gas pipeline leakage risk assessment and early warning method comprises the following steps: acquiring first monitoring data through a sensor network deployed on a submarine oil and gas pipeline, determining a leakage section through the first monitoring data, and acquiring second monitoring data through a buoy sensor network deployed on the sea surface; constructing a feature vector and a deep neural network model, inputting the first monitoring data into the deep neural network model, and outputting first simulation leakage data; the method comprises the steps of constructing a pipeline physical model through design parameters and environmental conditions of submarine oil and gas pipelines; based on the first simulation leakage data and a pipeline physical model, second simulation leakage data are obtained through computational fluid dynamics (CFD) simulation; correcting the deep neural network model until the difference between the second monitoring data and the second simulated leakage data is within a set difference value by comparing the difference between the second monitoring data and the second simulated leakage data; constructing an offshore gas disaster analysis model by taking the simulated oil and gas leakage area as a boundary to obtain combustible gas cloud space distribution data; and calculating a comprehensive disaster risk index according to the combustible gas cloud space distribution data, and dividing risk grades according to the comprehensive disaster risk index for early warning. According to the method, the leakage point can be accurately identified, and intelligent risk early warning and evaluation are realized.

To provide an environmental test apparatus capable of performing a long-term deterioration test in which a test sample is exposed for a long period to methane-dissolved water under a low-temperature, high-pressure environment simulating a seafloor environment.SOLUTION: An environmental test apparatus 100 according to an embodiment is an environmental test apparatus for performing a long-term deterioration test in which a test sample is exposed to methane-dissolved water under an environment simulating a seafloor environment where methane hydrate is present and includes: a pressure-resistant vessel 110 which houses the test sample and in which the methane-dissolved water at a temperature and pressure corresponding to the seafloor environment flows through the test sample; a cooling unit 120 that cools and maintains the pressure-resistant vessel 110 at the temperature; and a pressurizing circulation unit 130 that pressurizes the methane-dissolved water flowing out from an outlet 110b of the pressure-resistant vessel 110 and sends it toward an inlet 110a of the pressure-resistant vessel 110.SELECTED DRAWING: Figure 1

The invention discloses an integrated seabed data center structure based on a negative pressure barrel foundation, the integrated seabed data center structure comprises a negative pressure barrel body, a data center cabin is integrally formed on the top surface of the negative pressure barrel body, and a circulating cooling pipe is arranged in the data center cabin. Large-scale construction of the submarine data center can be realized, structural parameters of the negative pressure barrel and the data center cabin can be adjusted according to construction requirements, and a plurality of data cabins can be mounted in the data center cabin, so that the whole data center cabin shares one negative pressure barrel foundation, and the construction cost of the submarine data center is reduced; the negative pressure barrel body and the data center cabin are prefabricated through concrete, compared with steel materials, the economical efficiency is better, the construction cost is reduced, meanwhile, seabed sinking installation of the data center cabin is achieved through the negative pressure principle, large piling equipment is not needed, and the installation speed is high.

The invention relates to a method and system for determining the water content of a safe shutdown boundary of a submarine mixed transportation pipeline, and the method comprises the steps: carrying out a physical property parameter test, and taking the physical property parameter as a multiphase flow simulation input parameter; carrying out submarine pipeline shutdown simulation on the basis of the physical property parameter test data, and obtaining oil section temperatures and spatial distribution characteristics at different water depth positions in the pipeline; carrying out an oil-water layering characteristic experiment under a temperature control condition to obtain an oil-water emulsion layering characteristic; sampling oil sections with different layer heights in the oil-water layering characteristic experiment, and testing the water content and simulating the yield / slip characteristic at the oil section temperature based on the temperature distribution of the oil sections; according to the yield / slip stress of the oil sections with different layer heights, carrying out segmented calculation and weighted accumulation to obtain an overall starting stress; on the basis of the overall starting stress, the safe shutdown boundary moisture content is determined in combination with pressure boundary conditions. The method can effectively guide the safe transportation stopping and restarting of the submarine pipeline, optimizes the replacement operation after the transportation stopping of the submarine pipeline, and improves the operation safety and economy.

The invention relates to the technical field of sonar, in particular to a side-scan sonar seabed line detection method. The method comprises the following steps: firstly, acquiring real-time echo signal data of the left and right sides of the side-scan sonar, then updating detection parameters of the left and right sides according to the echo signal data, and then respectively detecting the off-bottom heights of the left and right sides; judging whether the left and right terrain clearances are symmetrical or not, and correcting error data; and obtaining the ground clearance of the starboard and the starboard of the current frame, smoothing the result, and outputting a submarine line tracking result, and the smoothing method adopts a median filtering method or a Kalman filtering method or a delay filtering method. According to the method, a seabed line result can be accurately detected in real time instead of post-processing based on a side-scan sonar image, the method can be used for wet end grounding early warning, and a basis is provided for side-scan sonar image real-time slope distance correction, image equalization, target real size measurement and sound image real-time inlaying.

The invention discloses a multi-dimensional stealth submarine bionic heterogeneous protective shell and a preparation method thereof, and relates to the technical field of ship and ocean engineering. The submarine bionic heterogeneous protective shell is composed of a sandwich structure and a modified carbon nanotube film; the sandwich structure comprises a bionic Priative multi-stage rotating core layer, a bionic Diamond multi-stage rotating core layer, a bionic octagonal body spiral core layer, a bionic rhombic dodecahedron spiral core layer, a micro-perforated plate and a sound absorption lower plate. According to the design, the Bouligand structure and the acoustic structure principle of the giant bone tongue fish scale are fused, the unique configuration of inter-layer rotation and spiral rising in the microstructure of the giant bone tongue fish scale is used for reference, four bionic structures are innovatively constructed, deformation is effectively restrained, the stability of the overall structure is improved, and through force-sound coupling regulation and control, the mechanical property of the giant bone tongue fish scale is greatly improved. And an innovative solution is provided for achieving the mechanical bearing and sound stealth integrated function of the submarine shell.