1976 results about "Buoy" patented technology

The invention discloses an on-site monitoring system for the performance of an elastic loose type anchorage system suitable for deep-sea buoys, and particularly relates to the field of deep-sea monitoring. The on-site monitoring system auxiliary comprises a central processing module, wherein the output end of the central processing module is separately connected with an information display module,an information storage module, an auxiliary recovery module and a driving module, and the input end of the central processing module is separately connected with a power supply module, a monitoring module, a correction module and a positioning module. According to the on-site monitoring system for the performance of the elastic loose type anchorage system suitable for the deep-sea buoys, the auxiliary recovery module is arranged, air bags are installed at a plurality of node positions on the anchorage system, a booster pump in a pressurization unit is used, outside air is conveyed into a floating ball unit through a pipeline unit, so that the retractable air bags are enabled to bulge upwards to float, the whole anchor system is directly floated in sections, people conveniently recycle theanchorage system greatly, the workload during the recycling process is reduced, and the practicability is high.

The invention relates to the technical field of ecological environment monitoring, and discloses a coastal wetland intelligent monitoring method and system based on artificial intelligence, and the method comprises the steps: collecting unmanned plane data, satellite remote sensing data, Internet of Things sensor data and water quality monitoring buoy data of a coastal wetland; the method comprises the following steps: processing satellite remote sensing data by adopting a wavelet threshold denoising algorithm based on an attention mechanism, calibrating Internet of Things sensor data by adopting an LSTM network, and carrying out data space-time alignment based on a space-time attention fusion model to obtain preprocessed data; inputting the preprocessed data into a Transform-ResNet hybrid model to carry out environmental change evaluation, and outputting an ecological health index; when the predicted ecological health index is lower than a threshold value, a PPO algorithm is adopted to dynamically adjust a monitoring strategy according to the early warning level, and an early warning report is pushed; the whole process is intelligent, manual intervention is greatly reduced, and support is provided for coastal wetland ecological protection.

The invention discloses an offshore red tide multi-parameter real-time monitoring method and monitoring system based on a buoy array. The method comprises the following steps: S1, performing array deployment and topology configuration according to a sea area sensitivity degree, tide characteristics and a historical red tide trajectory; according to the invention, through risk differentiation buoy array deployment and redundancy topology, traditional isolated monitoring limitation is solved, and sea area full coverage and red tide diffusion tracking are realized; physical-chemical-biological multi-source sensing and in-situ detection are combined with data fusion, so that the problem of single parameter is solved, and the monitoring accuracy is improved; by means of liftable stratified sampling and in-situ algae species and toxicity analysis, the vertical monitoring blank is filled up, and the complete life cycle process of red tide from occurrence to regression is completely captured; through linkage of edge-cloud multi-model early warning and field equipment, a'monitoring-early warning-disposal 'closed loop is formed, and the red tide monitoring real-time performance and the emergency response capability are greatly improved.

The invention belongs to the cross technical field of artificial intelligence and marine meteorological prediction, and discloses a sea wave probability prediction method and system, and the method comprises the steps: obtaining historical wind field data and sea wave spectrum data of a target sea area, carrying out the preprocessing and organization of the data, and constructing a training data set; constructing a hybrid expert probability model, wherein the model comprises a gating network and a plurality of expert networks; the training data set is used for training the hybrid expert probability model, the trained model receives input wind field data, and hybrid probability distribution is output through the synergistic effect of the gating network and the expert network; and sampling is carried out from the mixed probability distribution to obtain a predicted sea wave spectrum set, and sea wave risk probability prediction is realized. Complete distribution information can be obtained through one-time forward calculation, a numerical mode set is not needed, the computing power and energy consumption expenditure are remarkably reduced, and high-frequency updating and quasi-real-time business application can be conveniently achieved in resource-limited environments such as shipborne, buoys and offshore stations.

A method for simultaneous localization and mapping underwater. A robot is equipped with an IMU inertial unit and a sonar unit. When the robot submerges underwater, a buoy connected to the robot floats on the water surface, and the buoy moves in coordination with the movement of the robot. The position and observed velocity of the buoy are obtained by a shore-based lidar. During motion estimation in a SLAM algorithm, when an angular velocity is below a preset angular velocity threshold, the observed velocity of the buoy is decomposed into x-axis velocity and y-axis velocity, and updated as the two-dimensional operating velocity of the robot. The polar coordinates of an obstacle in a map under the coordinate system of the robot are associated with the polar coordinates of sonar data of a current frame by using the Mahalanobis distance. The polar coordinates of a new obstacle are converted to the world coordinate system and added to the map. The present invention has the advantages of not relying on underwater visibility, not requiring pre-installed devices, and possessing good generalization and stability.

The invention relates to the field of ocean data buoy data quality control, and discloses a buoy meteorological data restoration method based on a space-time double-attention neural network, and the method comprises the following steps: data collection and preprocessing; establishing a space-time double-attention neural network model, wherein the model comprises a time coding link, a space coding link and a feature fusion layer; model training: inputting the time features containing the target elements to be predicted into a time coding link, inputting the meteorological elements into a space coding link, and outputting the target elements at the moment to be predicted by the model; after model training, evaluating the model by using the test set; and predicting a vacancy value or an abnormal value in the multi-dimensional meteorological element data acquired in real time by using the model which is qualified in evaluation, so as to realize data restoration. According to the method disclosed by the invention, time and space constraints are effectively combined, and abnormal values and vacant values in the meteorological data are predicted and replaced, so that the meteorological data are repaired.

The invention discloses a semi-submersible wave energy-wind energy integrated power generation platform and a damping regulation and control method of a damping regulation and control system of the semi-submersible wave energy-wind energy integrated power generation platform. The platform comprises a semi-submersible wind driven generator platform, a wind driven generator set, a wave energy power generation device, an energy storage system, a sea area detection system and the damping regulation and control system. The wave energy power generation devices are divided into two groups on the inner side and the outer side of the platform, each wave energy power generation device comprises a platform inner side wave energy power generation device and a platform outer side wave energy power generation device, and each wave energy power generation device comprises a swing arm floater, a universal hinge, a connecting arm and a damping adjusting module. The semi-submersible wind power generation device and the wave power generation device are combined, and offshore renewable resources are comprehensively utilized; the six-buoy design is adopted, buoyancy is dispersed, stability and safety are improved, and meanwhile space is provided for a wave power generation device; the floaters are symmetrically arranged in a hexagonal shape and carry a damping regulation and control system, and the whole structure is more easily stabilized by adjusting the damping of the floaters in all directions.

A Beidou / GNSS-based real-time high-precision sea surface measurement method and a buoy, wherein by using free-to-use broadcast ephemeris freely broadcast by GNSS satellites, precise point positioning of China's Beidou satellites, and other high-precision service space signals, real-time centimeter-level element information comprising time, longitude, latitude, water level, wave height, wave period, wave direction, sea surface current velocity and direction, atmospheric water vapor content, and the like can be directly obtained. The information can be stored locally in a buoy or transmitted back by communication, and is suitable for real-time high-precision sea surface measurements in offshore and far-sea scenarios.

The invention provides a multi-source data fused SAR sea surface significant wave height deep learning inversion method, and belongs to the technical field of remote sensing ocean, and the method specifically comprises the steps: preprocessing buoy data, and obtaining sea wave significant wave height data observed by a buoy; acquiring dual-polarization single-view complex SAR data collected in an interference wide-width mode; preprocessing the SAR data, and taking significant wave height data obtained by buoy observation as a label of the SAR data; acquiring auxiliary data corresponding to the SAR data, wherein the auxiliary data comprises wind speed, wind direction data, rainfall data and OCN data; and constructing a multi-source data-fused SAR significant wave high-depth learning inversion model, training the multi-source data-fused SAR significant wave high-depth learning inversion model, and testing and verifying the trained model by using the test set and the verification set to obtain a final significant wave height inversion result. According to the method, the SAR data inversion capability of the model is improved.

The invention discloses a multi-mode water-air-land cross-medium cooperative sensing system and method based on an MEMS (Micro Electro Mechanical System). The system comprises an underwater robot, a buoy station, an unmanned aerial vehicle and a ground control station, each node is provided with an MEMS sensor integrating acoustics, optics, magnetism, inertia and other modes, and data transmission and relay are achieved through acoustics, optical communication and radio links. And the ground control station carries out fusion processing, target identification and decision control on multi-source data through time synchronization and space registration, and issues a cooperative instruction to each node to realize a perception-decision-re-perception closed loop. The method comprises five steps of node deployment and calibration, multi-modal data acquisition, hierarchical transmission and sharing, adaptive weighted fusion and closed-loop cooperative control. According to the invention, global coverage, high-precision and multi-mode environment and target perception can be provided in a complex water-air-land environment, and the detection reliability and the communication efficiency are significantly improved.

The invention provides an ocean live big data fusion method based on deep learning, and relates to the technical field of ocean big data fusion, and the method comprises the steps: obtaining meteorological driving variables, ocean historical reanalysis data, satellite observation data and ocean field observation data, and performing unified format conversion processing on the basis of the acquired meteorological driving variables, the ocean historical reanalysis data, the satellite observation data and the ocean field observation data, and configuring a unified ocean environment feature tensor set. A unified marine environment feature tensor structure is configured, meteorological driving variables, marine historical reanalysis data, satellite remote sensing data and on-site buoy observation data are subjected to space resampling, time interpolation and missing complementation and are unified into a space-time grid under a geographic reference coordinate system, a four-dimensional tensor set is configured, and the four-dimensional tensor set is obtained. The problem of fusion obstacles caused by non-uniform data formats and inconsistent temporal-spatial resolutions in the prior art is effectively solved.

The invention discloses a method for aggregating ocean buoy data, which comprises the following steps of: establishing a space-time correlation model according to the spatial distribution density of buoy clusters and the dynamic characteristics of an ocean environment, and adaptively dividing buoy groups with a data compression cooperative relationship; performing a data-related compression operation within the buoy group; constructing a transmission priority dynamic sorting engine, and calculating a dynamic weight coefficient; performing hierarchical packaging processing on the differential coding data, distributing enhanced forward error correction coding resources for high-priority data, and reserving redundant transmission time slots; the method comprises the following steps: receiving a hierarchical data packet through a low-orbit satellite and executing on-satellite preprocessing, reconstructing an original data stream by using a distributed decoder, and meanwhile, fusing multi-dimensional space-time related data to generate an aggregated data product; and finally, the aggregated data product is transmitted to a ground data center through a satellite downlink. According to the invention, uplink data volume is reduced, disaster monitoring timeliness is improved, ground station processing load is reduced, and system environment adaptability is enhanced.

The invention provides a sea area digital twinning method and system based on buoy and station data fusion, belongs to the related technical field of sea area digital twinning, and establishes a data quality evaluation mechanism and a similarity data compression mechanism by constructing a multi-source heterogeneous ocean data acquisition system. Intelligent fusion processing of buoy data and station data is achieved, and the problem that in the prior art, the data fusion precision is low is solved; through the self-organizing growth network structure of the ocean dynamic response enhancement model, neuron connection can be automatically adjusted according to the spatial-temporal variation degree of training data, an optimal topological structure is formed, and the defect that a traditional fixed parameter model cannot adaptively process a complex ocean environment is effectively overcome; by establishing a dynamic parameter adjustment mechanism and a multi-algorithm adaptive switching strategy, the most suitable prediction algorithm is automatically selected according to the correlation characteristics of the ocean data sequence, and high-precision digital reproduction of the complex ocean environment is realized.

The invention discloses a semi-submersible fan-oscillating water column wave energy device integrated device with heaving plates. The semi-submersible fan-oscillating water column wave energy device integrated device comprises floating fan equipment, a central platform, buoys, hydraulic heaving plate connecting rods, an upper-layer permeable heaving plate, a lower-layer non-permeable heaving plate, a control system, a wind speed monitoring sensor and a laser wave surface displacement sensor. The floating fan equipment is arranged at the upper part of the central platform; the buoy is formed by connecting a permeable protective cover, a hydraulic connecting rod, an OWC device, an air pressure enhancing device, an internal supporting rod, a telescopic supporting plate and a pneumatic turbine. The upper-layer permeable heaving plate and the lower-layer non-permeable heaving plate are connected to the bottom of the buoy through the hydraulic heaving plate connecting rod; the control system is positioned in the lower horizontal plate of the central platform; the wind speed monitoring sensor and the laser wave surface displacement sensor are arranged on a stand column of fan equipment. The fan operation efficiency and the system reliability are improved, the whole life cycle cost is effectively reduced, and good durability and maintainability are achieved.

The invention provides a buoy ship collision early warning method based on artificial intelligence, a medium and a system, and belongs to the technical field of buoy ship collision early warning. A ship trajectory prediction model is utilized to dynamically adjust neuron time constant parameters through an adaptive adjustment function to output a ship future position prediction sequence, and the prediction sequence is input into a greedy snake path verification algorithm to carry out gridding processing and continuity inspection to delete abnormal path segments. Based on the verified prediction sequence, calculating a collision probability value comprehensively considering a distance parameter relative speed parameter course angle difference value and a sea condition complexity coefficient, determining a collision risk level according to the collision probability value and a preset threshold value, and calculating estimated collision time; and the collision early warning information including the risk level estimation time and the emergency degree is output, so that the technical problems of low accuracy and poor timeliness of collision early warning of ships in a sea area are solved.

The invention discloses a fish school photoacoustic feature recognition method based on a multi-modal deep network, and the method comprises the steps: 1, obtaining data: obtaining acoustic data and visual data; 2, acoustic data preprocessing and feature engineering; 3, visual data preprocessing and labeling alignment are carried out; 4, constructing a sample and organizing a data set; step 5, designing a multi-mode branch network; step 6, carrying out cross-modal fusion and joint representation; step 7, performing multi-task output and a loss function; and step 8, model training and evaluation. According to the invention, by fusing acoustic, optical and environmental sensor data, an acoustic-vision-environment three-branch feature extraction network is constructed, adaptive fusion of multi-modal features is realized, the problems of low fish school recognition precision, difficulty in alignment and fusion of multi-modal data and the like in a complex marine environment are effectively solved, and the accuracy of fish school recognition is improved. The method is suitable for real-time fish school monitoring and analysis tasks of unmanned ships, buoys and fishery administration monitoring platforms.

The invention relates to the technical field of perception fusion, and discloses a tug fusion enhanced perception method based on target tracking and obstacle recognition, which comprises the following steps: processing an AIS, an optical fiber compass, a Beidou RTK, a laser radar, a millimeter wave radar and a panoramic camera on a tug to establish a ship coordinate system; and collecting message data of the AIS and combining with the MMSI information to screen the target ship to obtain the initial position of the target ship. And predicting the state sequence of the target ship to obtain the position, speed and heading information of the target ship. And carrying out target identification on the image, outputting identification results of semantic levels of the ship, the buoy and the quay wall, and carrying out space projection alignment with radar data. And fusing the processed results to obtain a fusion result, and performing early warning according to the fusion result. According to the method, the target ship relative pose resolving precision and the obstacle recognition reliability are improved, the sensing link time delay is shortened, and the driver blind area risk is reduced.

The invention discloses a composite material float bowl and a multifunctional floating mat structure, the composite material float bowl comprises a float bowl body formed by a fiber reinforced material, the float bowl body is of a cylindrical structure extending in the horizontal direction, and a float bowl cavity of a sealed cavity structure is formed in the float bowl body; a mounting plane of a plane structure is formed at the top of the buoy body, the mounting plane extends to the two ends of the buoy body in the length direction, and the two sides, in the width direction of the buoy body, of the mounting plane protrude to form mounting flanges. Due to the fact that the whole top face of the buoy body is the installation plane of a plane structure, assembling connection with an upper structure is facilitated, all the installation planes and the installation turnups can support the upper structure, a huge supporting area is provided for the upper structure, the bearing capacity and the bearing stability of the multifunctional floating mat structure are improved, and the service life of the multifunctional floating mat structure is prolonged. And the universality is improved. The multifunctional floating mat structure can be used as various water platforms and is good in universality, high in structural strength and good in weather resistance.

The invention relates to a profile drifting buoy state monitoring method, which belongs to the technical field of state monitoring, and comprises the following steps: constructing a digital twinborn model of a profile drifting buoy, and updating parameters of the digital twinborn model according to a preset fault type to obtain fault simulation data; segmenting the fault simulation data according to a preset sliding window, and extracting multi-dimensional statistical features; inputting the multi-dimensional statistical features into a multi-layer perceptron for calculation to obtain feature scores, converting the feature scores into probability values through a discrete distribution sampling layer, and screening the probability values through binarization coding according to a preset threshold to obtain screened feature vectors; and inputting the screened feature vectors into the gradient elevator for state monitoring to obtain a state monitoring result, performing adaptive feature screening by using a multi-layer perceptron to avoid limitation of manual feature selection, realizing state monitoring through the gradient elevator, and combining feature selection and classification advantages to obtain a state monitoring result. And the monitoring result has high precision and interpretability.

The invention relates to the technical field of bridge pier scour monitoring, discloses an air-water-shore cooperative overwater float type bridge pier scour monitoring system, and belongs to the technical field of bridge safety monitoring. According to the system, an integrated framework is formed by a buoy platform, a shore-based cooperative monitoring station and a cloud monitoring platform. The buoy platform is fixed to a pier through a mooring lead, the underwater sonar module of the buoy platform can scan the terrain of a riverbed at multiple angles, and the overwater module integrates high-precision positioning and video monitoring. The shore-based station is provided with a laser scanner and a high-definition camera to monitor the overwater structure and the macroscopic environment of the pier. The cloud platform serves as a control center, converges the multi-source data of the buoys and the shore stations through a communication network, carries out time sequence correlation and fusion analysis, and dynamically evaluates the scour pit development and the bridge pier structure state. According to the invention, the limitation of a single monitoring mode is broken through, collaborative acquisition and intelligent fusion of air, water and shore data are realized, the comprehensiveness, accuracy and early warning capability of monitoring are remarkably improved, and reliable guarantee is provided for safe operation of bridges.

The invention provides a method for cooperatively regulating and controlling chlorophyll-a concentration variability through vortex-upwelling, and relates to the technical field of ocean remote sensing and environmental monitoring. A multi-source satellite and reanalysis data set is obtained, and a stationary time sequence is constructed; identifying a dominant variation period in chlorophyll-a concentration, sea surface temperature and sea surface height anomalies, and extracting a monthly abnormal value sequence; performing complex empirical orthogonal function analysis on the sea surface height anomaly, extracting a dominant spatial variation mode of the sea surface height anomaly, and identifying a vortex distribution position and form; classifying and screening target vortexes according to vortex types and seasons, carrying out normalization processing on the chlorophyll-a concentration, aligning to vortex centers, and analyzing a spatial response relationship between the vortexes and the chlorophyll-a concentration; and drawing a density time-depth profile map and a CHL-SLA composite map by combining surface chlorophyll-a concentration, sea surface height anomaly and vertical Argo buoy data, and analyzing an influence mechanism of vortex-upwelling on chlorophyll-a horizontal distribution.

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 energy management method based on the automatic cleaning system of the buoy ship for the river comprises the steps that the surface dirt area and dirt thickness of the buoy ship are collected and input into a pollution degree evaluation model, and the cleanliness level is judged; constructing a relation model of the cleanliness grade, the water pump power and the nozzle spraying angle; based on the output power and the operation time of the electric cleaning machine of the buoy ship under different cleaning degree grades, the corresponding relation between the actual cleaning degree grade and the output power and the operation time of the electric cleaning machine of the buoy ship is obtained, and a relation model between the cleaning degree grade and the output power and the operation time of the electric cleaning machine of the buoy ship is constructed; taking the minimum total energy consumption of the water pump and the electric cleaning machine as a target, solving a water pump output power model by using a differential evolution algorithm, updating the output power of the electric cleaning machine in real time, and obtaining an optimal energy-saving scheme of the automatic cleaning system of the buoy ship for the river. On the premise of guaranteeing the cleaning effect, the method reduces the overall power consumption of the system to the maximum extent, improves the energy utilization efficiency and prolongs the service life of equipment.

The invention provides an overwater platform remote operation and maintenance method and system based on time sequence loads, and the method comprises the steps: dividing a buoy stress region based on structural characteristics and working condition simulation, and constructing a sensor layout set and load mapping model; acquiring load signals of the sensor arrangement set in real time, and outputting a filtered load signal set; estimating a regional total load based on the filtered load signal set, and constructing a spatial load matrix; an overload early warning threshold value is set based on the platform bearing capacity and the real-time load, and a load state vector is obtained; dividing risk levels based on time sequence evolution and fatigue prediction of a spatial load matrix; and evaluating platform safety based on the load state vector and the accumulated fatigue risk index, and visualizing a load thermodynamic diagram, a fatigue risk distribution diagram and a comprehensive analysis trend chart. Structural simulation and sequential load evolution analysis are combined, the dynamic coupling relation between the platform and the environment is constructed, and the method is suitable for remote monitoring, operation and maintenance of the water platform.

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.

An Archimedes ocean energy harvesting device, comprising a floating body (1) and a thin-plate contact-separation TENG (2). The floating body (1) is mounted below a water surface and is capable of moving with wave fluctuations, thereby driving the thin-plate contact-separation TENG (2) loaded inside the floating body (1) to generate electricity. The floating body (1) comprises a silo (11), a buoy (12), and an elastic annular waterproof belt (13) connecting the silo (11) and the buoy (12). The silo (11) is inserted upwards into the buoy (12), and the waterproof belt (13) connects the buoy (12) and the silo (11) to form a closed cylindrical chamber. The thin-plate contact-separation TENG (2) is mounted in the cylindrical chamber, and comprises a fixing mechanism (24), a connecting rod (23), a plurality of circular friction discs (21) and a plurality of electrode discs (22), wherein the friction discs (21) and the electrode discs (22) are alternately arranged in parallel along the axial direction of the cylindrical chamber, and the friction discs (21) and the electrode discs (22) are respectively fixed on the buoy (12) and the silo (11).

The invention provides an intelligent target supervision method and system based on data analysis. The method comprises the steps that visible light and thermal infrared image sequences are synchronously collected through a coastline monitoring camera, and plastic garbage contour features and temperature distribution differences are recognized; segmenting the contour, extracting displacement to generate an ocean current trajectory prediction path, arranging a buoy collection device, and driving surface water flow to converge by using a propeller to guide garbage to move along the path to form a dynamic accumulation area; plastic garbage difficult to decompose is recognized and marked based on visible light reflection difference, offset is calculated in combination with temperature difference to generate a path correction instruction, the track of a collecting device is adjusted to preferentially intercept a target, and real-time images are synchronized to update closed-loop supervision. According to the technical scheme, intelligent recognition, dynamic gathering and precise interception closed-loop supervision of marine plastic garbage difficult to decompose are achieved, the pollution treatment efficiency is improved, and secondary diffusion is restrained.

The invention discloses a marine ecological environment problem identification method based on multi-source data, and relates to the technical field of marine ecological evaluation, marine ecological environment data is collected from a plurality of data sources of a research area, and is preprocessed to form a marine environment data set; constructing a multi-level ecological environment partitioning system based on the marine environment data set, wherein the multi-level ecological environment partitioning system comprises preliminary partitioning and refined partitioning; and determining the boundary of each hierarchical partition by utilizing a GIS (Geographic Information System) technology and combining ocean topography and landform, hydrodynamic characteristics and ecological function differences. By integrating satellite remote sensing, ocean buoy, subsurface buoy observation, ship investigation, port and coastal monitoring stations and other multi-source data, various aspects of the ocean ecological environment can be comprehensively covered, rich information sources and multi-dimensional and multi-level data sets are provided, information insufficiency or deviation possibly existing in a single data source is effectively made up, and the method has the advantages of being simple in structure, convenient to operate and high in practicability. And the precision and comprehensiveness of marine ecological environment problem identification are obviously improved.

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 relates to the technical field of ocean unmanned vehicles, and particularly discloses a wave glider laying and recovery system and a collaborative operation method thereof.The wave glider laying and recovery system is composed of a transferring laying device, a tail buoy, a flat cable recovery device and a salvage recovery rod; the moving, transporting and laying device adopts a modularized quick-release structure and a positive and negative thread screw collinear self-adjusting positioning mechanism and is matched with a hand-pulled spring shackle to achieve five-minute zero-assembly laying, and a tail buoy achieves floating body electric control separation through a waterproof steering engine driving lead screw nut transmission and a guide column-guide sleeve conical self-centering structure. The flat cable recovery device is designed based on an eccentric cam self-locking principle and a progressive clamping mechanism, and is combined with a three-stage hoisting strategy to complete efficient and lossless recovery of the flexible flat cable of 7-8 meters; the invention further discloses a collaborative operation method of the wave glider laying and recovery system, which comprises the processes of moving and positioning, device separation, hand pulling and releasing and underwater unfolding in a laying stage, and a time sequence linkage strategy of buoy separation, positioning and dragging and graded hoisting in a recovery stage.