3859 results about "Ocean environment" patented technology

The invention relates to the technical field of data processing, and discloses a multi-source heterogeneous marine data intelligent fusion and marine disaster prediction method and platform, and the method comprises the steps: carrying out the hierarchical deployment and network topology construction of marine environment monitoring equipment, communication relay equipment and data processing equipment, and obtaining a sea-air-shore integrated monitoring network; collecting marine environment data with a space-time identifier; carrying out ocean element modeling and dynamic feature extraction to obtain an ocean environment digital model; performing feature fusion and correlation analysis on the multi-source heterogeneous ocean data to obtain spatio-temporal feature data; and training and multi-model integration are carried out on the typhoon path prediction model, the storm surge prediction model and the red tide prediction model by adopting a transfer learning method to obtain a marine disaster prediction model set, so that the prediction precision and timeliness are improved, the allocation and scheduling of computing resources are optimized, and the real-time requirement of mass data processing is met.

The invention relates to the technical field of marine environment monitoring, in particular to a time-space diagram neural network anomaly detection method and system for marine ecology. The method comprises the following steps: acquiring ocean monitoring data; performing data preprocessing on the acquired marine monitoring data; constructing a space-time diagram structure based on the preprocessed marine monitoring data; performing spatio-temporal feature extraction on the constructed spatio-temporal diagram structure by using an anomaly detection model; carrying out feature fusion on the extracted spatial-temporal features; and carrying out local anomaly and global anomaly scoring on the marine ecosystem based on a multi-scale anomaly scoring mechanism. According to the method, a unified data processing framework is designed, multi-source heterogeneous data such as satellite remote sensing data, water quality monitoring data and biological survey data can be processed at the same time, information contained in various kinds of data is fully utilized, and the comprehensiveness and accuracy of anomaly detection are improved. Through the construction of a space-time diagram structure, the incidence relation between different data sources is effectively captured, and more comprehensive feature expression is provided for anomaly detection.

The invention relates to the technical field of marine environment prediction, in particular to a marine environment multi-modal fusion prediction method and system based on digital twinning. The method comprises the following steps: capturing time-space correlation characteristics of multi-modal data of a marine environment based on a dynamic multi-modal graph neural network; performing multi-scale feature fusion on the space-time correlation features by using a multi-scale gating unit to obtain comprehensive feature representation; predicting the comprehensive feature representation by using a mixed time sequence prediction framework to obtain preliminary marine environment prediction data, including short-term dynamic modeling and long-term trend modeling; and performing noise fitting on the preliminary marine environment prediction data by using a generative adversarial network to generate final marine environment prediction data. According to the invention, through innovative combination of dynamic multi-modal data fusion, a mixed time sequence prediction framework and a multi-scale gating Tanh unit, the problems of insufficient data fusion, limited prediction precision and poor dynamic response capability in the traditional marine environment monitoring and prediction technology are effectively solved.

The invention relates to the technical field of environment management, in particular to a marine environment risk assessment method and system based on multi-source data fusion, and the method comprises the following steps: carrying out the assessment based on the multi-source marine environment monitoring data through the data integrity, consistency and timeliness of a plurality of data sources, and calculating efficiency scores of the plurality of data sources and adjusting the contribution proportion of the target data source to generate a multi-source data fusion model. According to the method, by analyzing the efficiency of multiple data sources, adjustment of data source contribution is achieved, dynamic adaptability and flexibility of data fusion are optimized, the accuracy and timeliness of marine environment risk assessment are improved, and detection and recognition of abnormal data of multiple environment indexes are achieved by combining analysis of water quality related indexes and temperature data; through analysis of marine development activity events, a decision basis is provided for adjustment of marine management policies, and accurate assessment of marine environment risks is realized in combination with prediction of marine biological diversity.

The invention relates to the technical field of wind turbine generator data analysis, and discloses an offshore wind turbine generator operation data analysis and prediction system. The system comprises a marine environment data integration module for collecting data to generate a multi-source time-space synchronization data set; the multi-modal feature fusion module is used for extracting cross-modal correlation features to generate a high-dimensional fusion feature tensor; the dynamic fault prediction module is used for constructing a two-way gating circulation network model to predict the degradation probability and the residual life of key components of the equipment; and the self-adaptive optimization control module is used for constructing a multi-target dynamic programming model to optimize a fan operation strategy. In addition, the system is further provided with a feedback correction module for correcting prediction model parameters, and a virtual sensor module based on a physical information neural network is used for monitoring tower stress and diagnosing sensor faults. According to the system, comprehensive monitoring, accurate fault prediction and optimal control of the offshore wind turbine generator are realized, the operation efficiency, reliability and safety of the wind turbine generator are effectively improved, and the operation and maintenance cost is reduced.

The invention discloses a marine environment dynamic monitoring system based on cooperation of artificial intelligence and multi-source remote sensing, relates to the technical field of marine environment monitoring, and realizes multi-dimensional monitoring of a sea surface state by receiving multi-source data of a plurality of monitoring ends. Chlorophyll concentration mutation is detected in real time by setting sliding window difference, a subsequent processing flow is dynamically triggered, and the response speed of sudden events (such as red tide) is increased; aI is utilized to predict clock offset, interpolation reconstruction, weighted fusion and ocean current compensation technologies are combined, and space-time asynchronous errors of multi-source data are remarkably reduced; and generating a data cube fusing the backscattering coefficient, the temperature field and the chlorophyll gradient, and calculating a diffusion velocity field, thereby providing high-precision decision support for marine disaster early warning. The limitation of a traditional single data source is broken through, efficient cooperation of multi-modal data is driven through artificial intelligence, and the real-time performance, the accuracy and the dynamic analysis capability of marine environment monitoring are improved.

The invention relates to the technical field of marine anomaly detection, in particular to a marine ecological anomaly early warning method and system based on multimode sensing and spatio-temporal reasoning. Comprising the following steps: acquiring original multi-modal data including image data and time sequence data; performing global context coding on the acquired image data, performing dynamic evolution coding on the time series data, and performing multi-modal feature alignment through feature normalization; carrying out feature fusion on the multi-modal features based on a dynamic ecological knowledge graph fusion mechanism; predicting fusion features based on space-time atlas changes; and performing attribution and diagnosis based on a prediction result. According to the method, the model can automatically amplify key signals and suppress irrelevant noise, and the capturing capability of early weak abnormal signals is greatly improved in a complex marine environment.

The invention relates to the technical field of communication, in particular to a low-delay data transmission system and method for marine observation data, and the system comprises a data collection module which collects the multi-modal data of a marine environment, and carries out the priority classification of the data according to the real-time performance and importance; the route optimization module dynamically adjusts a multi-path fragmentation strategy and single-path task distribution in combination with a reinforcement learning algorithm and path optimization data; the communication module supports multi-mode communication modes such as laser communication and low-orbit satellite communication, and monitors the link state in real time; and the data fusion and analysis module performs dimension reduction, fusion and real-time analysis on the data by using manifold learning and a graph neural network, and meanwhile, predicts a long-term trend through time sequence analysis to generate observation report data. The method effectively reduces the transmission delay of high-priority data, improves the response efficiency of the system to abnormal events, and is suitable for the scenes of marine disaster early warning, environment monitoring and the like.

Provided is an in-situ high-precision non-destructive testing device for deep-sea parts, the device comprising a bottom plate (1), wherein a linkage base (5) is fixedly mounted on a side surface of the bottom plate (1), a first bearing (6) is provided on the linkage base (5), a rotary rotating shaft (7) is fixedly mounted on the inner side wall of the first bearing (6), the rotary rotating shaft (7) is connected to a driving bevel gear (8), the driving bevel gear (8) meshes with a driven bevel gear (9), a driving threaded shaft (10) is mounted on the driven bevel gear (9), and the end of the driving threaded shaft (10) close to the top surface of a side plate (2) is in transmission connection with the top surface of the bottom plate (1). The driving threaded shaft (10) is rotated to enable a lifting square block (11) in threaded connection with the driving threaded shaft (10) to move upwards, such that a non-destructive testing box is in an inclined position; in addition, by means of a stabilizing assembly, a laser scanner (66) can stably perform a scanning testing operation on parts, thereby avoiding errors in testing results, and improving the accuracy of the testing results. Moreover, the device can simulate a typical marine environment to perform standard testing on deep-sea parts, and can establish a surface damage database for comparison with deep-sea in-situ testing results, thus further improving the reliability of testing results in different marine environments.

The invention relates to the technical field of wireless communication, discloses an air-sea cross-domain network reinforcement learning routing algorithm based on an SDN (Software Defined Network), and mainly aims to solve the problems of poor dynamic environment adaptability, insufficient multi-task QoS (Quality of Service) demand guarantee capability and the like caused by a static rule or local optimization of an existing air-sea cross-domain routing algorithm. Global network state perception and resource centralized scheduling are realized through an SDN controller, and a dynamic routing decision model driven by deep reinforcement learning (DRL) is constructed. A network state is sensed in real time through an SDN controller, a multi-target reward function is designed, and a cross-domain path selection strategy is dynamically optimized. According to the method, the limitation of a traditional routing protocol in a cross-domain heterogeneous network is broken through, the end-to-end transmission efficiency, the resource utilization rate and the multi-task differentiated service quality guarantee capability are remarkably improved, and the method can be widely applied to the scenes of marine environment monitoring, emergency rescue, cross-domain cooperative detection and the like.

The invention belongs to the technical field of marine environment prediction, and discloses a ship navigation sea wave dynamic space-time prediction method and system based on deep learning. The method comprises the following steps: carrying out space-time alignment, missing value repair and standardization processing on acquired ship AIS data and an ERA5 reanalysis data set, and generating node feature vectors containing latitudes and longitudes, timestamps, wind speeds and significant wave heights; through node feature coding, centrality coding, space coding and time coding, ship trajectory node importance and time-space interaction relation are quantified. Constructing a SeaGraph model, and outputting an effective wave height prediction value of a target waypoint; and performing verification. According to the method, the space-time constraint of a traditional static modeling framework is broken through, the advantages of a self-attention mechanism and a graph network are integrated, the predictive modeling capability of dynamic evolution of a wave field in front of a ship navigation track is enhanced, and high-precision sea wave forecasting support is provided for intelligent ship navigation under complex sea conditions.

The invention discloses an unmanned ship multi-agent collaborative obstacle avoidance method and system for complex sea conditions, and relates to the field of unmanned ship multi-agent collaborative obstacle avoidance, and the method comprises the steps: obtaining the real-time data of each unmanned ship and the surrounding environment; generating a candidate obstacle target point cloud cluster list based on a density clustering algorithm; on the basis of a Kalman filter, real-time absolute motion state estimation of the candidate obstacles is obtained, and an obstacle feature list is output; determining a safety radius compensation amount required by autonomous obstacle avoidance of each unmanned ship; obtaining the safe sailing space of each unmanned ship at the current moment; constructing a global synthetic potential field, and generating a group of optimal alternative paths of the unmanned ship from the current position to the target point; on the basis of adopting a consensus binding algorithm, an unmanned ship multi-agent collaborative obstacle avoidance path reaching a consensus is obtained. The method has the advantages that safe, efficient, cooperative and consistent intelligent obstacle avoidance of a multi-unmanned-ship cluster in a real complex marine environment is realized.

The invention provides an unmanned ship dynamic path planning method and system based on deep reinforcement learning, and relates to the technical field of path planning, and the method comprises the steps: obtaining the initial position, target position and marine environment information of an unmanned ship; inputting the initial position, the target position and the marine environment information of the unmanned ship into a pre-constructed deep reinforcement learning model, and based on the deep reinforcement learning model, using a state value function to evaluate the long-term return of the unmanned ship taking different actions in each state, determining an optimal action strategy of the unmanned ship according to the long-term return, and obtaining a local path planning result of the unmanned ship; based on a preset path optimization algorithm, performing smooth optimization on the local path planning result to obtain a dynamic global path of the unmanned ship; and controlling the course and speed of the unmanned ship according to the dynamic global path, so that the unmanned ship travels to a target position along the dynamic global path.

The invention belongs to the technical field of data storage, and discloses a multi-source marine environment data storage method and system based on HDF5. According to the method, a hierarchical storage framework is constructed, a domain-type-source three-level directory architecture is constructed, a space-time dimension dynamic extension mechanism is executed, and space-time composite coding is performed; designing an HDF5 nested packet storage architecture, wherein grid topological structure definition, dynamic resolution self-adaptive storage, multi-source data processing and metadata management are carried out; constructing distributed data association and indexes, including data standardization preprocessing and cross-source data association; the storage performance is optimized and expanded, wherein density sensitive blocking and layered compression are carried out. According to the method, redundant storage of a high-latitude region is greatly reduced, and dynamic adaptation of a local encryption grid is supported. According to the invention, open circulation and collaborative utilization of interdisciplinary data resources are facilitated.

The invention discloses a typhoon wave forecasting method based on integrated machine learning. The method comprises the following steps: firstly, integrating historical typhoon wave data, meteorological data and marine environment data; preprocessing the data, including integration, cleaning, vacancy filling and standardization, and performing multi-source data completion by adopting a K-nearest neighbor algorithm and a spline interpolation method; secondly, screening key characteristic parameters through a Pearson's correlation coefficient, and reinforcing nonlinear correlation representation in combination with a mutual information method; then, constructing an integrated prediction model containing an LSTM (Long Short Term Memory), an XGBoost (X Goose Boost) and a Transform; and finally, dividing a training set and a verification set by adopting a dynamic time sequence division strategy, optimizing model hyper-parameters, and completing training and testing of the typhoon wave height prediction model. According to the method, the data sparsity problem is solved through multi-source data fusion and feature selection optimization, the generalization ability is improved through an integrated model architecture, and compared with a traditional single model, the training period is remarkably shortened, and the forecasting precision and timeliness are improved.

The invention provides a multi-temperature-salinity-depth sensor cooperative detection task planning method, and aims to improve the detection precision and resource utilization efficiency of a sensor network. The method comprises the following steps: firstly, acquiring initial detection parameters of a plurality of temperature-salinity-depth sensors, and calculating a detection capability matrix of each sensor based on the information; then, a target detection area is partitioned through a regional environment model, and a target information entropy model is constructed based on dynamic changes of environment parameters; and combining the target information entropy model and the detection capability matrix to construct a task allocation optimization model, and generating a preliminary sensor task allocation scheme. Furthermore, on the basis of considering environment change, detection data quality and sensor resource constraints, the preliminary task scheme is optimized, and finally a sensor collaborative detection task planning scheme is formed. Through the information entropy model and the optimization task allocation method, the detection efficiency and data quality of the sensor are improved, and the method can be widely applied to marine environment monitoring and underwater detection tasks.

The invention provides a marine reanalysis wind field data correction method based on buoy observation data, and belongs to the technical field of meteorologies.The marine reanalysis wind field data correction method comprises the steps that firstly, abnormal value elimination and quality control are conducted on the buoy observation data, and space-time matching is conducted through a bilinear interpolation method; constructing a dynamic time window, and adaptively adjusting the time precision according to the meteorological event type; calculating wind speed and wind direction errors, and extracting spatio-temporal characteristic parameters; the method comprises the following core steps of: optimizing Gaussian process regression by using an ocean physical constraint neural network model, wherein the model comprises a surface layer physical process coding layer, a boundary layer dynamic embedding layer and a multi-head sparse attention layer; constructing a space-time covariance model to generate an error field; correcting the reanalyzed wind field data and outputting uncertainty evaluation; the technical problem that a significant space-time error exists between reanalysis wind field data and an actual observation value in a complex marine environment is solved.

The invention discloses an offshore platform emergency control method based on a Beidou satellite system and related equipment, and relates to the technical field of Beidou satellites.The offshore platform emergency control method based on the Beidou satellite system comprises the steps that Beidou satellite time service signals and real-time meteorological data are obtained, the method comprises the following steps: synchronously acquiring structural deformation parameters, marine environment dynamic parameters and personnel positioning information of an offshore platform, and constructing multi-dimensional data of the offshore platform; fusing and analyzing the multidimensional data of the offshore platform, extracting space-time correlation characteristics through a pre-trained dynamic risk prediction model, constructing a multi-factor causal chain, and determining a platform risk level and accident type prediction result; and triggering graded emergency response according to the accident type prediction result, encrypting and transmitting a corresponding early warning instruction to a preset terminal through a Beidou short message, and executing a corresponding emergency control method. According to the invention, risk prediction and emergency response are carried out through the multi-dimensional data of time-space synchronization, so that the offshore platform can carry out timely emergency control.

The invention provides a medical material distribution method and system based on an offshore medical rescue unmanned aerial vehicle. Wherein a marine environment model is generated; according to the marine environment model, an AI scheduling algorithm is applied to analyze the specific demand, priority and emergency degree of a marine receiving point, and an optimized task sequence and a material loading scheme are obtained; establishing a cooperative communication mechanism between offshore medical rescue unmanned aerial vehicle formations through an AI-driven distributed consensus algorithm, and dynamically adjusting a flight path and generating a distribution path by using an AI path planning technology in combination with real-time situation awareness and obstacle detection analysis; and after arriving at a specified receiving point, automatically calibrating the landing coordinates, and synchronizing the material state, handover information and environmental data to a medical team and a central scheduling system through a low-delay wireless transmission mode according to the distribution path. According to the technical scheme provided by the invention, the efficiency and reliability of offshore material distribution are greatly improved.

The invention relates to the technical field of marine environment monitoring, in particular to a marine physical data intelligent fusion and optimization processing method, which comprises the following steps: collecting marine physical data in real time through various sensors, including temperature, salinity, flow velocity, tide and sound wave characteristic data; and spatial-temporal feature extraction and spatial correlation analysis are carried out by using a depth residual-graph convolution hybrid model. And performing parameter optimization on the multi-source fusion data set by using a quantum annealing optimization algorithm to generate an optimal parameter set so as to improve the fusion precision of the data. And abnormal data nodes are identified through a self-adaptive threshold detection algorithm, so that the accuracy and reliability of the data are further ensured. And finally, constructing a three-dimensional visual marine environment model according to the optimized parameter set, and realizing a dynamic mapping relationship between model parameters and physical characteristics so as to realize real-time updating and visual display. According to the method, the processing precision of the marine environment data is effectively improved, accurate marine environment prediction is provided, and the method has a wide application prospect.

The invention provides a coast defense monitoring system based on unmanned aerial vehicle group collaboration and an application method thereof, and relates to the technical field of coast defense monitoring, and the coast defense monitoring system integrates air unmanned aerial vehicles, water surface unmanned ships and underwater unmanned ships, constructs a four-dimensional collaborative monitoring network, and combines a frequency domain resonance superposition technology and real-time analysis of marine environment variables. A dynamic three-dimensional environment model is generated, threat prediction of a time dimension is realized, multi-source sensor data can be fused in real time, a complete space contour of a target is formed, threat distribution in several hours in the future is predicted through environment change, for example, unmanned aerial vehicle deployment is adjusted in advance to cope with illegal border crossing behaviors in storms, and the risk of threatening is reduced. Quantum entanglement communication and acousto-optic hybrid communication ensure the stability and security of data transmission, and support millisecond response. According to the effect, the real-time performance and foresight of coast defense monitoring are remarkably improved, so that the system not only can quickly cope with current threats, but also can actively prevent potential risks.

The invention provides an ocean net cage unmanned ship intelligent inspection method based on multi-source data and environment perception, which comprises the following steps: collecting and preprocessing multi-source ocean environment data of an ocean net cage group area, and simultaneously obtaining net cage distribution information, net cage inspection task requirements and an initial state of each unmanned ship; predicting the number of unmanned ships by using an improved random forest model; adopting an improved genetic algorithm to generate a plurality of optimal inspection paths; intelligently distributing a plurality of optimal routing inspection paths to each unmanned ship, and performing routing inspection on each unmanned ship according to the distributed path; multi-source marine environment data collected by a fixed monitoring station deployed in a net cage group area and a sensor of the unmanned ship can be comprehensively utilized, unmanned ship inspection path pre-planning, intelligent task allocation and dynamic adjustment of the inspection process are achieved, and therefore the inspection efficiency is improved, inspection safety is guaranteed, resource allocation is optimized, and the cost is reduced. And the intelligent level of mariculture management is improved.

The invention discloses a seabed sediment classification method and product based on seabed surface sediment acoustic characteristics, and relates to the field of seabed acoustic detection. The method comprises the following steps: firstly, acquiring actual measurement data of seabed sound reflection of a measurement area in a marine environment, and performing inversion to obtain seabed surface sediment characteristic parameters; by analyzing the uncertainty and correlation of the characteristic parameters of the seabed surface sediments, acoustic characteristic parameters of the seabed surface sediments are screened out; constructing a deep neural network model based on the acoustic characteristic parameters of the seabed surface sediments, wherein the deep neural network model comprises a seabed acoustic-physical information neural network model and a sediment attribute prediction neural network model; expanding the training sample set according to the actual measurement data of seabed sound reflection and the acoustic characteristic parameters of the seabed surface sediments, and training and testing the deep neural network model; the trained seabed sediment classification model is adopted to perform seabed sediment classification of the measurement area, fine classification of the seabed sediment can be realized, and classification continuity and classification precision are improved.

The invention discloses an error correction system and method for marine environment observation data. The error correction system comprises a data acquisition module, a compensation modeling module, an error analysis module, a data compensation module, a data fusion module and a feedback optimization module. The data acquisition module acquires ADCP flow measurement, GPS, IMU, temperature, pressure and water quality monitoring data. And the error analysis module detects a drift error by using Kalman filtering and a hidden Markov model, and analyzes an environmental error based on a long short-term memory network. The data compensation module optimizes a compensation model and improves data precision. And the data fusion module fuses multi-sensor data by adopting a particle filter and a Bayesian optimization algorithm. The feedback optimization module dynamically adjusts the weight of the compensation model according to the error correction data, and improves the adaptability of the system. According to the invention, the accuracy of ocean current monitoring data is improved, and more reliable data support is provided for underwater environment research.

The invention discloses a marine environment short-term prediction method and system based on a conditional adversarial network, and particularly relates to the field of marine environment short-term prediction, and the method comprises the steps: S1, obtaining multi-dimensional original data covering a marine physical environment and a remote sensing field; s2, performing space-time registration and abnormal value elimination on the multi-dimensional original data, and filling missing values by adopting a moving average method to obtain standardized data; s3, fusing the standardized data into coupling features reflecting the ocean dynamic process, and constructing spatial-temporal feature codes; s4, constructing a conditional adversarial network model based on the spatio-temporal feature codes, driving the conditional adversarial network model to learn a real data distribution rule through a loss function, generating prediction features meeting spatio-temporal conditions, and completing training; according to the method, the three-dimensional spatial-temporal feature tensor reflecting the ocean dynamic process is obtained through multi-source data fusion and coupling feature engineering, and the characterization capability of data and the description precision of a physical mechanism are improved.

The invention relates to the technical field of marine environment monitoring, and discloses an intelligent collection method and system for marine multi-dimensional information and a storage medium. The method comprises the following steps: synchronously acquiring environmental parameters at a plurality of measuring points and a depth layer, and constructing a hydrological characteristic data set; calculating spatial correlation and dividing a high variation region and a low variation region; constructing an equipment spacing model based on an information density classification result, and generating a point distribution scheme by adopting an optimization algorithm; combining real-time monitoring data to judge significant changes and dynamically updating layout parameters; and issuing an instruction to the equipment through the main and standby wireless channels to complete deployment. According to the invention, the adaptability and layout efficiency of marine environment perception are improved, and the method is suitable for intelligent monitoring application of complex and changeable sea areas.

The invention relates to the technical field of deep learning, and discloses a multi-source ocean data deep learning analysis and prediction platform construction method and device, and the method comprises the steps: collecting historical multi-source ocean environment parameter data, and carrying out the classification preprocessing, and obtaining a sudden change monitoring data set, a periodic monitoring data set, and a statistical monitoring data set; performing data decomposition and mask encryption processing to obtain a local training data set and a distributed training data set; constructing a local prediction model based on the local training data set, and constructing a distributed prediction model based on the distributed training data set; training and iteratively optimizing the local prediction model and the distributed prediction model by adopting a federated learning framework to obtain a target prediction model; and deploying the target prediction model to a real-time calculation unit, performing multi-source marine data analysis on the real-time multi-source marine environment parameter data, and outputting a marine environment parameter prediction result, thereby improving the adaptability and prediction accuracy of the model to marine environment changes.

The invention discloses an internal wave characteristic field information rapid prediction method based on deep learning, and belongs to the technical field of ocean internal wave detection. According to the method, rapid prediction is carried out on the basis of traditional numerical simulation data by using deep learning, so that the efficiency of inner solitary wave prediction is remarkably improved; through multi-channel coupling learning and an iterative prediction strategy, the inner wave field prediction achieves the effect of high precision and high efficiency. Meanwhile, in combination with high-resolution numerical mode data, the consistency of a model prediction result and a physical process is ensured, and rapid extension period prediction of a solitary wave characteristic field in the ocean is realized while low error and high relevancy are kept. The method is suitable for application scenes of real-time ocean internal wave monitoring and early warning and the like, and can be widely applied to the technical fields of ocean environment monitoring and early warning, intelligent ocean observation, ocean engineering safety and the like.

The invention discloses an ocean current comprehensive monitoring system based on an ocean observation buoy, and relates to the technical field of ocean observation, the ocean current comprehensive monitoring system comprises a buoy main body module, a net buoyancy adjusting module, a motion state prediction module, a buoy deviation evaluation module, a buoy motion optimization module and a data transmission module, and the modules are in electric signal connection; the buoy main body module carries various sensors to collect physical parameters of the marine environment. Through cooperative work of the motion state prediction module and the buoy motion optimization module, the system can predict and adjust the motion state of the buoy so as to reduce data acquisition errors caused by environmental changes, so that the accuracy of data is improved, a solid data basis is provided for marine scientific research, and through the motion state evaluation module, the accuracy of the buoy is improved. The abnormal motion state of the buoy can be found in time, and the state of the buoy is rapidly adjusted through the motion optimization module, so that the continuity and accuracy of data are ensured.

The invention discloses an unmanned ship navigation path intelligent optimization system and method based on ocean data, and particularly relates to the technical field of unmanned ship navigation. The accuracy of monitoring data of the sensor in a marine environment is continuously evaluated through a series of steps of preliminary calibration, data acquisition, filtering processing, abnormal marking, feature extraction and the like of the unmanned ship sensor, and real-time evaluation and time period division of the quality of the monitoring data are realized through comprehensive analysis of abnormal features of sound velocity and conductivity. The abnormal degree is predicted in the abnormal monitoring time period, and for the condition of high abnormal degree, a safety avoidance strategy is automatically set through a path planning system, and the route and the navigational speed of the unmanned ship are dynamically optimized, so that a risk area is avoided, the navigation precision and safety are improved, the reliability of sensor data is remarkably improved, and the safety of the unmanned ship is improved. The influence of data deviation on path planning is reduced, and the task success rate and the navigation safety of the unmanned ship in a complex marine environment are enhanced.