433 results about "Ocean current" patented technology

The invention discloses an AUV (Autonomous Underwater Vehicle) path planning and control integrated method for a complex marine environment, and provides an intelligent path control scheme integrating reinforcement learning and local obstacle avoidance mainly aiming at underwater navigation problems such as submarine topography fluctuation, frequent ocean current disturbance and uncertain dynamic obstacle distribution. According to the method, a realistic three-dimensional ocean grid environment is constructed, an improved Q-learning algorithm is utilized to complete global path planning, a group of optimal path point sequences are generated, and it is ensured that the AUV has the macroscopic navigation capability. Local obstacle avoidance is performed in combination with a three-dimensional dynamic window method (3D DWA), and sudden obstacles are dynamically avoided through speed sampling and trajectory prediction. A DDPG control strategy based on deep reinforcement learning is further introduced, end-to-end training of path planning and control execution is realized, and the system can directly output a control instruction according to a real-time sensing state. A cosine attenuation learning rate, a self-adaptive exploration mechanism, an energy consumption penalty function and a dynamic obstacle prediction and control action filtering technology are fused in the method, so that the intelligence of path planning, the stability of control output and the robustness of obstacle avoidance response are effectively improved. According to the invention, the autonomous navigation capability, the environmental adaptability and the safety task completion efficiency of the AUV in a complex marine environment can be obviously enhanced, and reliable intelligent control technical support is provided for underwater operation.

The invention discloses a polar region sea area ice area route dynamic planning method based on multi-source data, and the method comprises the following steps: obtaining sea ice, ocean current and wind field historical data of a target channel sea area, and carrying out the preprocessing; inputting a preprocessing result into a constructed sea ice forecasting model to obtain a sea ice concentration and sea ice thickness distribution forecasting result of a planning day; combining the forecast result with ocean current and wind field forecast data, and utilizing a pre-established random forest navigational speed reasoning model to obtain target channel sea area planning daily navigational speed space distribution data; and carrying out route planning with minimum time cost through an improved A * algorithm to obtain a dynamic planning result of the target channel sea area route. The invention relates to the technical field of ship navigation in a sea ice area, and relates to the technical field of ship navigation in the sea ice area, a polar sea area navigation speed background field is deduced according to a fitting relationship through random forest modeling and fitting of the relationship between the navigation speed and sea ice, ocean current and a wind field, and dynamic route planning with minimum time cost is carried out.

The invention discloses an improved Kalman filtering ocean current estimation method based on inertial information and DVL speed. The method comprises the following steps: S1, system initialization and data acquisition; s2, performing inertial navigation system (INS) resolving, namely performing mechanical arrangement resolving based on IMU data; s3, constructing a fusion filtering state model and an observation model; s4, adaptive Kalman filtering iteration estimation: after discretizing the state equation and the observation equation, executing filtering iteration according to a period; and S5, state feedback correction and ocean current output. According to the improved Kalman filtering ocean current estimation method based on inertial information and DVL speed, a fusion framework of an augmented state model and adaptive Kalman filtering is constructed, and high-precision ocean current estimation and navigation correction are realized through five key processes.

The invention relates to a DRL-based AUV (Autonomous Underwater Vehicle) path planning and obstacle avoidance method in a partially observable environment, which comprises the following steps of: firstly, executing an AUV path planning and obstacle avoidance task under the condition that marine environment information is partially observable; secondly, constructing an underwater environment model and an AUV kinetic model, and constructing a dynamic and static separated dual-channel adaptive sensing model through local environment information acquired by detection equipment; then, path planning is modeled as a Markov decision process, and a multi-objective optimization reward function is designed for collaborative optimization; finally, an improved maximum entropy SAC algorithm is combined with a priority experience playback mechanism to train a strategy network, a gliding mode is dynamically triggered through a physical constraint layer, attitude angle and thrust output is corrected in real time, and the safety boundary condition is monitored in real time. According to the method, the real-time performance of path planning, the dynamic and static obstacle avoidance success rate and the ocean current utilization rate under the complex sea condition can be remarkably improved, and a high-robustness autonomous navigation solution is provided for ocean exploration.

The invention relates to the technical field of underwater robots, in particular to an underwater robot cluster path planning method and system based on a dung beetle algorithm, and the method comprises the steps: obtaining a coordinate division region to construct an environment model, calculating and extracting an obstacle distance to fuse ocean current interference to generate a dynamic obstacle avoidance strategy, and evaluating the path speed similarity to generate a candidate path. And acquiring an offset angle difference, normalizing environmental factors to generate a correction path, and calculating a curvature fusion collision probability to generate a global path control instruction. According to the method, a dynamic model is constructed based on Cartesian coordinate division, the spatial analysis precision is improved, a sonar distance and ocean current interference are fused to generate a dynamic obstacle avoidance strategy, the trajectory smoothness is enhanced, candidate paths are screened by using vector similarity and Monte Carlo simulation, the collaboration is optimized, disturbance is reduced through coordinate offset and ocean current correction, and the accuracy of the dynamic obstacle avoidance strategy is improved. The curvature and collision probability are combined to balance safe energy consumption, and multi-dimensional parameter fusion improves cluster adaptability, precision, efficiency and energy utilization rate.

The invention relates to the field of ocean current remote sensing, and relates to a vector flow inversion method combining a single-station ground wave radar and a satellite-borne SAR, and the method mainly comprises the steps: calculating the frequency deviation of a first-order Bragg peak of the ground wave radar, carrying out the inversion of the sea surface radial flow of the ground wave radar, and carrying out the inversion of the sea surface radial flow of the SAR through employing a Doppler centroid anomaly method; interpolating a ground wave radar radial flow result to an SAR data grid through bilinear interpolation; inverting a single-station vector flow based on a ground wave radar radial flow result, projecting single-station vector flow data with high confidence coefficient to an SAR range direction, calculating a difference mean value of a projection flow velocity and an SAR radial flow velocity, and further calibrating the SAR radial flow; and combining a ground wave radar radial flow result with the calibrated SAR radial flow to invert a vector flow. According to the method, the defect of ocean current observation through a single means is overcome, high-precision inversion of the combined vector flow is achieved, and the method has important significance on ocean environment monitoring, disaster forecasting, shipping safety and the like.

The invention discloses a marine algal bloom disaster early warning system based on big data, particularly relates to the technical field of marine algal bloom disaster early warning, and is used for solving the problems that an existing algal bloom disaster monitoring means is lagged, early warning response is not timely and multi-source environmental data is difficult to fuse. The method comprises the steps of obtaining multi-source marine environment data including algae bloom position data and ocean current motion data, performing time-space synchronous calibration and standardization processing on the data, establishing a risk early warning area identification model, and calculating a dynamic correction coefficient in combination with an environment modulation factor. Key early warning features such as propagation trend intensity, influence range and duration are extracted to construct a multi-dimensional early warning signal vector, the multi-dimensional early warning signal vector is input into a pre-trained early warning level prediction model to generate a prediction early warning level, and a dynamic early warning trigger condition is set based on economic value and ecological sensitivity; and finally generating a grading early warning signal and pushing the grading early warning signal to a disaster early warning terminal in real time, thereby realizing high-precision, grading and dynamic early warning of the algae bloom disaster.

The invention discloses a real-time ocean simulation method and system based on sensing data, and relates to the technical field of ocean information engineering and intelligent sailing. A graph neural network is combined with a multi-head attention mechanism to carry out data fusion, importance of different sensors and data time steps is dynamically weighted, and real-time ocean simulation is realized. According to the method, the quality difference and missing conditions of heterogeneous data are adaptively processed, the deviation and noise influence in the fusion process are reduced, meanwhile, physical consistency constraints such as mass conservation and boundary non-penetration are introduced, the physical rationality and smoothness of an output ocean current field are guaranteed, non-physical artifacts are avoided, and the reliability and practicability of a result are enhanced; a streaming updating mechanism allows a graph structure and model parameters to be rapidly and incrementally updated when new data arrives, data processing and assimilation delay are greatly reduced, and nearly real-time ocean current forecasting is realized, which is crucial to a ship autonomous navigation system, and accurate ocean current information can be timely provided to support dynamic path planning and risk avoiding decision making.

The invention discloses a water surface unmanned ship control method and system under path tracking, and relates to the technical field of attitude control, and the method comprises the steps: building a kinetic model of a water surface unmanned ship; establishing a path tracking error equation for describing a system error of the unmanned surface ship relative to an expected path under a Serret-Frenet coordinate system; constructing a path tracking controller based on a linear active disturbance rejection controller; inputting the system error and a water surface unmanned ship control signal into a fuzzy RBF neural network, and outputting an optimal control parameter of a path tracking controller by taking minimization of the system error as a target; updating a path tracking controller according to the optimized control parameters; and re-estimating external disturbance by using the updated path tracking controller, outputting a control signal for optimizing the water surface unmanned ship, and controlling the water surface unmanned ship to advance along an expected path. According to the invention, the attitude control precision under strong ocean current and changeable weather is improved, and the path tracking precision of the water surface unmanned ship is greatly improved.

The invention discloses a vibration control method for an offshore wind turbine structure, which comprises the following steps of: a, implementing real-time multi-source data acquisition which is realized by deploying a series of sensors on the offshore wind turbine structure and in the surrounding environment so as to obtain environmental load parameters, structural response parameters and running state parameters of the offshore wind turbine; and b, inputting the acquired data into a composite environment and structure state decoupling and feature extraction module, preprocessing the acquired data by the module, and identifying an environment load spectrum in real time. According to the vibration control method for the offshore wind turbine structure, through multi-source information fusion, online system identification and self-adaptive multi-target control, broadband, multi-mode and self-adaptive suppression of vibration of the offshore wind turbine structure is achieved, and the limitation of a traditional fixed parameter damper in a complex dynamic marine environment is overcome; complex excitation sources such as wind, waves and ocean currents and the coupling effect of the excitation sources are effectively handled.

The invention relates to the technical field of ocean current data analysis, in particular to a multi-scale modeling and verification method and system based on ocean current spring layer structure analysis. And obtaining the thermocline and halocline parameters of the target sea area and carrying out abnormal value elimination and spatial interpolation to form a thermocline data set. A convolutional neural network is used to extract spring layer space structure features, and based on Gaussian curvature quantification boundary complexity, a thermocline and halocline equivalent geometric model is constructed. On the basis, taking the equivalent geometric model as a boundary condition, and performing multi-scale decomposition on the Navier-Stokes equation set to generate a dynamic approximate equation; and then inputting the approximate equation and the equivalent geometric model into a physical information neural network for constraint training to obtain a multi-scale flow field prediction model, and comparing the multi-scale flow field prediction model with actually measured flow field data to complete verification. According to the method, automation, refinement and multi-scale coupling of spring layer structure modeling are achieved, and scientificity and engineering applicability of ocean current modeling are improved.

The invention relates to a target track association method in a multi-target environment. The method comprises the following steps: firstly, preprocessing a new received trace point; secondly, predicting the next position of each track by using the existing track historical information and adopting a Kalman filtering algorithm, meanwhile, adjusting a state transition matrix and a process noise covariance matrix in real time by considering factors such as ocean current and wind direction in an offshore environment, and adjusting an observation noise covariance matrix according to meteorological information; then comprehensively considering distance, speed difference and motion direction difference to calculate a correlation degree magnitude between a new receiving trace point and each predicted track position; and finally, judging whether the newly received trace point belongs to the existing target track or forms a new target by adopting a threshold judgment method according to the correlation degree value, and updating the state of the track. The method can effectively cope with multi-target intersection, observation loss and clutter interference, improves the stability of a matching result, can realize continuous tracking of a marine target, can adjust parameters according to the environment and target characteristics, and has high adaptability.

The invention discloses a vortex induced force calculation method and system and a computer readable storage medium, relates to the technical field of ship and ocean engineering, and is used for evaluating the influence of drifting trailing vortex on underwater movement attitude and trajectory of a wrecking target and providing a corresponding vortex induced force numerical calculation method. Based on a computational fluid mechanics method, a multi-ocean-current scene numerical model is established, the falling process of a wreck target in still water and ocean current environments is simulated, corresponding trailing vortex structural features are captured, differences of trailing vortex distribution under the still water and ocean current conditions are compared and analyzed, and the influence mechanism of ocean current on trailing vortex evolution is revealed. And the interference mechanism of the trailing vortex on the falling track of the wrecked target is clarified. According to the method, the vortex induced force is extracted from the trailing vortex distribution and is quantified into the mathematical expression, so that the influence of the trailing vortex on the underwater falling track of the target can be directly pre-judged, the prediction precision of the falling track envelope and the falling point dispersion range can be improved, and the success rate of the underwater search of the wrecked target can be improved.

The invention belongs to the technical field of marine environment monitoring and protection, and discloses an unmanned aerial vehicle multi-mode marine environment real-time monitoring system based on deep learning, and the system comprises the steps: extracting an instantaneous velocity vector based on an ocean current trajectory; a sea surface flow velocity field is generated, a calibration pile fingerprint database is constructed, and three-dimensional grid parameters are set; secondly, correcting the thickness of a vertical layer of the three-dimensional grid, constructing a tide compensation floating coordinate system, and generating a multi-modal sensor collaborative acquisition instruction set; secondly, performing hard synchronization on multi-modal data, generating a fusion data cube, predicting pollutant concentration increase through LSTM (Long Short Term Memory), and generating a high-risk area list; thirdly, obtaining the battery health state of the unmanned aerial vehicle, limiting the flight attitude, generating an optimized path, and predicting a residual endurance curve; and finally, dynamically allocating unmanned aerial vehicle-subarea mapping through an auction algorithm, verifying bidding by using a block chain, and finely adjusting LSTM parameters through an energy consumption data reverse calibration model and anomaly detection to form closed-loop optimization.

The invention discloses a marine environment spring layer characteristic diagnosis data processing method, and aims at complex dynamic characteristics of a land frame edge sea spring layer to realize accurate diagnosis through multi-source data cooperation and an intelligent algorithm. The method comprises the following steps: acquiring and preprocessing field observation, remote sensing, sonar, terrain and tidal ocean current data; extracting spatio-temporal characteristics based on a CNN-LSTM hybrid network, and generating a dynamic spring layer threshold; fusing multi-source data by adopting an improved D-S evidence theory, and constructing a three-dimensional spring layer characteristic field; predicting an internal wave in combination with an attention mechanism model, and optimizing a preorder model by using sonar data. The method breaks through the limitation of a fixed threshold value, realizes spring layer dynamic identification, three-dimensional reconstruction and internal wave prediction, improves the diagnosis precision of a complex sea area, and provides key technical support for underwater detection, engineering safety and environmental research.

The invention relates to the technical field of ship group intelligent control, and discloses a ship motion trail optimization control system based on deep learning, which comprises control devices deployed on ships, receiving navigation state information of adjacent ships through a ship automatic identification system, generating asymmetric virtual energy-saving potential fields, superposing the asymmetric virtual energy-saving potential fields to form a total potential field, and outputting the total potential field; according to the method, a ship group spontaneously forms a dynamic navigation manifold with relatively low overall energy consumption, and through the asymmetric potential field design of bow repulsive force and stern gravitational force, linkage course correction caused by traditional collision avoidance is converted into collaborative energy-saving motion, so that the energy-saving effect of the ship is improved. Meanwhile, navigation safety is ensured in combination with a post-arbitration mechanism, and environment self-adaptive compensation is achieved through micro-disturbance inversion ocean currents.

The invention discloses an ocean current extraction method and system, and relates to the technical field of data processing, and the method comprises the steps: building causal priori from SWOT satellite observation data based on a set variation assimilation system and a structural equation model; wherein the causal priori comprises a causal path and strength between wind stress forcing and ground rotation adjustment; carrying out iterative generation through a physical constraint diffusion model under the conditions of causal priori and SWOT satellite observation data, and outputting a sea surface flow field; performing multi-level interpretability verification on the causal path and the step of performing iteration generation through the physical constraint diffusion model to obtain a verification result; a verification result is fed back to the structural equation model and the physical constraint diffusion model to form closed-loop optimization, and then the optimized sea surface flow field is obtained. According to the method, iteration is carried out through the physical constraint diffusion model to generate the sea surface flow field, multi-level interpretability verification is carried out, the sea surface flow field can be optimized by utilizing the verification result, and high-resolution ocean current acquisition is realized.

The invention provides an underwater robot and a control method thereof.The underwater robot comprises a robot body, a propelling mechanism arranged on the side face of the robot body and used for providing propelling force, and a processing mechanism in communication connection with the propelling mechanism and arranged in a closed space, the processing mechanism sends an operation instruction to the propelling mechanism so as to solve the problems that when the robot works in a narrow space (such as the interior of a net cage and between pipeline supports), the robot cannot counteract center-of-gravity shift through large-range maneuvering, and the robot is prone to collide with surrounding structures; and in a strong ocean current environment, frequent attitude correction further amplifies the risk of position drift.

The invention discloses a physical ocean virtual simulation experiment teaching device and system in the technical field of virtual reality, and the system comprises a virtual module which is used for constructing a three-dimensional dynamic virtual ocean scene; the tactile feedback module is used for simulating tactile feedback intensity of wave impact, water pressure resistance and ocean current drag force in an ocean environment; the environment simulation module is used for generating temperature, salinity change and wind field disturbance matched with the virtual ocean scene; and the edge calculation module is used for analyzing user operation behaviors and physical parameters of a virtual scene in real time through a machine learning algorithm, and dynamically optimizing tactile feedback intensity and environment simulation parameters. According to the scheme, the authenticity of environment perception can be improved, so that a more real and effective virtual simulation experiment teaching effect is realized.

The invention discloses a sediment collection and supply integrated device for offshore wind turbine pile foundation scour prevention, which is arranged around the periphery of an offshore wind turbine single pile foundation, relates to the technical field of offshore wind power engineering, and comprises an annular sand collection unit, a sand storage unit, a sand unloading unit and a sand supply guide unit which are communicated from top to bottom; the annular sand collecting unit guides sediment in ocean current to be deposited and collected, the sand storage unit receives and stores the collected sediment, and the sand unloading unit can seal a bottom outlet of the sand storage unit in an opening and closing mode; the sand supplementing guide unit directionally guides the discharged silt to the scouring pit; the mechanical triggering unit and the elastic resetting unit are used for opening and resetting the sand unloading unit. Sediment carried by ocean current is utilized, through pure mechanical passive operation, integrated self-circulation of collection, storage, triggering, release and supply of the sediment is achieved, external energy is not needed, the self-adaptive adjusting capacity is achieved, and the construction and operation and maintenance cost is remarkably reduced.

The invention discloses an ocean flow field generation method and system, a storage medium and an environment forecasting device, belongs to the field of ocean current generation and prediction, is used for carrying out randomized prediction on ocean current according to historical observation data of an ocean flow field, and comprises the steps of obtaining multi-source ocean current observation data and carrying out data preprocessing on the data; a deterministic background flow field model and a random disturbance model are constructed, the background flow field model and the random disturbance model are coordinated and coupled under physical constraints of mass conservation and quasi-earth-to-balance, a probabilistic flow field set is generated by adopting a Monte Carlo method, and an ocean flow field is constructed. According to the method, the physical certainty and the data randomness of the ocean current are coordinated and coupled under the same physical constraint, the ocean current generation model containing random disturbance is constructed, and the prediction precision of the ocean current is improved.

The invention provides an ultra-short baseline calibration method, device and system, and belongs to the technical field of underwater acoustic measurement, and the ultra-short baseline calibration method comprises the following steps: according to an antenna phase center position, a carrier instantaneous attitude, a distance and azimuth information at each observation moment, and installation parameters of an ultra-short baseline acoustic transducer relative to an inertial navigation system, carrying out calibration on the ultra-short baseline; establishing a distance equation between the ultra-short baseline acoustic transducer and the ultra-short baseline acoustic beacon at each observation moment; establishing a residual equation of each distance equation; and solving the residual equation through a nonlinear least square method to obtain installation parameters. According to the method, the installation parameters of the ultra-short baseline acoustic beacon can be efficiently and accurately calibrated, and the problems that beacon laying and recycling operation is complex, cost is high, the operation period is long, efficiency is low, and the beacon is prone to drifting due to the influence of ocean currents are effectively solved.

The invention provides an underwater operation path adaptive planning method and system combined with machine learning, and the method comprises the steps: employing satellite remote sensing, underwater in-situ sensing and edge computing architecture, obtaining flow fields and terrain basic data of different scales through a multi-source sensing device and technology, achieving the fusion of multi-source data through combining with an intelligent weight distribution algorithm, and achieving the adaptive planning of an underwater operation path. A high-precision real-time sensing result is output, a physical constraint and data-driven hybrid modeling framework is constructed, a spatial-temporal feature capture model is adopted to process multi-scale data, an online self-adaptive updating mechanism containing forgetting factors is designed, dynamic adaptation of the model to time-varying ocean current characteristics is achieved, simulation pre-training, on-site fine tuning and online re-planning closed-loop algorithms are constructed, and real-time ocean current sensing is achieved. The model training efficiency is improved based on reinforcement learning and transfer learning, and an optimal correction path is generated in combination with a trigger mechanism and energy consumption optimization logic.

The invention provides a water quality pollution tracing method and system applied to a wave radar sea area, and the method comprises the steps: firstly obtaining a sea area polarization echo time sequence set captured by a wave radar and a sea area pollutant characteristic set measured synchronously, and then carrying out the polarization characteristic deconstruction processing of the sea area polarization echo time sequence set; a polarization characteristic parameter set of each sea area block is obtained, then a pollutant molecular structure and a polarization reflection coefficient in the sea area pollutant characteristic set are associated and adapted with the polarization characteristic parameter set to generate a pollution polarization association map, and a polarization-driven pollution traceability model is constructed based on the pollution polarization association map. The method comprises the steps of inputting a pollution polarization correlation map and preset sea area ocean current parameters, outputting a traceability path set of pollutants and an initial pollution area boundary, finally analyzing an original diffusion starting point of the pollutants according to a polarization-driven pollution traceability model, and generating a pollution traceability report containing information such as coordinates of the initial pollution area. Therefore, a sea water quality pollution source can be accurately positioned.

The invention discloses a multi-physics field coupling numerical method for simulating wave-ocean current-earthquake-seabed interaction, and relates to the technical field of ocean engineering numerical simulation, and the method comprises the following steps: model construction, load application, coupling solution, and result analysis and application. According to the method, revolutionary simulation of collaborative response of a seabed and a structure in a complex marine environment and multi-physics field high-precision bidirectional coupling of random waves, steady-state ocean currents and seismic excitation are realized by constructing a total-factor coupled high-precision numerical model, the problems of incomplete coupling dimensions and excessive model simplification of a traditional method are solved, and the method has the advantages of being high in practicability and high in reliability. According to the wave-induced seabed liquefaction simulation method, the simulation result and the actual working condition coincidence degree are remarkably improved, the frequency domain transfer function method is introduced to analyze the seabed pore pressure response, the cognitive depth and the prediction precision of the wave-induced seabed liquefaction mechanism are remarkably improved, and in addition, through efficient algorithms such as GPU parallel calculation, self-adaptive grids and frequency domain-time domain mixed solution, the wave-induced seabed liquefaction simulation method has the advantages that the wave-induced seabed liquefaction simulation precision is improved. And the calculation efficiency is greatly improved while the precision is ensured.

The invention relates to a bionic rotifer preying wheel disc type composite underwater target collection device and method, the collection device comprises an underwater robot and a bionic collection head, and the underwater robot is provided with a high-definition camera, a foresight sonar, an adjustable highlight LED underwater lighting lamp and a seabed in-situ metal measurement sensor; the bionic collecting head is provided with a bionic roller wheel and a conveying pipeline; the conveying pipeline is of a Venturi tube type conveying structure, and bionic protruding structures are arranged on the surfaces of the bionic roller wheels. The bionic roller wheels are coaxially connected with rotating output shafts of the waterproof driving motors correspondingly, and the waterproof driving motors drive the bionic roller wheels on the two sides of the conveying pipeline to rotate and vibrate relatively. Disturbance on a seabed sedimentary layer and influence on a seabed ecological environment can be reduced, so that collecting mechanical parts are not easy to damage in operation, the service life is prolonged, and the safety and the accuracy are improved; seabed ocean current disturbance is reduced, and an acquisition path is accurately controlled; energy consumption is reduced, jamming is avoided, sampling efficiency is improved, and the application range is widened.

The invention discloses an intelligent ship navigation system based on a space-time prediction risk field and deep reinforcement learning, relates to the technical field of ship navigation, and aims to solve the problem that a conventional navigation system is insufficient in dynamic risk assessment and prospective planning in the prior art. The technical key points are that the system comprises a multi-source data acquisition module, a dynamic risk mapping module, an intelligent path planning module, a visual interaction module, a risk early warning module and a self-checking module. According to the system, multi-source data such as weather, ocean current and ship dynamics are collected in real time, a predictive risk field is generated by using a long-short-term memory network, a route is optimized in combination with an adaptive deep reinforcement learning algorithm, and risk distribution and a recommended route are visually presented in a multi-scale three-dimensional form; the system can early warn a high-risk area in real time, and the reliability is ensured through a self-checking function. According to the invention, the sailing safety and the intelligent level of the ship in a complex environment are obviously improved.

The invention relates to a seawater environment thermal resistance determination method and system considering multi-factor influence and a medium, and the method comprises the following steps: determining the type of ocean current according to the Reynolds number of the ocean current, and calculating to obtain the Nusselt number of the ocean current; obtaining a seawater heat conductivity coefficient through a Gibbs function according to the seawater state parameters; obtaining seawater heat conduction thermal resistance according to the seawater thermophysical characteristic parameters and the seawater heat conductivity coefficient; calculating the average convective heat transfer coefficient of the surface of the submarine cable according to the seawater heat conductivity coefficient and the Nusselt number of the ocean current; obtaining convection heat transfer thermal resistance according to the average convection heat transfer coefficient; and obtaining the seawater environment thermal resistance according to the seawater heat conduction thermal resistance and the convective heat transfer thermal resistance. Compared with the prior art, on the basis that the average convective heat transfer coefficient of the surface of the submarine cable is obtained, the convective heat transfer thermal resistance is obtained according to the average convective heat transfer coefficient, the seawater environment thermal resistance is dynamically adjusted according to the seawater thermophysical characteristics and the flow speed and temperature of ocean current, and accurate calculation of the seawater environment thermal resistance is achieved.

A fully-submerged multi-stage Horizontally-oriented Conical-helical Hydrokinetic Turbine that captures water stream flows (river, tidal, deep ocean currents) and funnels this kinetic-rich concentrated flow into a conical turbine system consisting of a rotating arrangement of helical hydrofoil blades wherein the internal stream is forced to exit out through the frustum turbine blade assembly. With a higher Capacity Factor and Power Coefficient than conventional renewables, this turbine is designed to be an environmentally friendly baseload electricity generator to meet the ever-increasing electricity demands.

This invention provides a trajectory planning method for unmanned surface vessels (USVs) under complex ocean current conditions, belonging to the field of unmanned control. The method includes: acquiring the USV's initial state information, final state information, size information, and kinematic parameters, as well as a multi-objective cost function; acquiring map information and ocean current information between the USV's starting and ending points; constructing a dynamic model of the USV considering ocean currents to obtain state constraints; constructing a coarse trajectory for the USV to obtain an initial solution vector and a navigation tunnel constraint; constructing boundary constraints based on the initial and final state information; constructing control input constraints based on the size information and kinematic parameters; and obtaining the optimal control sequence to complete the trajectory planning of the USV. This invention employs a two-layer trajectory planning algorithm based on numerical optimization and a trajectory planning method considering ocean currents to ensure the safe and efficient operation of the USV.