842 results about "Ocean current" patented technology

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 provides an underwater vehicle multi-source information fusion accurate navigation method and system. The method comprises the following steps: firstly, acquiring inertial navigation data, DVL data and ocean current information of an underwater vehicle and observation information of a distance between the underwater vehicle and a beacon, and preprocessing the inertial navigation data, the DVL data and the ocean current information; then, establishing a state model according to the kinematics principle of the underwater vehicle, and establishing an observation model by combining various types of information; carrying out fusion processing on the state model and the observation model by adopting a self-adaptive Kalman filtering algorithm; and finally, estimating the current position of the underwater vehicle according to the fused state vector, comparing the current position with a preset target position to calculate a deviation, and correcting the deviation by adjusting a rudder angle and the rotating speed of a propeller. According to the method, multi-source information is integrated, innovation is carried out on construction of a state model and an observation model and position estimation and deviation correction, adaptive Kalman filtering is adopted, and the method has the advantages that the navigation precision is improved, the ocean current influence is considered, adaptive adjustment is realized, and real-time navigation is realized.

The invention discloses a seabed tracked robot path planning method and system based on reinforcement learning, and the method comprises the steps: obtaining environment data in real time through multiple sensors, extracting submarine topography three-dimensional features, obstacle distribution and ocean current dynamic parameters in combination with a neural network, and carrying out the combined feature extraction; establishing a seabed environment simulation model, and synthesizing various landform training data by using an adversarial generation technology; a hierarchical reinforcement learning framework is designed, a global layer collaboratively optimizes a long-distance path through a distributed agent, a local layer designs a high-frequency control strategy, and the global and local strategies realize multi-dimensional collaborative optimization through a dynamic weight adjustment mechanism; model parameters in a dynamic environment are updated in real time through an online optimization module, and a simulation strategy is quickly deployed to an entity robot through transfer learning. According to the invention, a neural network feature extraction and multi-level reinforcement learning collaborative autonomous decision-making system is constructed, and a high-reliability and low-energy-consumption autonomous operation solution is provided for a deep sea operation scene.

The invention discloses a high-temporal-spatial-resolution refined flow field reconstruction method, device and equipment and a medium, and relates to the technical field of ocean current reconstruction, and the method comprises the steps: carrying out the normalization and temporal-spatial alignment of satellite remote sensing, buoy observation and numerical simulation data; based on the alignment data, performing rehearsal on the unstructured nested grid through an FVCOM model, and then dynamically encrypting the grid according to the flow field gradient and generating a background flow field; inputting the background flow field into a PINN-GAN combined framework, and outputting a refined flow field through physical constraint loss and double-discriminator adversarial training; and scheduling a calculation task by adopting a heterogeneous accelerator, verifying the reconstructed refined flow field in real time, and performing feedback optimization. Through generation of the background flow field and refinement reconstruction, the ocean current flow field with high temporal-spatial resolution can be reconstructed efficiently and accurately.

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 belongs to the technical field of unmanned ship tracking control, and discloses a self-adaptive path tracking control method and device for an unmanned ship. The method comprises the steps of monitoring position data in real time through a boundary judgment algorithm to ensure that a ship is always in a preset safe area; multi-source data of ocean currents, radiation, batteries and the like are synchronously collected, three constraint conditions of predicting a drifting track, a charging area and remaining endurance time are constructed, a global optimal path is dynamically generated by applying a path planning algorithm, and the shortest distance, low energy consumption and high charging benefits are balanced. For energy management, a multi-stage power threshold is preset to trigger a differential loss reduction strategy. In a control layer, calculating propeller control parameters in real time based on path deviation, course deviation and environment interference; through collaborative optimization of global path planning and local control, the autonomous navigation capability, the energy utilization efficiency and the task sustainability of the unmanned ship in a dynamic environment are improved, and the method is suitable for ocean monitoring, resource exploration and other scenes.

The invention discloses a deep and far sea unmanned aerial vehicle monitoring and management platform based on multi-sensor fusion, and relates to the technical field of unmanned aerial vehicle marine monitoring. The intelligent analysis module is used for receiving the fusion data after time-space alignment and the real-time ocean current data, optimizing the moving path of the unmanned aerial vehicle according to the received data, evaluating the resource development possibility, obtaining an analysis result and transmitting the analysis result; an anti-interference communication module; an energy management module; and dynamically adjusting an energy distribution strategy. The module combines the task priority weight and the environment energy consumption weight, optimizes the energy use efficiency, prolongs the endurance time of the unmanned aerial vehicle, and ensures the smooth completion of the task. Particularly in a complex marine environment, navigation errors are corrected in real time through a quantum navigation error compensation model, the positioning precision is improved, and the moving path of the unmanned aerial vehicle is optimized in combination with real-time ocean current data and an ocean flow field cost function.

A wind and wave response modeling and ocean current inversion method based on machine learning relates to the technical field of artificial intelligence and ocean information services, and comprises the following steps: (1) data acquisition; (2) data processing; (3) constructing a data set; (4) establishing a sea surface flow velocity inversion model by using a particle swarm optimization machine learning algorithm; and (5) obtaining an optimal sea surface flow velocity inversion model through testing and case analysis. Aiming at ocean and Doppler frequency shift characteristics of the sea surface, the invention provides a particle swarm optimization XGBoost model wind wave Doppler contribution prediction method for the first time, and the wind wave Doppler contribution estimation precision and speed can be effectively improved.

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

The invention relates to the technical field of ocean engineering construction, and discloses a construction positioning and SCADA visualization integrated intelligent arrangement method for an arrangement ship, which comprises the following steps: synchronously acquiring coordinates and attitude parameters of the arrangement ship through a Beidou positioning terminal and an inertial navigation device, and constructing a three-dimensional digital twinborn model of a construction scene in combination with submarine topography surveying and mapping data; analyzing boundary and gradient characteristics of the target arrangement area based on the model, and generating an initial arrangement path planning scheme; position deviation and ocean current flow velocity data are monitored in real time in the operation process, and the laying path is dynamically adjusted; and the position of the laying ship, the path adjustment record and the submarine topography change are displayed in real time through a visual interface of the SCADA system. According to the method, high-precision positioning, dynamic path optimization and full-process three-dimensional visual monitoring of the laying operation are realized, the problems of large positioning deviation and response lag of a traditional method are solved, and the construction precision and efficiency are remarkably improved.

The invention discloses a cable underwater robot trajectory tracking control method for coping with ocean current disturbance, and belongs to the technical field of underwater robot trajectory tracking, and the method comprises the following steps: S1, constructing a kinematics and dynamics model of a cable underwater robot, and simplifying the kinematics model of the cable underwater robot; s2, combining speed information of a Doppler velocimeter sensor, designing a super-spiral expansion state observer, and compensating a cable underwater robot system; designing a self-adaptive super-spiral expansion state observer by combining a self-adaptive law; and S3, designing a self-adaptive super-spiral integral terminal sliding mode controller based on the self-adaptive super-spiral expansion state observer. The real-time dynamic performance and stability of the underwater robot in sliding mode control are improved, the buffeting problem existing in sliding mode control is solved, it is ensured that under the condition of ocean current disturbance, the ROV can still achieve accurate trajectory tracking, and the robustness is very high.

The invention discloses an AUV (Autonomous Underwater Vehicle) path tracking method based on predicted line-of-sight angle guidance, which comprises the following steps: acquiring a real-time navigation state through inertial navigation, acoustic positioning and DVL fusion, introducing a fixed time disturbance predictor to estimate a sideslip angle and an ocean current velocity component on line, adaptively adjusting a line-of-sight distance according to transverse error-navigational speed-curvature, and tracking the path of the AUV. Calculating a predicted line-of-sight angle and outputting an expected heading; and then a propeller and a control surface are driven by adopting a fixed time feedback control law, so that track and attitude error fixed time convergence of the under-actuated AUV under the conditions of strong time-varying ocean current and large sideslip is realized. The method does not need a side thruster or an ocean current prior model, is simple in algorithm structure, is suitable for a high-speed high-curvature track, and has the advantages of high speed, high steady-state precision, low energy consumption and high robustness.

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 further provides an unmanned ship collision avoidance method and device based on the DBSCAN algorithm, and the method comprises the following steps: 1, collecting ship data, and training a six-degree-of-freedom ship motion model through a neural network; step 2, acquiring a wind speed by using a wind speed sensor, and acquiring ocean current information by using an ocean current sensor; step 3, acquiring speed and position information of other ships by using AIS / radar; step 4, collecting ship captain driving information, establishing a ship driving model, and predicting the future motion state of the ship; 5, performing risk analysis on other ships by using a DBSCAN algorithm, and calculating ship collision risks; step 6, according to the step 5, performing corresponding collision avoidance behaviors aiming at the risk; and 7, judging an action execution condition, and returning a reward value and a result. According to the technical scheme, the collision avoidance capability of the unmanned ship can be improved, the generalization capability is good, and the efficiency of collision avoidance and safe navigation of the unmanned ship on the water surface is improved on the whole.

The invention relates to the field of offshore floating type wind power, in particular to an automatic buoyancy adjusting system and method for a floating type draught fan platform, the adjusting system comprises a buoyancy device which is fixedly installed on a stand column or a buoyancy box, the buoyancy device comprises a semi-flexible shell, and at least one air bag is arranged in the semi-flexible shell; the volume of the air bag is determined through the following steps that S1, real-time motion data and environment data of the floating type draught fan platform are obtained; and S2, whether the current posture of the floating type fan platform is stable or not is evaluated by analyzing the real-time motion data and the environment data, and if not, the size of an air bag is adjusted by inflating and deflating. By arranging the buoyancy device, buoyancy can be flexibly adjusted according to sea conditions, heaving, trim and rolling motions of the floating fan platform under the action of waves, wind loads and ocean currents are remarkably reduced, the platform can adapt to rapid changes of the sea conditions in a short time, and impact of stormy waves on the platform is reduced.

The invention relates to a method and a system for detecting the sealing performance of a spherical underwater connector. The method comprises the following steps: injecting dry inert gas with the same working pressure into a sealing ring through a pressure measuring hole; 5-15-degree and 0.1-1Hz sinusoidal deflection motion is applied to the sphere, and the dynamic working condition that the underwater pipeline is impacted by ocean current for a long time is simulated through short-term limit periodic deflection motion; meanwhile, the pressure fluctuation amplitude delta P is recorded with the sampling frequency larger than or equal to 10 Hz, the first period delta P0 serves as the benchmark, delta Pn is measured regularly, and a delta P (t) curve is built; and determining a critical threshold value delta Pmax through an experiment, and calculating the duration of increasing the delta P0 to the delta Pmax according to the delta P (t) curve as the predicted service life. Through short-term limit dynamic loading and high-frequency monitoring, real-time state detection and long-term performance evaluation of the sealing ring are achieved, and the problem that sealing performance degradation under alternating stress cannot be predicted through traditional static detection is solved.

The invention relates to the technical field of unmanned ship navigation control, and discloses an unmanned ship navigation state control method and system based on intelligent perception. Navigation environment information of multi-dimensional data is obtained, a multi-dimensional data fusion technology is adopted for construction of an environment state matrix, and discrete environment elements are converted into structured matrix expression. In the initial path generation stage, data acquisition value evaluation is fused, obstacle avoidance requirements are considered, and a high-value detection area is actively covered. In the path correction stage, a multi-objective cost function including energy consumption, threat degree and path length is constructed, and real-time path optimization in a dynamic environment is realized. In the propulsion control link, propulsion force and rudder angle parameters are calculated by integrating the attitude, the ship speed and the ocean current speed of the unmanned ship. According to the method, the contradiction among the real-time performance of dynamic obstacle avoidance, the data acquisition efficiency and the energy consumption optimization is solved through the cooperation of global-local double-layer path planning and the control of the propulsive force and the rudder angle of the unmanned ship.

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 provides a deep sea temporary platform large-diameter steel casing dynamic response analysis method, and relates to the technical field of deep sea engineering, and the method comprises the steps: building a three-dimensional explicit dynamic coupling model according to the typhoon frequent generation characteristics of a target sea area and steel casing structure parameters; according to the three-dimensional explicit dynamic coupling model, the wave load is decomposed, the vibration hammer load effect, the ocean current load effect and the internal wave current load effect are integrated, and a comprehensive dynamic load set of the steel casing is determined. By establishing a precise model, integrating multi-source loads, identifying a risk area, monitoring stress in real time and optimizing design parameters based on data, self-adaptive matching of steel casing design and a complex marine environment is achieved, the structural safety of the deep sea temporary platform steel casing is effectively improved, and the cost is reduced.

The invention provides a single-point mooring floating type fan platform capable of actively yawing, and belongs to the technical field of offshore wind power. The problem that the aerodynamic efficiency of an impeller is reduced due to the fact that a fan platform needs to bear multiple coupling effects of ocean currents and waves and the fan platform deviates from the best windward direction due to the hydrodynamic force coupling effect when an included angle is formed between the ocean current direction and the wind direction under the action of the complex ocean environment is solved. The system comprises a wind turbine generator, a mast type tower, a platform foundation, a single-point mooring device and a flow guide device, the edge of the platform foundation is connected with the single-point mooring device and can rotate by 360 degrees around the single-point mooring device. The flow guiding device is installed at the edge position of the platform foundation and arranged opposite to the single-point mooring device, the horizontal section of the flow guiding device is in a symmetrical streamline shape, and the flow guiding device is provided with a driving device which drives the flow guiding device to rotate in the horizontal direction and adjusts the acting force of ocean current on the fan platform. The power generation device is mainly used for improving the power generation efficiency of the mast type tower offshore wind turbine platform.

The invention discloses an L-shaped wind wave flow-earthquake simulation system and method suitable for a high gravity field, the simulation system comprises a model box, a wind generation system, an L-shaped wave generation system, a flow generation system and a vibration seabed model, and the model box is used for accommodating and installing each system and is arranged on a rotating arm of a centrifugal machine in an up-and-down swinging manner; the wind generation system is used for forming horizontal airflow circulation, the L-shaped wave generation system is used for generating waves, the flow generation system is used for generating flow, and the vibration seabed model is used for researching interaction between a maritime work structure and seabed soil under various sea conditions. The simulation system can reproduce sea wind, wave, ocean current and earthquake scenes in a high gravity field.

The invention relates to an anti-offset wireless charging coupler for an underwater vehicle, which solves the problem that the multi-dimensional offset charging efficiency is reduced due to ocean current by adopting the collaborative design of a grouped serial-wound transmitting end and a geometric adaptive receiving end. The transmitting end is formed by connecting an outer ring winding and an inner ring winding in series, the outer ring provides wide-area magnetic field coverage, the inner ring strengthens the local magnetic field intensity, and the ferrite array is combined to suppress magnetic leakage to cope with horizontal and rotary offset; the receiving end adopts a curved surface type structure and fits the geometric contour of an aircraft shell, interference of a rotation offset delta phi on a magnetic field is limited through optimal design of a coverage angle theta, and magnetic flux is concentrated by a ferrite array on the back side to improve the coupling efficiency. The system does not need complex mechanical adjustment, realizes energy transmission through magnetic field coupling, keeps stable efficiency in seawater, fresh water and air environments, has the advantages of compact structure, high offset resistance, wide medium adaptability and high expandability, and remarkably improves the reliability and cruising ability of wireless charging of the underwater vehicle.

The invention relates to the technical field of data processing, in particular to a deep learning fusion-based marine hydro-meteorological data fusion processing method, which comprises the following steps: S1, data acquisition: monitoring marine hydro-meteorological data through a distributed marine monitoring network; s2, constructing a regional collaborative fusion network; s3, space-time consistency optimization: introducing a multi-scale space-time attention mechanism, capturing a local short-term change and a global long-term trend which exist in the data at the same time, and dynamically adjusting weights of different time scales; and optimizing a data fusion result by taking marine physical characteristics as constraint conditions, wherein the marine physical characteristics comprise tide and ocean current periodicity laws. According to the method, through introduction of a multi-scale space-time attention mechanism, accurate capture of short-term dynamic change and long-term trend is realized on the basis of global fusion data, and through a dynamic time window adjustment mechanism, the method can adapt to change characteristics of different time scales.

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 discloses an anti-ocean-current trajectory tracking control method and system for a jet-arm submarine cable laying robot. The method comprises the steps that an anti-ocean-current motion model of the jet-arm submarine cable laying robot is constructed; acquiring a position error and a course tracking error according to a given reference trajectory based on the ocean current resistance motion model, and constructing a preset time controller; according to the preset time, the controller obtains a virtual controller of the jet arm seabed cable laying robot so as to define a longitudinal speed error and an angular speed error; constructing a parameter adaptive law according to the longitudinal velocity error and the angular velocity error; and constructing a disturbance observer based on the virtual controller and the parameter adaptive law to obtain an actual controller for ocean current trajectory tracking of the jet arm submarine cable laying robot. Aiming at the problem that an existing submarine cable laying robot cannot realize accurate trajectory tracking in a complex submarine environment, a more perfect ocean current resistance motion model is established, and a disturbance observer is introduced to estimate disturbance in real time so as to realize accurate control; a preset time theory is adopted, so that the system can be converged to a preset time, and the robustness is better; the disturbance observer is used to more accurately compensate the interference of ocean current of the seabed, and the anti-interference capability of the submarine cable laying robot is improved.

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 provides a multi-physical-field collaborative generation experiment method for a floating tunnel truncation model, and belongs to the technical field of floating tunnel tests, and the method can efficiently and accurately simulate the multi-field coupling effect of waves, ocean currents and earthquakes. And a reliable experiment means is provided for researching the dynamic response and the safety performance of the floating tunnel structure under the complex environmental load. The experimental method comprises the steps of truncation design of a full-scale ultra-long floating tunnel prototype, design of a reduced scale floating tunnel truncation model, construction of a wave-flow-earthquake multi-field input system and collection of dynamic response of the reduced scale floating tunnel truncation model. According to the invention, stable lateral earthquake input can be realized based on the existing wave-flow water channel, the influence of the earthquake effect transmitted from the cable to the tension leg type suspension tunnel pipe body on the dynamic response can be simulated, and the coupling effect of wave, flow and earthquake can be reproduced; and a wave-flow-earthquake multi-field collaborative generation experiment scheme is provided for the ultra-long tension leg type floating tunnel truncation model.

The invention discloses an optimal heading control method for a digital-analog cooperative driven AUV (Autonomous Underwater Vehicle) in a three-dimensional unknown environment, which comprises the following steps of: firstly, establishing a three-dimensional six-degree-of-freedom kinematics model and a dynamics model of an under-actuated AUV in an unknown time-varying ocean current environment, and designing a sliding mode controller; secondly, designing a compensation scheme of a PPO reinforcement learning algorithm according to interference force suffered by the sliding mode controller in a three-dimensional unknown ocean current environment, constructing a state space, an action space, a reward function, an actor network and an evaluator network, and training the AUV; and after training is completed, reinforcement learning outputs disturbance force compensation in sliding mode control in real time according to the position and speed of the under-actuated AUV under the influence of the current three-dimensional unknown time-varying ocean current, and a sliding mode controller outputs a final control quantity. According to the method, the precision and robustness of sliding mode control in a complex ocean current environment are improved, and environment optimal heading positioning control of the autonomous underwater vehicle in a three-dimensional space is realized.

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 technical field of ship navigation, and discloses a ship navigation route optimization method and system. The method comprises a meteorological change sequence, an ocean current fluctuation sequence and channel obstacle distribution data. Then carrying out two-dimensional route topology construction processing to generate route distribution characteristics; calling a pre-trained path optimization model to carry out nonlinear path analysis processing to obtain an optimal route prediction value and a key risk area identifier; performing navigation environment compensation correction processing on the optimal route prediction value based on the association relationship between the meteorological change sequence and the ship control sensitivity to generate a corrected optimal route prediction value; and finally, generating a course maintenance strategy set including a course adjustment path optimization scheme and a speed dynamic regulation and control processing scheme according to the key risk area identifier. According to the method, dynamic environmental factors can be comprehensively processed, the accuracy and adaptability of route planning are improved, risks are effectively identified and avoided, and high efficiency and safety of ship navigation are guaranteed.