378 results about "Navigation safety" patented technology

The invention discloses an automatic obstacle avoidance method and system for an unmanned ship based on Beidou artificial intelligence, and relates to the field of computer vision, and the method comprises the steps: collecting a visible light image and a near-infrared image of a water surface environment, and carrying out the reflection suppression processing; performing feature fusion on the image to generate an environment perception graph; obstacle features are extracted through a cascade cavity convolution structure, and an obstacle pixel-level semantic segmentation result is obtained; constructing an obstacle motion prediction model, and obtaining a predicted obstacle motion track; acquiring motion track data of the unmanned ship based on Beidou positioning and electronic chart data; establishing a space-time collision detection model, calculating collision parameters, and generating a collision data set; an obstacle avoidance path is generated through a dynamic situation field method, and a rudder angle instruction and a propeller rotating speed instruction are obtained. The unmanned ship has the advantages that accurate sensing, motion prediction and intelligent obstacle avoidance of the unmanned ship on water surface obstacles are realized through Beidou positioning and technology, and the sailing safety and intelligent level are improved.

The invention discloses a channel flow velocity measurement error compensation method based on multi-source fusion, and the method comprises the following steps: S1, synchronously collecting flow velocity, position, attitude and environment data, including ADCP radial flow velocity, GNSS position / speed and IMU attitude angular velocity, through a multi-source sensor, aligning multi-source data based on a time synchronization mechanism, and achieving microsecond time synchronization based on PPS time service; according to the method, RTK high-precision positioning and ADCP bottom tracking data are integrated, the water surface elevation and the water bottom distance are inversed in real time, and the actual water depth and the riverbed erosion and deposition rate are dynamically calculated; edge calculation, motion compensation and external compass correction technologies are fused, the flow velocity and flow direction measurement precision is improved, and platform motion and attitude interference is eliminated; multi-source data local fusion processing and real-time generation of a flow velocity thermodynamic diagram and a water level change curve are realized, remote transmission and visualization are supported, and comprehensive and accurate hydrological data support is provided for maritime affair intelligent supervision, navigation safety early warning and digital channel construction.

The invention relates to the technical field of computer vision, in particular to a method for generating a blind person navigation obstacle avoidance map. The method comprises the following steps: driving a blind guiding robot to a preset inspection point, and collecting a surrounding environment image in real time; generating a three-dimensional point cloud according to the environment image, and constructing an environment map; extracting feature points based on the surrounding environment image, performing feature matching, and outputting a feature matching result; evaluating an inter-frame motion state by using a feature matching result; determining pose features of the blind guiding robot according to the inter-frame motion state; semantic segmentation is carried out by using the pose features, and an obstacle area is determined; acquiring an obstacle image of the obstacle area, and fusing the obstacle image with the surrounding environment image to generate an obstacle map; and constructing a dynamic occupancy grid based on the obstacle map. According to the invention, real-time updating and intelligent obstacle avoidance functions of the blind navigation map are realized based on the computer vision technology, and the navigation safety and the path planning accuracy are improved.

The invention relates to the technical field of ship navigation risk assessment, in particular to a ship navigation risk assessment system based on multi-source heterogeneous data fusion, which comprises a multi-source data integration module, a spatial-temporal feature mapping module, a dynamic risk detection module, a linkage decision control module and a feedback optimization module. According to the method, standardized operation data is generated through multi-source data cleaning and fusion, a spatial-temporal feature distribution map is generated by using a multi-dimensional dynamic clustering algorithm, a risk index set is extracted in combination with adaptive boundary adjustment and a nonlinear optimization algorithm, and accurate path planning and real-time regulation are realized. In addition, a global sensitivity analysis framework and an early warning module are introduced into the system, and the ship navigation safety and reliability are improved. According to the method, the risk prediction accuracy can be remarkably improved, the navigation accident probability is reduced, the navigation efficiency is optimized, and safe operation of the ship is guaranteed.

The invention discloses an AGV navigation system used in a storage dynamic environment, relates to the technical field of storage robot control, and is used for solving the problem of poor stable navigation in dynamic storage. According to the method, a closed-loop navigation link of state convergence, confidence evaluation, corridors, setting generation and cooperative gating is constructed, state frames are packaged in unified time, and positioning confidence and availability marks are generated based on the consistency of pose predictive quantity and pose observed quantity, so that credible hierarchical control is realized in shielding, light reflection and personnel forklift passing scenes, and the positioning accuracy is improved. A time sequence occupation constraint set is formed through obstacle tracking, a forbidden area mark is fused, a passable corridor boundary is generated, a speed set value sequence and a curvature set value sequence are generated in a corridor, acceleration change rate constraint and steering change rate constraint are applied, and stable driving control and steering control instructions are output. And the way-giving sequence and the recovery sequence are matched, and the execution state and the occupation state are written back, so that the navigation safety, the stability and the operation continuity are improved.

The invention provides a combined fleet steering cooperation method and system based on intelligent control, and relates to the technical field of data processing, and the method comprises the steps: calculating a cooperative steering deviation value of an actual track and a preset reference path; if the collaborative steering deviation value exceeds a preset collaborative deviation threshold value, a real-time safe navigation boundary is generated through a universal dynamic point set convex hull updating algorithm; calculating a hydraulic pressure correction parameter and a rudder angle compensation amount according to the spatial position relationship between the cooperative steering deviation amount and the real-time safe navigation boundary; and converting the hydraulic pressure correction parameter into a pressure regulation instruction, and driving a pressure self-adaptive regulation mechanism of the hydraulic system to execute a compensation steering action. According to the invention, the steering deviation is reduced, and the navigation safety in a complex environment is improved.

The invention discloses a ship navigation safety risk identification method based on multi-source environment information, and the method specifically comprises the following steps: S1, constructing a ship navigation state knowledge graph, obtaining the basic data of ship navigation, and carrying out the building of the ship navigation state knowledge graph through the preprocessed basic data; s2, acquiring real-time navigation state data of the ship, and performing comprehensive safety risk identification according to the real-time navigation state data of the ship; and S3, optimizing a ship navigation strategy according to a comprehensive safety risk identification result, and finally issuing a ship end to carry out an auxiliary driving decision. According to the method, ship navigation basic data is obtained through the shore-based platform, and a data basis is provided for the platform to identify ship navigation environment risks; risk identification is carried out based on basic data obtained by a shore end, a navigation optimization strategy is provided in combination with a ship navigation state, the navigation optimization strategy is issued to a ship end handheld terminal for alarm reminding, and advanced alarm and navigation optimization are carried out on environmental safety risks existing in ship navigation.

The invention discloses a rotor unmanned aerial vehicle visual navigation method and system for an unknown forest region, and the method comprises the steps: firstly, combining a depth camera with a Sobel gradient filtering mechanism, and effectively eliminating interference information; secondly, pixel-level segmentation and three-dimensional positioning of the trunk are realized by segmenting a cascade model of a convolutional network and a residual network, visual information is accurately mapped to a world coordinate system, and the problem of positioning drift caused by similar textures in a forest scene is solved; based on starting point-target point-terminal point minimum jerk trajectory planning, a smooth trajectory is generated through polynomial optimization and quadratic programming, flight jitter is inhibited fundamentally, and the mechanical loss is reduced while the image acquisition quality is guaranteed; and finally, through real-time target point updating and trajectory dynamic correction of the depth camera, a sensing-planning-execution closed loop is formed, so that the unmanned aerial vehicle has dynamic obstacle avoidance and path online optimization capabilities, autonomous crossing is finally realized, and navigation safety and stability in a complex environment are significantly improved.

The invention relates to the technical field of ship navigation safety, in particular to a real-time ship trajectory tracking and early warning method, which comprises the steps of multi-source data acquisition and preprocessing, trajectory prediction model construction, collision risk assessment, early warning triggering and the like. By integrating AIS, radar and meteorological data, accurate trajectory prediction is realized by adopting Kalman filtering and extended Kalman filtering algorithms, and the method adapts to a complex environment based on dynamic weight factors and fuzzy logic control. Meanwhile, a multi-level early warning mechanism and redundant communication design are provided, and timely transmission of early warning information is ensured. The collision risk assessment model is optimized by using the deep neural network, and the early warning precision is improved. The real-time performance, accuracy and dynamic adaptability of ship trajectory tracking and early warning can be improved, and the efficient and safe requirements of modern shipping are met.

The embodiment of the invention discloses a low-altitude wind shear early warning method and device based on multi-source meteorological data. A specific embodiment of the method comprises the steps of generating a first region of interest for a target region according to a contour topographic map; determining a second region of interest according to the real-time rainfall distribution diagram and the real-time air temperature distribution diagram; performing region optimization on the first region of interest according to the real-time wind field graph to obtain a third region of interest; generating a fourth region of interest according to the second region of interest and the third region of interest; and according to the fourth region of interest and a pre-trained low-altitude wind shear prediction model, generating wind shear region early warning information. According to the embodiment, accurate prediction of the wind shear is realized, so that early warning is performed on the low-altitude aviation equipment, and the navigation safety of the low-altitude aviation equipment is ensured.

The invention relates to the technical field of ship intelligent navigation, and provides a dynamic rendering engine implementation method and system based on multi-source data fusion, and the method comprises the steps: collecting multi-source data in real time, carrying out the preprocessing, and obtaining multi-modal data; performing time alignment and coordinate transformation on the multi-modal data through space-time registration, performing prediction through adaptive Kalman filtering, calculating a residual error according to prediction data, and performing sensor anomaly detection according to the residual error; carrying out weighted fusion according to the state of the sensor, carrying out Mercator projection transformation on fused data to obtain rendering coordinates, and carrying out detail level marking according to the rendering coordinates; performing alarm judgment according to the detail level marks, and performing rendering sorting according to the alarm judgment; and carrying out load detection and rendering parameter adjustment through a frame rate control module according to the rendering sequence. According to the invention, the whole process optimization from data acquisition and processing to visual display is realized, the navigation precision and reliability are improved, and the ship navigation safety is improved.

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 a stern shaft sealing device for a ship, and relates to the technical field of ship equipment. The device comprises a tail shaft barrel, a tail shaft, a sediment intercepting assembly, a first sealing unit and a pipeline system. And a sealing structure is arranged in a gap between the stern shaft and the stern shaft barrel. The first sealing unit comprises four sealing leather cups which are arranged in the axial direction and divide the interval into a first installation cavity, a gas cavity, a second installation cavity and a cooling liquid cavity, and the pipeline system is communicated with the gas cavity and the cooling liquid cavity so as to maintain pressure. The core is a buffer structure in the first mounting cavity and the second mounting cavity, and the buffer structure comprises a sealing ferrule, an adjusting assembly and an abutting assembly. When the seawater pressure suddenly increases, the buffer structure converts impact kinetic energy into elastic potential energy and gas compression potential energy through elastic deformation, pressure conduction and the like, pressure buffering is achieved, meanwhile, the sealing performance is dynamically enhanced, seawater backflow is prevented, the ship navigation safety is guaranteed, and the reliability and stability of the sealing device are improved.

According to the SARIMA prediction-based inland river navigation draught optimization management and control method for the ship with the overrun draught, a multi-source time sequence data set is obtained and preprocessed to obtain an input time sequence set, and the input time sequence set is processed through a constructed SARIMA model to obtain a water level prediction sequence; a ship dynamic draught prediction sequence is obtained through the constructed ship dynamic draught model based on water level prediction sequence processing, a draught safety margin judgment operator is constructed to carry out draught safety margin evaluation and risk grading, and finally a draught optimization management strategy is solved through the draught optimization management model and issued. A ship draught control closed-loop chain of prediction-calculation-evaluation-optimization-feedback is formed, through deep coupling of SARIMA time sequence prediction, ship dynamic draught calculation and draught optimization management, prospective recognition and dynamic regulation and control of draught safety margin of a draught overrun ship are achieved, and under the premise of guaranteeing navigation safety, the ship draught safety margin is effectively controlled. And the water depth utilization efficiency of the inland waterway and the ship loading efficiency are greatly improved.

The invention provides an ice layer thickness identification and stress sensing cooperative navigation monitoring method and system, and relates to the technical field of ship navigation safety monitoring. The method comprises the following steps: acquiring and preprocessing sea ice images around a ship in real time; a self-built data set is made through labeling, a deep learning model is selected for segmentation, segmentation results of the snow accumulation layer and the ice layer are obtained, mask pixel values of the snow accumulation layer and the ice layer are obtained, and then the actual thickness is calculated; acquiring real-time stress data; using a timestamp synchronization mechanism to match the sea ice image of the time corresponding frame and the stress data; carrying out the normalization processing of the sea ice image and the stress data, carrying out the weight fusion of the normalized data, and calculating a comprehensive risk coefficient; a risk early warning level is output according to the interval where the coefficient is located; and when the coefficient exceeds a set threshold value, sound-light alarm and interface warning information are sent out. According to the invention, through collaborative analysis and risk assessment of multi-dimensional data, the sailing safety of the ship in the polar region and the sea ice region is improved.

The invention discloses an intelligent decision-making method based on a maritime collision avoidance rule, and belongs to the technical field of intelligent ship collision avoidance decision-making, and the method comprises the following steps: S10, setting a global route of a ship autonomous cruise task; s20, obtaining ship positioning and environment perception prediction data; s30, acquiring a ship local reference trajectory through a local trajectory calculation module; s40, constructing a ship navigation encounter situation model; s50, constructing a multi-element composite collision avoidance risk assessment model; s60, constructing a behavior decision tree model based on a marine collision avoidance rule; s70, a trajectory planning module performs trajectory planning based on a behavior decision tree model result; s80, establishing a monitoring continuous decision operation mechanism; according to the invention, on the premise of guaranteeing the safety and efficiency of ship navigation, on the premise of complying with the International Marine Collision Regulation, the interactive collision avoidance behavior between the autonomous ship and the manned ship in a complex and changeable sea surface environment is explored, so that a rapid and reasonable collision avoidance decision is expected to be realized, and the navigation safety and efficiency are improved.

The invention discloses a digital twinning-based multistage water-saving ship lock navigation safety guarantee method and system, and the method comprises the steps: collecting multi-source sensing data, generating an alignment data flow and a quality evaluation report, and updating a digital twinning state; by means of the aligned data flow and the quality evaluation report, through non-contact observation and probability fusion, mooring force probability distribution is obtained; based on the digital twinning state and the alignment data flow, identifying a hydraulic impulse response and adopting hydraulic phase lock tracking to generate a vibration suppression opening template; and in the digital twin system, integrating the mooring force probability distribution and the vibration suppression opening template, executing distribution robust random control barrier optimization, and outputting an executable valve track. According to the method, water hammer oscillation can be actively inhibited, robust safety guarantee is provided for uncertain mooring force, and the navigation safety of the large water-saving ship lock is effectively improved.

The invention discloses a dynamic isobath generation method combining super-resolution water depth and real-time water level, and belongs to the technical field of marine surveying and mapping. Extracting sparse production water depth data from a standard electronic chart product, and converting the sparse production water depth data into a low-resolution water depth digital elevation model under a unified coordinate system; performing super-resolution reconstruction on the low-resolution water depth digital elevation model by using a deep learning super-resolution model to generate a high-resolution water depth digital elevation model; acquiring real-time water level data; carrying out fusion calculation on the high-resolution water depth digital elevation model and the real-time water level data to obtain an actual water depth digital elevation model; and extracting a dynamic isobath from the actual water depth digital elevation model. According to the method, the problem that the static isobath in the existing electronic chart cannot respond to the real-time water level change is solved, full-automatic generation from sparse water depth data to the high-precision dynamic isobath is realized, and the water depth information accuracy and the navigation safety guarantee capability of the electronic chart are remarkably improved.

The invention provides an offshore area navigation safety control and optimization method, which comprises the following steps of: 1, acquiring multi-source observation data of an offshore navigation target, and preprocessing the multi-source observation data to obtain a space-time observation association diagram; 2, evaluating the dynamic credibility of each observation node by using a multi-modal consistency graph neural network; constructing an adaptive weighted factor graph model according to the obtained dynamic credibility, and solving a system state to obtain an optimal fusion track, a corrected environmental parameter and a posterior error covariance matrix; 3, constructing a regional risk field based on the optimal fusion track, the corrected environmental parameters and a posterior error covariance matrix; 4, on the basis of the regional risk field, a multi-target collaborative optimization model is constructed and solved, and navigation control parameters are obtained; and 5, executing the navigation control parameters, and performing adjustment and optimization by adopting multi-level closed-loop feedback to complete the navigation safety control and optimization of the offshore area.

According to the ship power device cooling water temperature intelligent control system, the water temperature is monitored in real time through the temperature sensor arranged at the cooling water inlet, and the electric three-way flow regulating valve with the stepping motor is used for changing the mixing proportion of low temperature and bypass fresh water for control; a cooperative control framework is adopted; an upper-layer model prediction control module performs rolling time domain optimization based on a time-delay thermodynamic model which is identified and updated on line, and outputs a feedforward signal; and the lower-layer fuzzy self-adaptive PID controller dynamically adjusts parameters according to the temperature deviation and the change rate of the temperature deviation to generate compensation control quantity. And the two outputs are linearly superposed and then converted into a valve accurate action signal through the driving module. The cooperation method effectively fuses the feedforward optimization capability of the MPC and the strong robustness of the fuzzy PID to model mismatch and disturbance, significantly improves the control precision and stability of the water temperature of the main engine under the inertia and time-varying working conditions, reduces the equipment damage caused by temperature fluctuation, prolongs the service life of the main engine, and improves the navigation safety and the energy efficiency.

The invention provides a ship berthing and leaving early warning method and system, and relates to the field of port ship management, and the method comprises the steps: obtaining ship position data, converting the ship position data into distribution density, extracting time sequence features, building a motion prediction relation, calculating a congestion risk coefficient, constructing water area risk distribution, and carrying out the route crossing calculation to obtain an avoidance demand value. And determining an avoidance sequence, calculating an avoidance route and a navigation time window, and generating time-sharing avoidance early warning information containing an early warning level and an avoidance instruction. The ship congestion risk can be predicted in advance, the navigation time is reasonably planned, and the navigation safety and efficiency of the port water area are effectively improved.

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 provides a composite feedforward cooperative control method and system for propulsion of a controllable-pitch propeller of a ship. The method comprises the following steps: establishing a propulsion composite model comprising a ship load inquiry function model and a controllable-pitch propeller propulsion cooperative control model; identifying ship resistance characteristics in real time through a load inquiry model, predicting main engine power and propeller rotating speed requirements at different navigational speeds, and calculating a main engine power margin; setting a host security threshold based on the maximum persistent power margin; within the allowable range of the safety threshold value, three cooperative control modes are selected and executed in a self-adaptive mode according to the navigation working condition, wherein the three cooperative control modes include the pitch-accelerator joint control mode under the constant rotating speed of a main engine, the main engine rotating speed-accelerator joint control mode under the fixed pitch of a controllable-pitch propeller and the controllable-pitch propeller rotating speed-pitch joint control mode under the constant power of the main engine. According to the invention, through feedforward prediction and multivariable cooperation, navigational speed closed-loop stable control is realized, the problems of host overload, black smoke emission, high exhaust temperature and large control fluctuation are fundamentally avoided, and fuel economy and all-condition navigation safety are improved.

The invention discloses an intelligent navigation mark and ecological environment cooperative monitoring system for an offshore wind power area, and relates to the technical field of offshore monitoring, the system comprises a plurality of intelligent navigation mark nodes deployed in an offshore wind power plant, at least one gateway node and a monitoring center, the intelligent navigation mark nodes integrate a plurality of monitoring and communication modules, the system maintains a virtual resource map through a gateway node or a specified node, and records information such as a node geographic position, link quality, residual energy and a dynamic role; the intelligent navigation mark nodes periodically broadcast heartbeat packets and dynamically switch roles, an optimal data transmission path tree is initially constructed, and local network reconstruction is started when the nodes fail or links are interrupted; according to the invention, the adaptability and the anti-damage and disaster-tolerant capability of the network are improved, the cooperative monitoring of the navigation safety and the ecological environment is realized, and the reliable transmission of key data is ensured.

The invention discloses a ship overwater and underwater integrated obstacle detection method and system based on multi-source data fusion, and belongs to the technical field of ship intelligent navigation and navigation safety guarantee. According to the method, original data of overwater and underwater multi-source sensors are collected through a distributed sensing layer, and after preprocessing and space-time synchronization, core algorithms such as JPDA + EKF overwater target fusion, RANSAC three-dimensional model fitting cross-medium obstacle recognition and Bayesian updating of a 3D occupation grid map are adopted to realize integrated fusion and risk assessment of the overwater and underwater environments, so that the risk assessment of the overwater and underwater multi-source sensors is realized. And finally outputting a 3D fusion situation map containing the obstacle three-dimensional structure, the semantic information and the risk level. The corresponding system comprises a sensing layer, a fusion processing layer and an application layer, and all the layers cooperatively achieve cross-medium, high-precision and high-reliability obstacle detection. According to the method, the overwater and underwater sensing boundary is broken through, the problems that a traditional system is split in sensing, insufficient in capacity and one-sided in situation understanding are solved, and a sufficient basis is provided for intelligent ship collision avoidance decision making.

The invention belongs to the technical field of navigation safety analysis, and particularly discloses an intelligent navigation mark and AIS (Automatic Identification System) data collaborative navigation safety improvement method, which comprises the following steps: acquiring a navigation channel image through a navigation mark visual sensor, preprocessing the navigation channel image, and generating an initial detection frame through multi-scale detection; an NMS threshold value is dynamically configured according to the physical size of the ship, and a final detection frame with a physical size label is output; extracting ship visual feature vectors, distributing feature updating strategies for multiple ships according to physical sizes, predicting positions through historical tracks when the ships move out of a visual field, and triggering re-identification; and finally, a visual tracking result and real-time AIS data are fused, and a channel risk early warning and navigation strategy is generated. According to the method, the problems of missing of a front-end sensing layer, missing detection caused by the fact that an NMS mechanism is not involved, shielding of target re-identification design and re-identification trigger logic blank and the like in the prior art are solved in a targeted manner, and the accuracy and reliability of navigation safety guarantee are comprehensively improved.

The present invention discloses a water surface vehicle sailing safety verification system and operating method thereof. Specifically, the water surface vehicle sailing safety verification system and operating method thereof has a calculating unit, a storage unit and an output unit. The calculating unit uses data from the storage unit to generate obstacle information, and the output unit transmits the obstacle information to a sailing control module which is on a real water surface vehicle. The sailing control module controls the sailing of the real water surface vehicle for obstacle collision avoidance test via hardware-in-the-loop.

The invention provides an inland river navigation collision avoidance method and system fusing improved A * and a dynamic window method, and the method comprises the following steps: 1, constructing a two-dimensional grid map of an inland river navigation environment, setting a starting point and an ending point of ship navigation, and determining the distribution information of obstacles in the map; 2, performing global path planning based on an improved A algorithm; step 3, optimizing the global path; 4, performing local planning and fusion based on a dynamic window method DWA; and step 5, repeating the step 4 until the local target point is an end point, and outputting a final navigation path to realize autonomous collision avoidance of the inland ship. According to the technical scheme, navigation safety, efficiency and environmental adaptability are considered.

The invention provides an intelligent port and navigation AI application system under digital twin enabling, and belongs to the technical field of artificial intelligence application systems. The system comprises an inland river exclusive AI model module, a strong noise voice interaction module, an instruction-scene mapping interaction module and a digital twinborn fusion module. And the inland river exclusive AI model module is used for realizing real-time sensing and accurate decision-making of the navigation channel situation. According to the system, an inland river exclusive AI model module, a strong noise voice interaction module, an instruction-scene mapping interaction module and a digital twinborn fusion module cooperate with each other; real-time sensing and accurate decision making of channel situations, effective voice interaction in a strong noise environment, accurate mapping of voice instructions and virtual scenes and fusion of a high-precision channel model, meteorology and monitoring are achieved, and the problems that traditional inland river shipping management is low in efficiency, inconvenient to interact and poor in scene restoration degree are effectively solved through the design. And the inland river shipping efficiency is improved, the navigation safety is guaranteed, and the operation cost is reduced.

The invention relates to the field of path planning, and discloses an unmanned surface vehicle track planning method and device and a medium, and the method comprises the steps: obtaining the real-time dynamic water area environment information of an unmanned surface vehicle; constructing a decision model of unmanned surface vessel track planning, and adopting an improved deep reinforcement learning SAC method based on strategy divergence to train and iteratively solve the decision model to obtain an optimal approximation strategy, namely a trained decision model; and inputting the real-time dynamic water area environment information into a trained decision model to obtain an optimal execution action of the unmanned surface vessel, and completing route planning. According to the method, a constraint perception adaptive entropy adjustment mechanism and a security layer verification module are innovatively introduced, so that the algorithm actively perceives and avoids risks in each stage of training and execution, the multi-target balance problem is successfully solved, and the USV autonomous operation capability and the navigation safety level are remarkably improved.