690 results about "Collision risk" patented technology

The invention relates to the technical field of intelligent building data processing, and provides a BIM-based bridge collision detection method, which is used for solving the problem of collision early warning lag caused by space-time dislocation of a static model and real-time monitoring data in bridge construction. A comprehensive point cloud data set with a unified space-time reference is generated through multi-source data dynamic fusion and a quantum optimization algorithm, a self-evolutionary dynamic BIM model is constructed, and digital mapping of millimeter-level precision of a construction scene is achieved. A collision risk prediction model is constructed based on causal reasoning and multi-physics field coupling, a multi-dimensional relationship among construction parameters, environment variables and structural response is associated, and a linkage effect caused by mechanical motion is predicted. According to the method, the time dimension limitation of a traditional static model is broken through, full-process closed-loop control of perception-analysis-decision-verification is formed, the real-time performance of collision early warning and the controllability of construction safety are remarkably improved, and the method is suitable for high-precision construction scenes such as complex bridge rotation and cantilever casting.

The invention discloses an intelligent maritime affair security situation awareness system and method, and relates to the technical field of path planning, and the method comprises the steps: obtaining multi-source ship data, carrying out the space-time dimension alignment and feature weight distribution, generating a fusion feature tensor, extracting a spatial topological relation through a graph convolution operation, and capturing a time dependence feature through an LSTM model. Constructing a ship interaction potential energy field model in combination with the spatial topological relation and the time dependence features, calculating a collision risk index in combination with a conflict cone algorithm, generating a dynamically updated sea area risk thermodynamic diagram, and performing situation map construction on the sea area risk thermodynamic diagram and the fusion feature tensor to obtain a dynamic situation map; a ship-environment-event triple relationship is extracted by adopting a hierarchical graph attention network, an interpretable semantic situation map is constructed, a threat matrix is generated through node-level risk assessment in sequence, and an air route planning scheme is obtained through regional-level path optimization; high-risk areas can be avoided, and navigation safety and efficiency are improved.

The invention relates to a ship safety monitoring method, system and device based on data fusion, and relates to the technical field of ship navigation safety, and the ship safety monitoring method comprises the steps: collecting multi-source heterogeneous data; preprocessing the multi-source heterogeneous data; carrying out fusion processing on the multi-source heterogeneous data based on an information entropy theory; a ship safety domain model is constructed, trajectory prediction is executed, a collision risk index is calculated, and the ship collision risk is evaluated; and performing anomaly detection on the ship equipment operation data based on manifold learning and a thermodynamic model. According to the invention, a multi-source heterogeneous data fusion technology is adopted, multi-dimensional data sources are integrated, and omnibearing ship safety situation awareness is constructed. The early warning time is prolonged to 8-10 minutes, which is far better than the early warning time of 3-5 minutes in the prior art. The system thoroughly solves the problem that subsystems in a traditional ship monitoring system operate independently and do not communicate with one another, the crew information acquisition cost is reduced, and the number of times of interactive operation is reduced by 42%.

The invention relates to the technical field of equipment control, and provides an autonomous tracking anti-interference control method and system.The confidence coefficient of a sensor is determined by combining the historical precision of the sensor and the current signal quality through a sensing recognition module, and the fusion weight is dynamically adjusted based on the confidence coefficient, so that the target tracking state can accurately reflect the actual motion characteristics of a target; the trajectory planning module takes an interference type, an interference degree and a target tracking state as input parameters cooperatively, and combines an extended state space and a reward function containing an anti-interference reward, so that an explored tracking trajectory can actively adapt to an interference scene; meanwhile, the predicted collision probability is compared with a probability threshold value, so that the global updating triggering opportunity is ensured to be accurate, and the collision risk caused by an unreasonable track is effectively avoided; and the control optimization module generates a targeted feed-forward compensation amount according to the interference type and the interference degree, and generates a control instruction after superposing the basic control amount, thereby realizing cooperation of anti-interference compensation and trajectory tracking control.

The invention relates to the field of channel management, in particular to an intelligent channel management method and system based on multi-source data fusion. The invention discloses an intelligent navigation channel management system based on multi-source data fusion. The system comprises a combined ship identification module, a positioning frequency updating module, a collision risk assessment module and an intervention management module. According to the invention, the system can sense multi-dimensional environmental parameters such as illumination intensity, visibility, AIS signal quality, radar echo signal-to-noise ratio and the like in real time by constructing a combined ship identification model containing AIS, radar, vision and infrared, and dynamically adjust the fusion weight of each sensor to compensate for the deficiency of a single sensor; according to the method, the robustness of ship dynamic monitoring is improved, high-precision tracking can be kept in severe weather, the data redundancy contradiction of a traditional multi-sensor system is avoided, and therefore continuous and stable target state input is provided for follow-up risk analysis.

The invention provides a ship general assembly construction safety quality real-time monitoring method based on AI image recognition and a storage medium, and the method comprises the steps: building a time-space aligned multi-modal data set through collecting and preprocessing multi-source heterogeneous data such as a high-definition visible light image, an ultrasonic scanning signal, infrared temperature data and three-dimensional point cloud data; based on the data set, a multi-modal deep learning model is constructed, texture, time sequence and temperature features are automatically extracted, and the features are fused through an attention mechanism. The model can identify potential quality hazards in the ship construction process, monitor safety problems, predict collision risks and output a real-time monitoring report. By means of comprehensive analysis of multi-source data, the accuracy and timeliness of monitoring are enhanced, powerful support is provided for management decision, and resource allocation and emergency response are improved.

The invention discloses an offshore platform anti-collision device and method based on multi-mode BEV perception, and the device comprises a three-dimensional fusion distance measurement module, a UWB distance measurement module, a ship navigation state monitoring module, a platform marine environment monitoring module, and a central processing unit. The three-dimensional fusion ranging module comprises a holder, a laser radar and a camera, and is used for performing 360-degree three-dimensional scanning on the periphery of the offshore platform; the UWB distance measuring modules are installed on the periphery of a ship and pile legs of an offshore platform. The ship navigation state monitoring module is used for acquiring ship position, navigational speed and course information in real time; the platform marine environment monitoring module collects wind wave data and weather data near the offshore platform and route information of passing ships through network communication. The invention discloses an offshore platform anti-collision device with real-time monitoring and collision risk assessment functions, the anti-collision safety of an offshore platform is improved, and the occurrence rate of collision accidents is reduced.

The invention provides a ship collision risk analysis method and system based on a fault tree and a Bayesian network, and relates to the technical field of ship safety. The method comprises the following steps: determining that main risk factors are human factors, ship factors, environment factors and management factors by analyzing ship collision accident cases; taking ship collision as a top event, performing deductive reasoning according to occurrence logic, and constructing a fault tree structure; the mapping relation between the fault tree and the Bayesian network is utilized, the Bayesian network of ship collision is established, and basic events, middle events and top events correspond to root nodes, middle nodes and leaf nodes respectively; the prior probability of a root node and the conditional probability of an intermediate node are determined through statistical analysis of main risk factors, and a Bayesian network model is formed; based on the model, a collision risk rate is calculated through forward reasoning, key risk factors are diagnosed through backward reasoning, and a main risk path causing collision is identified. According to the scheme, the complex relation between the risk factors can be accurately and objectively subjected to deep analysis and quantitative description.

The invention discloses a bridge ship collision early warning system based on artificial intelligence, and relates to the field of intelligent traffic infrastructure safety protection. The method is used for solving the problems of difficulty in multi-source data fusion and low risk identification accuracy in a complex water area. The method comprises the following steps: firstly, uniformly mapping a radar point cloud, an AIS signal and a visual trajectory to a coordinate system taking a pier as a center through a Lie group transformation algorithm, and constructing a ship motion state tensor; then, ship behavior characteristics are extracted through a space-time diagram convolutional network, and intention probability distribution and behavior deviation degree are output; thirdly, an orthogonal constraint variational auto-encoder is adopted to decouple the autonomous control behavior and the environment disturbance factor, and a collision risk quantized value is generated according to the norm ratio of the autonomous control behavior and the environment disturbance factor; and finally, constructing a dynamic alarm threshold value according to the behavior deviation degree and the disturbance characteristics, realizing graded early warning, and driving a behavior pattern library to update through a feedback mechanism to form a self-adaptive closed-loop early warning system.

The invention discloses an intelligent ship autonomous collision avoidance method based on COLREGs and a DDPG algorithm, and relates to the technical field of an intelligent ship technology and an autonomous collision avoidance algorithm, and the method comprises the steps: building an intelligent ship kinematics model based on the motion parameters of a ship in a north-east coordinate system; aIS, radar and visual information are fused, COLREGs rule constraints are embedded, an environment model is constructed, and a ship size safety threshold value is calculated. According to the method, COLREGs and a DDPG algorithm are combined, a high-precision environment model is constructed by utilizing multi-source sensing data, and an algorithm structure is optimized aiming at a typical collision avoidance scene, so that the intelligent ship can automatically identify the meeting situation type, determine the way-giving responsibility or the direct navigation obligation and generate an optimal collision avoidance path in a complex marine environment; compared with a traditional collision avoidance method depending on manual driving and fixed rules, the autonomous collision avoidance capability of the ship in dynamic and complex ocean traffic scenes is remarkably improved, and the collision risk caused by human factors is reduced.

The invention discloses a large-scale ocean data buoy safety monitoring system and method based on multi-source fusion, and relates to the technical field of buoy monitoring. Buoy equipment operation parameters, ocean environment data and surrounding ship dynamic information are synchronously acquired through a buoy body sensor group, an environment monitoring unit and a ship identification module; time-frequency domain feature extraction and anomaly detection are carried out on the collected data, and a collision risk dynamic evaluation model is constructed in combination with ship trajectory prediction; establishing an equipment health, environment interference and ship threat fusion evaluation system, and performing risk grade division in combination with real-time monitoring data and a historical database; and generating early warning information based on the risk level, and automatically triggering an equipment maintenance instruction and a ship collision avoidance alarm. The method adapts to ship variable-speed steering scenes, threshold values are dynamically adjusted, and adaptability and analysis accuracy of the system are improved.

A navigation route planning apparatus includes processing circuitry configured to receive a planned route indicating a route and a current course direction of a movable body, receive movable body information that includes a position, a travelling direction, and a speed of the own ship, receive obstacle information that includes a position, a traveling direction, and a speed of an obstacle, determine a collision risk value associated with the planned route along the current course direction based on the movable body information and the obstacle information, and determine whether the movable body requires to at least one of evade the planned route and continue traversing on the planned route based on the collision risk value.

The invention belongs to the technical field of ship navigation and port automation, and particularly discloses a dock ship guiding path planning and dynamic obstacle avoidance system, which is characterized in that a data acquisition module acquires ship parameters, environmental data and obstacle information through a ship end sensing unit and a dock end monitoring unit and standardizes the ship parameters, the environmental data and the obstacle information; the initial path planning module generates an optimal initial ship guiding path based on an improved A * algorithm in combination with ship physical constraints, environmental constraints and operation rules; the dynamic obstacle avoidance module monitors the movement of an obstacle in real time, predicts the trajectory of a ship and the obstacle, and triggers local path re-planning and smoothing processing when a collision risk occurs; and the control output module converts the path into a control instruction, issues and receives ship feedback through two-way communication, and forms closed-loop control according to a deviation fine adjustment instruction or re-planning. The system realizes full-process automation of ship guiding, can dynamically adapt to environmental changes, improve path precision and operation safety, adapt to different docks and ship types, and ensure that ships are accurately and safely parked to target berths.

The invention discloses a diffusion model trajectory planning method based on historical guidance and collision suppression. The method comprises the following steps: collecting own vehicle information, environment information and routing lane information in normal vehicle driving data; constructing negative sample scene data containing vehicle collision; during model training, encoding static obstacle states, dynamic obstacle historical tracks and surrounding lane structure information in a current environment; training a diffusion model decoder based on the future trajectory of the own vehicle, inputting the environmental condition information, the routing lane information and the own vehicle planning result of the model at the previous moment as condition information into the decoder, and enhancing the time continuity of the planning result; and training a classifier based on the collision data, and applying negative guidance to a collision category by using a classifier guidance mechanism during reasoning and sampling of the diffusion model so as to suppress a trajectory generation result with a high collision risk. According to the invention, efficient, safe and stable planning track generation can be realized in a real road scene.

The invention provides an unmanned ship collision avoidance decision-making method based on navigation intention perception, and the method comprises the steps: collecting the motion state data of a ship and a target ship in real time through a multi-source sensor, and constructing a collision risk assessment model for screening a threat target; the posterior probability distribution of the navigation intention of the target ship is dynamically updated based on recursive Bayesian reasoning of the historical trajectory of the target ship, and the navigation intention is dispersed into a plurality of behavior modes; constructing the state of the ship, the motion characteristics of the target ship and intention distribution into a state vector, inputting the state vector into a strategy network based on reinforcement learning, and outputting a collision avoidance control action; policy network optimization is guided through a composite reward function, and the reward function fuses distance reward, rule penalty and collision risk penalty.

The invention discloses a ship collaborative collision avoidance decision-making method based on a multilayer coding genetic algorithm. The method comprises the following steps: obtaining and processing related information of each ship in an encounter situation based on a sensor; based on a collision risk degree model, constructing a dynamic early warning mechanism in a multi-ship encounter scene: constructing a multi-stage ship collision avoidance strategy based on a queuing theory theoretical framework, establishing a multi-ship collision avoidance decision queuing system with specific space-time coupling characteristics, introducing a dynamic time window distribution mechanism and a space-time constraint coupling network modeling method, and establishing a multi-ship collision avoidance decision queuing system; multi-objective optimization is carried out on the ship decision scheme of the first priority gradient, and a ship intelligent collision avoidance strategy with dynamic adaptability is provided; a multi-ship collaborative collision avoidance decision optimization model is constructed on the basis of a genetic evolution mechanism, a multi-target adaptation function is integrated, a decision scheme when multiple groups of ships meet in a first priority is determined through the synergistic effect of dynamic priority management and multi-layer coding optimization, and hierarchical analysis of decisions in a multi-ship meeting situation is achieved.

The invention discloses an equipment disassembly sequence automatic generation and verification method based on a physical engine, which belongs to the technical field of equipment disassembly, and realizes component physical attribute and dynamic connection constraint loading by acquiring an equipment three-dimensional model and initializing a physical engine simulation environment. And in the physical engine simulation environment, iteratively evaluating the feasibility of the disassembly operation, accurately capturing physical behaviors such as gravity and collision, and identifying disassembly obstruction and preset operation to generate candidate operation. A path search algorithm is utilized to intelligently explore and generate a creative disassembly sequence, and the limitation of traditional artificial experience and a static method is overcome. Through a multi-dimensional nonlinear total cost function, factors such as the number of disassembly steps, collision risks, tool replacement and accessibility and the like are comprehensively quantified, and an optimal solution considering efficiency, safety and operation convenience is verified and selected.

The invention relates to the technical field of dredging engineering, in particular to a dredger autonomous path planning method based on reinforcement learning, which comprises the following steps: acquiring a multi-dimensional state space vector in real time; constructing a digital twinborn scene, training a strategy network by adopting a three-stage course learning strategy through increasing environment complexity, and dynamically adjusting a path planning priority through a layered reward function; migrating the trained strategy network to a ship entity; recognizing an obstacle based on a target multi-mode sensing system, evaluating a collision risk through a dynamic probability model, and performing local path reconstruction when the collision risk exists; and in a multi-ship collaborative operation scene, constructing a global optimization model, performing dynamic task redistribution on each ship by adopting a distributed iterative solution and incremental updating strategy until convergence, and outputting a collaborative optimization scheme of each ship. According to the method, the path planning efficiency in a multi-ship collaborative operation scene can be improved, and the training cost is reduced.

The invention relates to the technical field of fishing port safety monitoring, and discloses a fishing port fishing boat berthing safety monitoring system. The system comprises a fishing boat dynamic sensing module which collects dynamic position and attitude data of each fishing boat in a fishing port in real time; the berth state analysis module generates berth occupation state distribution information based on the data; the environmental parameter acquisition module acquires fishing port hydro-meteorological parameters in real time; the safety constraint initialization module outputs an initial safety monitoring constraint including an allowable yaw threshold value and a maximum allowable collision risk value in combination with a berth occupancy state and hydro-meteorological parameters; the parking risk assessment module executes risk quantitative analysis and generates risk level identification data by taking the initial safety monitoring constraint as a condition; a scheduling parameter optimization module optimizes berth resource scheduling according to the risk level identification data to obtain safety scheduling control parameters; and the alarm signal generation module converts an alarm dimension according to the safety scheduling control parameter and the dynamic position data updated in real time, and outputs a multi-stage acoustic alarm signal.

The invention discloses a ship dynamic collision avoidance decision-making method based on an improved DWA algorithm. The ship dynamic collision avoidance decision-making method comprises the steps of constructing sea chart environment data containing obstacle density through a grid method; establishing a dynamic elliptical ship domain based on a quaternion ship domain (QSD) model, and calculating a distance collision risk degree (NCRI) between ships based on the dynamic elliptical ship domain; improving a DWA algorithm evaluation function, and adding a path yaw evaluation function and a collision avoidance rule evaluation function on the basis of optimizing a speed and azimuth angle evaluation function; fusing the multiple evaluation functions by adopting a weight distribution method; and finally, generating a local collision avoidance optimization route based on environment data, NCRI and an improved DWA algorithm. According to the method, safety boundary dynamic adjustment, rule compliance constraint and ship control characteristic cooperation are realized, and the robustness and practicability of collision avoidance decision making under complex sea conditions are remarkably improved.

The invention relates to the technical field of anti-collision systems, in particular to an offshore wind power construction safety pre-warning system based on AIS (automatic identification system) data, which comprises a ship dynamic uncertainty modeling module for acquiring AIS signal updating frequency and positioning precision marks of a target ship and generating a ship future position probability ellipse. According to the invention, the position coordinate, the speed value and the course angle of the ship are obtained in real time based on the AIS data, the reliability and the motion trend of ship position prediction are determined by combining the AIS signal updating frequency and the positioning precision mark, and the future position probability ellipse of the ship is accurately generated; a protection area is dynamically constructed, comprehensive calculation is carried out in combination with the spatial relation between a ship future position probability ellipse and the positions of surrounding fan pile foundations, and a hourly dynamic collision risk index sequence is obtained; and further according to the statistical deviation between the current motion state of the ship and the standard parameter of the function partition, combining with path difference analysis to obtain a navigation abnormity comprehensive index.

The invention relates to the technical field of intelligent driving assistance systems, in particular to a vision-assisted high-precision lane-level navigation system which comprises a visual perception module, a geometric modeling module, a trajectory optimization module, a dynamic obstacle avoidance module and an execution control module. Establishing a lane local coordinate system based on arc length parameterization, and accurately capturing road geometric characteristics; state representation is constructed in a Riemannian metric space, an optimal track is selected by calculating geodesic line deviation measurement, and the generated track is made to better conform to road geometric characteristics; compared with the prior art, the method has the advantages that the lane-level positioning with centimeter-level precision can be realized, the driving track with smoother curvature change can be generated, and the driving comfort can be obviously improved.

The invention provides an intelligent substation inspection method and system based on the Internet of Things, and the method comprises the steps: collecting a multi-mode perception data set, and constructing a real-time three-dimensional map of a substation; constructing a cross-modal generation type fault detection model, and extracting fusion features to obtain a diagnosis result; formulating an optimal inspection path and plan by using a double-layer reinforcement learning framework and an evolutionary algorithm; and executing inspection based on the map to obtain a result. According to the method, multi-source equipment data are fused through the bionic sensing network, and the complex fault recognition capability is improved in combination with the generative fault detection model; the real-time three-dimensional map is based on laser radar SLAM and UWB fusion positioning, and is dynamically updated by using an ICP algorithm, so that the static map problem is solved, and the collision risk is reduced; double-layer reinforcement learning and an evolutionary algorithm are combined to realize multi-objective collaborative optimization, so that high-efficiency routing inspection is guaranteed, energy consumption is reduced, and time is shortened; and a closed loop verification mechanism dynamically adjusts the strategy, so that the adaptive capacity of the system is enhanced, and the inspection accuracy, efficiency and flexibility are improved.

The invention provides a port mobile machinery intelligent decision-making method, device and equipment based on multiple modes, and relates to the technical field of artificial intelligence. Comprising the steps of capturing a causal chain of wind speed sudden change-lifting appliance swing amplitude increase-AGV path offset through a multi-modal causal embedding layer, mapping heterogeneous data into uniform potential space representation, eliminating data modal difference, and adaptively enhancing the response capability to key risks by a model by dynamically adjusting a causal edge weight, so as to improve the stability of the AGV path. And furthermore, an AGV speed limiting or lifting appliance braking instruction is triggered through a preset condition. Therefore, according to the embodiment of the invention, the problems of multi-modal data fusion and dynamic causal modeling can be solved, the decision-making speed and accuracy are improved, and the collision risk is greatly reduced.

The invention discloses a mobile robot accurate docking method based on edge calculation, and aims to solve the problems that the dynamic docking precision is reduced and the collision risk is increased due to micro-motion or drifting of a target station. According to the method, unified time reference alignment is carried out on data of a camera, a laser radar, an inertial measurement unit, an ultra-wideband range finder, a station encoder and a programmable logic controller at an edge node, and a three-dimensional special Euclidean group equivariant multi-source fusion network is used for outputting relative pose estimation and covariance; the estimation in the time window is further used as a condition to be input into a conditional diffusion short-time prediction model to obtain a time-varying mean value and a time-varying covariance, an anisotropic probability tube is constructed, and prediction-measurement joint correction is carried out based on a score function; scenarized opportunity constraints are constructed under correction probability tube constraints, a tubular nonlinear model is adopted to predict, control and solve a reference trajectory, an actuator command is generated in combination with depth visual servo and compliance control, and the technical effects of high precision, robustness and safe docking under the station dynamic disturbance condition are achieved.

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

The invention relates to the field of automation, and particularly provides a crane boom anti-collision method and system based on dynamic detection, and the method comprises the following steps: obtaining power parameters, motion data and structural data of a main engine boom and an auxiliary engine boom; determining the current position information of a main engine boom, and determining the current position information of an auxiliary engine boom; acquiring predicted deceleration position information and predicted stop position information of the main engine arm support based on the motion data, the power parameters and the structural data of the main engine arm support; based on the current position information of the main machine arm frame, the current position information of the auxiliary machine arm frame and the predicted deceleration position information and the predicted stop position information of the main machine arm frame, judging a collision risk assessment result of the main machine arm frame and the auxiliary machine arm frame; and when the collision risk assessment result is early warning, the main engine boom is decelerated, or when the collision risk assessment result is dangerous, the main engine boom is stopped. According to the anti-collision method, collision of the crane boom can be avoided.

The invention provides an intelligent ship collision avoidance method and device, and belongs to the technical field of ship navigation control, and the method comprises the steps: building a water area environment model according to the real-time navigation data and water area environment data of a to-be-controlled ship, and determining a real-time collision avoidance demand according to the water area environment model; determining a predicted relative trajectory according to the real-time navigation data of the to-be-controlled ship and the surrounding ships and the water area environment model, and determining the motion conflict radius of each surrounding ship according to the predicted relative trajectory; performing collision avoidance strategy generation optimization according to the motion conflict radius and the real-time collision avoidance demand to obtain a ship collision avoidance strategy; and adjusting the navigation state of the to-be-controlled ship according to the ship collision avoidance strategy and a preset collision avoidance rule until no collision risk exists and a re-navigation condition is met. Static obstacle information and dynamic change information of surrounding ships can be comprehensively considered in the collision avoidance decision, the accuracy of the collision avoidance decision is improved, and the collision risk is reduced.

The invention discloses an intelligent driving control method and system for an unmanned ship, and belongs to the field of unmanned driving control. Firstly, the position and speed of the unmanned ship and the positions and speeds of surrounding obstacles are obtained through a sensor and a positioning device, then dynamic path tracking is carried out in combination with global path planning and local path planning, and finally the unmanned ship is driven to sail along an optimized path by controlling the course and an accelerator. Local path planning adopts an improved dynamic window method, and an unmanned ship trajectory prediction evaluation function is modified by introducing a dynamic risk evaluation layer and a rule compliance layer; the dynamic risk evaluation layer is used for designing a collision risk evaluation function to dynamically screen a speed sampling space and rejecting a speed vector conflicting with a predicted trajectory of a moving obstacle; and the rule compliance layer classifies the meeting situation, introduces a rule evaluation function to perform weight correction on the candidate velocity vector, and drives the unmanned ship to generate a course adjustment strategy conforming to the collision avoidance rule. The unmanned ship realizes full-autonomous cruise, high-precision obstacle avoidance and path tracking in a complex marine environment.

The invention provides an automatic berthing method and device for a single boat in the new quality field, and relates to the technical field of automatic control of boats in the new quality field. Static berth information and a sensor sensing result are fused to construct a task situation package containing a multi-moving-obstacle trajectory prediction set and a dynamic prohibited area set, dynamic collision avoidance guidance is executed in a first-stage tracking path, and a staged deceleration strategy of vector motion and hovering is switched to obtain a berthing point state set when conflicts occur. Performing in-situ heading adjustment by using the state set of the berthing point, outputting consistent heading, zero navigational speed and position states, executing target point type vector approach guidance in the second stage, hovering when a collision risk is predicted, and recovering vector motion control in a safe time window to complete lateral positioning welt berthing. And finally, precise thruster control instruction output is achieved through thrust distribution of the double water-jet thrusters. According to the invention, automatic berthing of the new-quality field single boat under the condition of multiple moving obstacles can be realized.