833 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 relates to the technical field of navigation and positioning, in particular to an intelligent positioning and navigation early warning system and method for ships and boats. The method comprises the following steps: collecting and carrying out sea condition sensing and positioning signal evaluation according to a multi-source sensor synchronous data set to obtain a sensor reliability score table; performing target motion situation analysis according to the sensor reliability score table to obtain a fusion situation vector diagram; performing target motion state judgment according to the fused situation vector diagram to obtain a target motion state judgment result; performing multi-target collision risk assessment according to the target motion state judgment result to obtain a risk degree assessment matrix; performing navigation maneuvering performance constraint according to the risk assessment matrix to obtain a route planning maneuvering constraint rule set; and performing route trajectory search according to the route planning manipulation constraint rule set to obtain a navigation trajectory scheme. According to the invention, through the positioning navigation early warning technology, the positioning precision, the navigation safety and the early warning system practicability of the ship in a complex environment are obviously improved.

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 discloses an autonomous collision avoidance method and system for an unmanned ship in a mixed traffic mode, and the method comprises the following steps: obtaining the current position coordinate information, course information and surrounding environment information of the unmanned ship in real time through a ship positioning system and a ship recognition system on the unmanned ship; combining the target point, constructing a path plan and calculating the energy consumption of a navigation path, selecting a first navigation path, identifying and classifying static obstacles and dynamic obstacles, monitoring the static obstacles in real time, calculating and evaluating a detour difference, adjusting the path according to a collision avoidance rule, and generating a second navigation path; predicting the movement direction of the surrounding dynamic obstacles, and judging whether a collision risk exists or not; predicting a future position and adjusting the course to generate a third navigation path; and respectively evaluating the electric quantity threshold, the energy consumption threshold, the battery electric quantity and the energy consumption value, and adjusting an energy use strategy in real time. According to the invention, the accuracy of the navigation path is improved, and the safety of the unmanned ship in a complex environment is improved.

The invention discloses an unmanned ship obstacle avoidance path planning method and system based on global optimum, and belongs to the field of unmanned ship navigation obstacle avoidance. Comprising the steps that S1, a kinetic model of the unmanned ship is constructed, a motion coordinate system is defined, and the unmanned ship has corresponding speeds in the surging direction, the swaying direction and the yawing direction; s2, improving and simplifying the dynamic model of the unmanned ship through the influence of the water environment influence factors on the dynamic characteristics of the unmanned ship; s3, formulating a collision avoidance strategy based on predictive control, performing discretization processing, calculating parameters encountering other ships and a collision risk, and determining a target function in the collision avoidance strategy; and S4, determining a final collision avoidance path method, and searching a path global optimal solution of the unmanned ship through a loop iteration mode. According to the technical scheme provided by the invention, the problem that unmanned ship path planning and obstacle avoidance cannot be accurately and efficiently carried out in the prior art is effectively solved.

The invention discloses a ship path dynamic planning method based on a collision risk field, and the method comprises the following steps: carrying out the grid division of a map of a navigation water area of a ship, and obtaining a grid map of the navigation of the ship; performing path search on the grid map by adopting a path planning algorithm to obtain an initial route of the ship; establishing a collision risk field model of the target ship according to real-time navigation parameters of the target ship, and substituting the coordinates of the central point of each grid in the grid map into the collision risk field model to obtain a collision risk value of each grid in the grid map; when the ship sails to the grid with the collision risk value larger than a set threshold value, the grid which is closest to the terminal point and has the collision risk value smaller than the set threshold value serves as a local optimal point, and the ship route of the ship is updated; and repeating the steps until the ship arrives at the destination.

The invention relates to a ship collision risk assessment method and system based on multi-source heterogeneous data, and the method comprises the steps: obtaining a plurality of continuous frames of images shot at a fixed position, and extracting the center pixel coordinates of all ships in each frame of image; acquiring AIS data of the ship; aiming at the center pixel coordinates of all ships in each frame of image, identifying the ships with breakpoints in the tracks and the ships detected by mistake, and realizing the correction of the ship tracks; based on the pixel coordinates of the corrected ship trajectory, converting the pixel coordinates into world coordinates to obtain visual data; obtaining fusion data by matching the AIS data with the visual data; and based on the fusion data, ship collision risk assessment is realized. Compared with the prior art, the method has the advantages of improving the evaluation accuracy under the condition that the AIS data is partially missing, overcoming the problem of feature drift and the like.

The invention relates to the technical field of intelligent traffic systems, in particular to a curve early warning method, system and terminal based on target detection, and the method comprises the steps: generating a visual twin image of a target curve; the visual twin images are displayed on display devices on the two sides of a target curve; if the movement track of the moving target is overlapped with the position of the obstacle or the movement track of the moving target is overlapped with the estimated movement track of the obstacle, early warning information is sent, and the early warning information comprises sound prompt information and image prompt information. The graphic prompt information comprises that a visual twin image corresponding to the moving target is rendered on a display device on one side of the road consistent with the moving direction of the moving target. A visual twin image of a target curve is generated through a multi-sensor fusion technology, the motion states of a moving target and an obstacle are reflected in real time, and early warning information is sent out when a potential collision risk is detected, so that the traffic accident rate of a curve area is effectively reduced.

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 discloses a multi-agent path planning method, device and equipment, and the method comprises the steps: dividing a map according to first grids of a preset size, carrying out the global planning of all agents on the map, and generating a global path, the global path being a road section passing through a plurality of first grids from a starting point to a target point; on the basis of each path section of the global path, local planning is conducted on all agents, a local path is generated, and each path section is a road section formed by the center points of every two adjacent first grids; real-time data are obtained by monitoring changes of dynamic obstacles and dynamic targets in a map in real time, and the local path and / or the local path are / is updated and planned according to the real-time data to obtain an updated path. Potential collision risks can be quickly found, obstacle avoidance measures can be taken in time, and the safety of a multi-agent system and the smooth completion rate of tasks are improved.

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 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 discloses a ship path planning method and system based on Beidou grid codes, and belongs to the technical field of path planning, and the method comprises the steps: obtaining planning data of a to-be-planned area of a ship, carrying out the priority level division of the ship, and combining a prior path, a priority level and a planning index, a grid chart based on Beidou grid codes is generated through a real environment Beidou grid code management module to perform global path planning, collision risk analysis is performed according to real-time sensing data of a navigation positioning system, an AIS, an electric gyrocompass, a log, waves and weather forecast, a path planning effect is evaluated according to task completion efficiency after planning, and the path planning efficiency is improved. According to the method, real-time sensing data such as port information, ship route information, route planning historical data and meteorological information of ships are analyzed and excavated, the route is recommended, the safety, economical efficiency and reliability of the route are improved, and the method is suitable for large-scale popularization and application. And the intelligent level and the operation efficiency of the ship are improved.

The invention relates to the field of path planning, in particular to an intelligent routing inspection path planning method and system for power equipment, and the method comprises the steps: dividing a power equipment environment into grids based on three-dimensional point cloud data, and planning an initial routing inspection path; and after the real-time environment data is matched with the grids, obstacles are identified through connected domain analysis, and grid collision risk values are calculated. And dynamically adjusting the trajectory time sequence length of the DWA algorithm by using a local peak grid, and predicting multiple trajectories. And according to the collision risk value weighted average of the track grid sequence, obtaining the risk comprehensive degree, mapping the risk difference value of adjacent moments through a negative exponential function, generating a dynamic feedback coefficient, and correcting a DWA evaluation function. Finally, the track corresponding to the maximum dynamic avoidance evaluation function is selected, the inspection route is updated, intelligent path planning is achieved, and by introducing the dynamic feedback coefficient and collision risk evaluation, the applicability and reliability of the algorithm in different scenes are improved.

The invention discloses a ship encounter space-time risk assessment method and system considering navigation environment influence, and belongs to the technical field of ship safe driving. The invention aims to solve the problem that the collision avoidance action opportunity of a complex marine environment collision avoidance scene cannot be accurately determined. According to the invention, through a ship environment sensing module and a positioning module, channel environment information of a ship navigation area and position, course and speed information of a ship are obtained; calculating the nearest meeting distance of the intersection of the two ships, the meeting time of the ships sailing from the current position to the nearest intersection of the two ships, the safe meeting distance of the two ships, the absolute safe meeting distance, the latest avoiding distance and the distance capable of taking measures for avoiding; calculating the collision danger degree in the spatial position and the collision danger degree of the two ships in the time dimension; combining the space risk factor and the time risk factor by using a synthesis operator, and calculating the space-time collision risk factor; and evaluating the risk degree of ship collision according to the risk degree. The method is suitable for ship encounter space-time risk assessment.

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.