100 results about "Ship tracks" patented technology

The invention discloses a ship motion track prediction method based on ship track big data. The method comprises the following steps: collecting ship AIS data and preprocessing the ship AIS data to obtain ship track data; a sliding window is adopted to sample ship trajectory data, ship historical trajectory point state features are extracted, and a time interval relation matrix is constructed; the method comprises the following steps: constructing a Transform architecture model which only retains a decoder, discretizing an extracted trajectory feature and a to-be-predicted time interval into corresponding one-hot coding vectors so as to obtain embedded vectors, merging the embedded vectors of the two into a joint feature embedded vector, and stacking ship trajectory point sequences to construct an obtained joint feature embedded matrix, linearly adding the time interval relation embedded matrix and the position feature embedded matrix to obtain an embedded matrix, and inputting the embedded matrix into a Transform architecture model for training to obtain a prediction model; and outputting a ship motion trail prediction result through a decoder. According to the method, the accurate prediction of the non-uniform trajectory sequence is realized, and the problems of non-uniform time interval and low prediction accuracy of the ship trajectory data are solved.

The invention provides a ship abnormal behavior detection method based on an improved graph attention neural network, and belongs to the field of ship abnormal behavior detection. Through AIS data, a graph structure including attribute features such as ship position, speed and course is constructed, attribute features of ship tracks are expanded by using dynamic cavity graph convolution, and a space-time attention mechanism is fused into a multi-head attention mechanism, so that the model has the capability of processing time dimension features to better extract space-time features of the ship tracks, and the time-space attention mechanism is optimized. The detection effect of the abnormal behavior of the single ship is improved; in the aspect of abnormal behavior detection between ships, a two-layer graph coding structure is constructed, the first-layer graph coding adopts a coding mode of single ship detection to obtain trajectory features of the ships, and the second-layer graph coding constructs relation features between ship trajectories based on the trajectory features of each ship. Effective detection of abnormal behaviors between ships is realized in combination with the attention mechanism of the GAT, and the method has good accuracy and robustness and high practical value.

The invention discloses a tug configuration method based on AIS piloting data. The tug configuration method comprises the steps of obtaining original AIS data used for tug configuration; performing data analysis on the original AIS data to obtain an initial time sequence ship track; performing trajectory data cleaning processing on the initial time sequence ship trajectory to obtain a ship time sequence continuous trajectory; interpolating the acquired meteorological / ocean multi-source data and fusing the meteorological / ocean multi-source data with a ship time sequence continuous trajectory to obtain a ship trajectory fusion database; constructing a ship space-time matching and speed / course difference judgment mechanism to obtain a historical feature data set of ship-tug configuration according to the ship trajectory fusion database; constructing a tug configuration prediction model of AIS piloting data, and obtaining an optimal tug configuration prediction model based on historical feature data; and setting the optimal tug configuration prediction model in an existing port scheduling system to deploy an online service model for tug configuration so as to realize a tug configuration demand scheme based on AIS piloting data. The problem that the accuracy and the efficiency of tug configuration of AIS piloting data cannot be achieved due to the fact that AIS data is complex and uneven in quality and the temporal-spatial resolution, the coordinate system and the like of meteorological sea condition data are different from those of the AIS at present is solved.

The invention discloses an improved ship track data processing method and device, equipment and a medium. The method comprises the following steps: periodically obtaining AIS data of each ship in a target sea area; the AIS data comprises a timestamp, real-time navigation data and a navigation state code; preprocessing the AIS data to obtain an AIS data sequence of each ship; dividing the AIS data sequence based on the real-time navigation data and the navigation state code to obtain navigation segment data; performing data thinning based on inflection points of each segment in the navigation segment data to generate a thinned track list; and sorting based on the thinned track list to obtain the ship track of each ship. According to the method, the data storage amount is effectively reduced and the key track feature integrity is kept by adopting a sectional thinning strategy; the data processing efficiency is effectively optimized through a pre-segmentation mechanism based on state codes; the track segmentation accuracy is effectively improved through a dual verification mechanism fusing the motion characteristics of the navigation state code and the real-time navigation data.

The invention discloses a ship identification and water area safety situation analysis method and system. The method comprises the following steps: acquiring water area environment multi-source data; based on a ship target detection model and a target identity matching method, analyzing the water area environment multi-source data, and identifying a ship target; acquiring a historical ship track based on a ship track interpolation smoothing method according to the ship target and water area environment multi-source data; and obtaining a ship navigation safety situation score by using the ship track prediction model and the composite artificial potential field model according to the ship historical track and the water area environment multi-source data. According to the technical scheme provided by the invention, a ship target is identified through an SDRV method and a target identity matching method, abnormal track points are screened, a historical track is interpolated and smoothed, and ship navigation safety situation scoring is carried out based on track prediction and a composite artificial potential field; multi-source dynamic information of a ship target can be integrated, and ship identification and safety situation analysis are effectively supported.

The invention discloses a near-shore ship tracking method and device based on multi-source data fusion and a storage medium, and the method comprises the steps: obtaining and processing first-class radar point cloud data of a wind power plant millimeter wave radar, vibration sensor data of a breakwater, AIS data and second-class radar point cloud data of multiple radar stations; performing space alignment and time alignment on the processed first type of radar point cloud data and the processed second type of radar point cloud data, and performing time alignment on the processed AIS data; fusing the aligned data to obtain target state information; and predicting a plurality of states of the ship at future moments based on the target state information to obtain a plurality of corresponding prediction trajectories, correcting the prediction trajectories based on a wind power plant wake flow model, and screening out an optimal trajectory from the corrected prediction trajectories as a ship track. Therefore, the method can improve the multi-dimensional anti-risk capability, has higher safety guarantee, and can improve the supervision efficiency in a novel offshore wind power plant and other complex environments.

The invention discloses a visual drawing method and device for an inland ship navigation trajectory and a medium, and the method comprises the steps: collecting GPS positioning data, AIS dynamic parameters and static attribute data of a ship through a ship terminal, carrying out the preprocessing of the GPS positioning data and the AIS dynamic parameters, and storing the processed data in a database according to a timestamp index; calling a geocoding service interface, mapping the processed longitude and latitude coordinates of the ship to an electronic navigation channel chart coordinate system, and periodically updating a ship position mark in an electronic map interface; in response to a time period query instruction input by a user, extracting ship track point data corresponding to the timestamp sequence from the database, and superposing the ship track point data to the electronic channel chart to generate a dynamic track line; a user operation instruction is received through the time axis control, the ship mark position and the course display state on the trajectory line are dynamically adjusted, and visualized backtracking of the historical trajectory of the ship is achieved.

The invention discloses a moored ship motion state prediction and anti-collision early warning system, particularly relates to the field of traffic control and anti-collision early warning, and is used for solving the problems that ship motion state accurate prediction and anti-collision scheduling response lag cannot be realized in existing port mooring operation. According to the system, ship trajectory, port operation and environment monitoring data are acquired in real time through a data acquisition module, and a fusion data stream with consistent time-space attributes is generated; constructing a space-time diagram structure by using a feature extraction module, extracting traffic flow dynamic features, and calculating a short-term conflict probability, a trajectory convergence risk value and a mooring risk value; the risk early warning module generates a collision risk index based on risk level mapping and triggers graded early warning; the mooring decision-making module sets a key control leg according to the early warning level and generates a dynamic decision-making scheme; the anti-collision scheduling module executes actual channel passing, speed adjustment and track change, and dynamic coordination and safety management and control of port mooring anti-collision are achieved.

The invention relates to the technical field of track prediction, and particularly discloses a ship track prediction method based on spatial-temporal feature fusion, which comprises the following steps of: processing AIS information of each time in a known ship track by adopting a neural network model based on deep learning to extract time dimension features and spatial dimension features of the ship track; hidden association dominant modeling analysis is carried out on time dimension features and space dimension features of a ship trajectory to excavate hidden key clues between the time dimension features and the space dimension features, and alignment interaction fusion of the time dimension features and the space dimension features of the ship trajectory is realized based on the hidden key clues. And predicting the future track of the ship based on the space-time fusion features of the ship track. Through the mode, the correlation among the ship navigation speed, the course change and the geographic position can be deeply excavated, so that more accurate feature alignment interaction fusion is realized, and the accuracy of ship track prediction is further improved.

The invention discloses a marine radar multi-target tracking method and system. The method comprises the following steps: constructing an RSDT small target detection algorithm and training to obtain a marine radar image ship identification model; identifying the ship position of the current frame of the marine radar video stream through a marine radar image ship identification model to obtain a ship detection frame; constructing a ship trajectory prediction model, and predicting the ship position of the current frame according to the ship position of the previous frame of the marine radar video stream to obtain a ship prediction frame; matching the ship detection frame and the ship prediction frame of the same frame to obtain a matching result; if the matching is successful, inputting the ship detection frame and the ship prediction frame into the ship trajectory prediction model and updating the ship detection frame and the ship prediction frame, and outputting a new ship motion trajectory; and if the matching fails, creating a high-confidence detection frame in the ship detection frame as a new ship motion track, thereby realizing accurate identification of the ship target and stable tracking of the ship track in the marine radar scene.

The embodiment of the invention provides a leaning behavior recognition method and device, electronic equipment, a readable storage medium and a chip, and the method comprises the steps: obtaining a first driving track of double ships and corresponding first track data; determining a leaning behavior track section based on the first traveling track and the first track data of the double ships; determining a single ship trajectory positive sample data set according to the lapping behavior trajectory segment; determining a single ship trajectory negative sample data set according to the normal navigation trajectory segment; training a fusion neural network recognition model according to the single-ship trajectory positive sample data set and the single-ship trajectory negative sample data set; obtaining target trajectory data of a to-be-identified single target ship; determining a track fragment corresponding to the target track data; and determining a leaning behavior state corresponding to at least one track fragment of the single target ship. According to the scheme of the invention, under the condition that only one ship starts the AIS, the leaning behavior is identified, and the completeness of maritime behavior supervision is improved.

The invention discloses a ship dynamic high-precision positioning and abnormal behavior recognition method, and particularly relates to the technical field of navigation positioning. The method comprises the following steps: performing time sequence synchronization and noise elimination on satellite signal propagation time ranging data, carrier phase data and ship attitude data received by a ship; outputting a reflection path coupling relation parameter by analyzing a time-varying coupling relation between the rolling and pitching states of the ship body and a sea surface specular reflection path; by establishing a multi-path deviation correction model, error correction values of satellite signal propagation time ranging data and carrier phase data are calculated; ship track motion state data are output by extracting ship space attitude change features and track geometric morphology features; and updating the ship trajectory motion state data by using the corrected satellite signal propagation time ranging data and carrier phase data to obtain ship positioning trajectory data, and outputting a ship abnormal behavior identification result. And the detection accuracy and reliability of the abnormal behaviors of the ship are improved.

The invention belongs to the technical field of ship track prediction, and discloses a ship track prediction method and system based on multi-ship coupling influence. The method comprises the following steps: identifying adjacent ships generating coupling influence on a target ship; extracting a potential motion feature vector of the target ship under coupling influence at the # imgabs0 # moment; outputting a predicted track of the target ship by using the designed generator; and inputting the real track and the predicted track into a discriminator, and finally generating the probability that the input track is identified as the real track. The mutual competition training of the generator and the discriminator in the generative adversarial network can further improve the precision of ship track prediction, so that the constructed model is more suitable for a real ship navigation environment, and the collision risk is reduced.

This invention discloses a computer vision-based method for ship traffic situational awareness, comprising: collecting ship traffic data and preprocessing it to obtain a video frame sequence; performing ship target detection and outputting a set of ship detection results; constructing an improved ByteTrack model and outputting a set of trajectory segments; performing a Poisson-Dobernoli hybrid filter to obtain a set of ship tracks; performing coordinate mapping and track element calculation, calculating speed, heading, and position; performing risk event determination and outputting risk event results; and performing graded early warning and interpretable processing to generate early warning information. This invention, through the collaborative processing of the improved ByteTrack model and the Poisson-Dobernoli hybrid filter, achieves graded early warning and interpretable output of risk events for ship targets under complex sea surface imaging conditions.

The invention belongs to the technical field of ship positioning, and discloses a chart operation assessment comparison recording device, which is characterized in that a flat-panel display mounted on a fixed table board is matched with a high-definition camera and a light supplementing lamp, and ship real-time track information output by a Beidou satellite positioning module is introduced; therefore, the flat-panel display can display the track reference of the ship and the error allowable range curve in a highlight manner, so that the ship positioning result of the navigator and the positioning reference can be conveniently compared during the sea chart operation assessment, and the chart recording of the sea chart operation can be completed. By means of the device, chart drawing and positioning reference comparison and storage in assessment can be efficiently completed, the paper assessment chart can be erased after being stored, the actual requirement that the paper chart needs to be repeatedly used in actual operation is met, digital correction and filing work can be conveniently conducted on the stored chart operation result in the follow-up process, and the work efficiency is improved. The problems that in current sea chart operation practical training assessment, a sea chart operation result is difficult to store, and the comparison efficiency is low are solved.

The invention provides an unmanned ship group control method and system for ocean-crossing sailing, and the method comprises a wolf pack behavior simulation model which constructs a multi-level distributed command structure for unmanned ship formation, and the multi-level distributed command structure comprises three levels, namely an alpha wolf, a beta wolf and an Emega wolf; according to the position data, the ocean current model and an energy consumption minimization criterion, unmanned ship route planning is carried out, and an optimal route is planned by an alpha wolf through an internal rule and dynamic adjustment is carried out; and sailing and / or combat are / is carried out according to the unmanned ship track and the multi-level distributed command structure, a local wireless broadcasting mode is adopted, alpha wolves and beta wolves broadcast state instructions in a fixed period, the Omega wolf response frequency is low, and communication is carried out only when the task state is activated. According to the invention, autonomous navigation of the unmanned ship cluster can be realized under the condition of no central node control, target identification and tactical division cooperation are realized, and low-energy-consumption and high-precision path control in a cross-ocean long-voyage environment is realized.

The application discloses a near-shore ship tracking method and device based on multi-source data fusion and a storage medium. The method comprises the following steps: acquiring first radar point cloud data of a wind farm millimeter wave radar, breakwater vibration sensor data, AIS data and second radar point cloud data of a multi-radar station, and processing the data; performing spatial alignment and time alignment on the processed first radar point cloud data and second radar point cloud data, and performing time alignment on the processed AIS data; fusing the aligned data to obtain target state information; predicting multiple states of a ship at a future time based on the target state information, obtaining corresponding multiple predicted trajectories, correcting the predicted trajectories based on a wind farm wake model, and screening an optimal trajectory from the corrected predicted trajectories as a ship track. Thus, the multi-dimensional risk resistance capability can be improved, the safety guarantee is higher, and the supervision efficiency in a complex environment such as a new near-shore wind farm can be improved.

The present invention discloses a ship trajectory prediction method based on the fusion of marine environmental characteristics, including: obtaining the ship dynamic sequence data, ship position sequence data, and marine environmental information at the current moment, and inputting them into the ship trajectory prediction model; performing One-hot encoding on the ship dynamic and position sequence data, and performing spatio-temporal grid position association decoding on the marine environmental information; performing Encoder encoding on the One-hot encoded data of the obtained ship dynamic sequence and ship position sequence, as well as the spatio-temporal grid decoded data of the marine environmental information, to obtain the ship time series characteristics and marine environmental time series characteristics; performing cross-modal alignment fusion on these two time series characteristics and then performing Decoder decoding to obtain the ship dynamic information and ship position information at the next moment, and outputting them after One-hot decoding. This method uses marine environmental information to supplement ship trajectory modeling and improves the accuracy of the ship trajectory prediction model.

The invention discloses a navigation mark visual identification and collision early warning method and system based on deep learning, and relates to the technical field of intelligent shipping, and the method comprises the steps: collecting a navigation channel monitoring video stream in real time, and carrying out the preprocessing of a key image frame; constructing a navigation mark detection model based on an improved Officient Det network, and outputting the position and category of the navigation mark in the key image frame; the method comprises the following steps: constructing a ship track prediction model through ship historical track data, predicting a short-time track of a ship, calculating the relative position, speed and course angle of the ship and a navigation mark, and constructing a dynamic collision risk field; and fusing the visual detection result, the radar ranging data and the AIS information to obtain a ship collision risk, and triggering graded early warning based on a preset risk threshold. According to the method, the improved deep learning model and the real-time calculation framework are combined, the robustness of navigation mark identification is improved, an efficient collision early warning mechanism is established, and technical support is provided for intelligent shipping.

The present application relates to the field of radar and vision joint calibration technology, and specifically to a ship track joint calibration method based on radar and vision technology. The method includes: acquiring radar point cloud data and image data; performing three-dimensional synchronous acquisition of multiple groups of point cloud image data pairs at each moment; performing cross-correlation analysis on the radar point cloud data and image data of the dynamic target, and combining the multiple modal function values ​​of the sequence formed after the fusion of the two data to determine the track fusion confidence of the dynamic target at each acquisition moment; determining the sea condition stability index of the dynamic target at each acquisition moment by combining the smoothed mean value of the track fusion confidence of the dynamic target with the local periodic fluctuation index; selecting a calibration method based on the sea condition stability index to complete the calibration of the dynamic target. The present application avoids the delay problem of the traditional fusion model, circumvents the pollution of the main sensor degradation data, and ensures the continuity and anti-interference ability of the track calibration under complex sea conditions.

The invention discloses a ship encounter situation track prediction method based on a context embedding technology. The method comprises the following steps: extracting ship track characteristic variables under an encounter situation; establishing a ship encounter situation track prediction model based on a context embedding technology; and evaluating the ship encounter situation track prediction model. According to the method, the track information of the ship encountering the meeting situation in the ship navigation process is extracted, and the track behavior characteristics of the target ship and the surrounding ships are defined. In addition, the spatial feature information of the target ship and the encounter track is incorporated into a prediction framework of the invention, and is used for learning the behavior pattern of the ship track under the encounter condition of the ship so as to provide an effective and accurate track prediction service. According to the method, the feature extraction capability of the FIENet and the advantage that a weight adaptive adjustment mechanism contacts global information are combined, and the learning capability of the ship navigation mode of the track prediction model under the meeting condition of the ship is improved.

The utility model discloses a ship trackball with a drainage structure, which comprises a shell, a top cover, a bottom plate and a trackball body, the top cover and the bottom plate are respectively fixed on the upper surface and the lower surface of the shell, the trackball body is movably arranged in the shell through a plurality of stainless steel bearings, and a dustproof ring is arranged between the top cover and the trackball body. The surface of the dustproof ring is sleeved with high-elasticity foam relative to the bottom of the top cover. A drainage structure is arranged at the bottom of the bottom plate; according to the ship trackball, an existing ship trackball is improved, the drainage structure is designed at the bottom of the ship trackball, water in equipment can be smoothly drained from the bottom under the condition that the bottom plate is not detached, and due to the design, the waterproof performance of the equipment is improved, the maintenance process is simplified, the use cost of a user is reduced, and the service life of the ship trackball is prolonged. Meanwhile, the high-elasticity foam is designed at the top, so that a gap between the upper part and the lower part of the ball body can be buffered, and moisture permeating from the top surface can be absorbed, and therefore, the PCB inside is protected, and the overall reliability of equipment is ensured.

The invention provides a marine ship trajectory association method, device and equipment and a medium, and the method comprises the steps: employing OpenCV to extract a marine ship image, carrying out the preprocessing, and employing edge detection and contour processing, thereby obtaining the size features of a marine ship; extracting the contour gravity center position of the marine ship based on a six-parameter affine transformation matrix of GDAL, and extracting longitude and latitude coordinates of a target; obtaining the multi-modal features of the marine ship, carrying out the weighted fusion of the multi-modal features, and taking N targets with the highest probability as a candidate set; based on the dynamic behavior characteristics, obtaining the course and speed of the marine ship and then calculating a predicted target position; and matching the longitude and latitude coordinates of the target in the candidate set with the predicted target position, selecting the predicted position with the minimum distance as an association point through Euclidean distance calculation, generating a continuous track and forming target situation information. According to the technical scheme, the target recognition precision can be improved, and the continuous target situation can be formed.

The invention discloses an intelligent track planning system and method applied to an unmanned ship, and relates to the technical field of track planning, and the method comprises the steps: shooting a target image, setting the moving speed and collection interval of the unmanned ship, and obtaining an initial navigation track; historical navigation records of the unmanned ship are called, and a reference track in the navigation tracks is judged and extracted; acquiring a reference image of the water area, and extracting an obstacle area in the to-be-detected water area; and extracting a plurality of sub-trajectories in the to-be-detected trajectory to obtain all collection points, setting index conditions, performing early warning prompt if the index conditions are met, and changing the to-be-detected trajectory again until the adjusted navigation trajectory does not meet the index conditions. According to the invention, by analyzing the historical navigation record, judging the rationality of the adjusted navigation track and carrying out early warning prompt, intelligent planning of the unmanned ship track is realized, and the unmanned ship track planning capability and the measured water depth level are effectively improved.

The invention discloses a ship operation data backtracking method based on an error correction algorithm, which relates to the field of maritime engineering, solves the problem of high error rate of ship operation data abnormity backtracking, and comprises the following steps: setting a track monitoring area of a target ship in the navigation process, analyzing ship navigation data based on the track monitoring area, and determining the ship operation data according to the analyzed ship navigation data. Obtaining an optimized ship track of the target ship; the optimized ship track is analyzed, the target ship is classified according to the historical track of the historical ship, and the historical ship of the same category as the target ship is obtained; analyzing the ship operation data, judging an abnormal condition of the ship operation data corresponding to the target ship in combination with historical operation data, and generating a corresponding abnormal signal; after the abnormal signal is received, a ship operation function is constructed according to the historical operation data of the same type of historical ships, abnormity backtracking is conducted on the ship operation data of the target ship based on the ship operation function, and accurate backtracking of the ship operation data is achieved.

The application discloses a route design method based on marine climate data and ship historical track, relates to the technical field of route design, and is used for solving the problem that the environmental adaptability experience and risk evolution law contained in the historical ship navigation track are ignored, so that the route design scheme is difficult to adapt to the high-risk section, and the method comprises the following steps: constructing a global port geographic information database and a shipping channel data set, obtaining historical ship track and marine climate data of a starting position and a target position, generating an initial route, screening a basic route point with the minimum risk based on the risk mutation coefficient and the environmental stability of each route point of the path, connecting the basic route points to form a basic route, comparing the similarity to determine a selected route, and collecting attitude feedback data when the ship travels along the route, calculating a cumulative navigation efficiency difference after the target position, evaluating the consistency of the return route in combination with the feedback, and optimizing the route under the complex marine environment, so that the navigation safety and efficiency are improved.

The present invention discloses a method and system for processing electronic navigational charts based on the HarmonyOS. The method includes: obtaining an electronic navigational chart during ship navigation, where the electronic navigational chart includes a navigation route, and obtaining a ship track chart of the ship's real-time navigation, where the ship track chart includes the ship's actual navigation route; setting an image fusion model, and fusing the electronic navigational chart and the ship track chart according to the coordinates on the navigation route and the ship's actual navigation route. Among them, calculate the fusion value of each coordinate of each route on the fused image, and respectively display the navigation route and the ship's actual navigation route on the electronic navigational chart according to the fusion value.

The invention relates to the technical field of intelligent route planning, provides an intelligent route planning method based on deep reinforcement learning, and realizes intelligent route planning by combining AIS data preprocessing, a reinforcement learning algorithm, a state space design, an action space design and a reward function design. Specifically, through AIS data preprocessing, abnormal stop points, abnormal drift points and abnormal steering points are identified and eliminated. Meanwhile, historical ship route data are sorted and screened out, and a safety database of historical ship tracks is constructed. And thirdly, establishing a deep reinforcement learning model, and improving the convergence speed of the DQN algorithm by combining the K-means algorithm into the DQN algorithm. The efficient, safe and extensible intelligent route planning method is realized.

To track a target even in a small area in the vicinity of an own ship.SOLUTION: A ship tracking device 10 includes an acquisition unit 20, an area setting unit 30, a selection unit 50, and a tracking unit 60. The acquisition unit 20 acquires point cloud data comprising a plurality of feature points obtained by ranging a surrounding environment including another ship with respect to an own ship. The area setting unit 30 sets individual areas for the plurality of feature points based on a distribution of positions of the plurality of feature points. The selection unit 50 selects a target tracking ship based on the size of the individual areas. The tracking unit 60 generates tracking data based on a time-series change of the positions of the target tracking ship.SELECTED DRAWING: Figure 1

The invention discloses a ship platform abnormal behavior detection method based on ADS-B big data, and particularly relates to the technical field of ship navigation safety monitoring. The method comprises the following steps: acquiring automatic related monitoring broadcast data of a target sea area ship, constructing a ship track evolution matrix, and generating a ship perturbation motion feature tensor; constructing a ship neighborhood interactive topology network, calculating a trajectory disturbance propagation weight between nodes, and forming an abnormal propagation probability matrix; establishing a coupling response relation between a local track disturbance energy sequence and neighborhood interaction intensity through time sequence folding mapping, generating an abnormal behavior evolution prediction curve, calculating an abnormal energy aggregation index, generating a ship abnormal behavior risk distribution map in combination with an abnormal propagation probability matrix, extracting implicit behavior indexes, and calculating a ship abnormal behavior risk distribution map. Through dynamic matching with the preset abnormal behavior mode, the micro-scale abnormal navigation behavior in a complex sea area environment can be effectively identified, and the accuracy and the early warning capability of ship abnormal behavior detection are improved.