CN116828391B - Method for detecting AIS abnormal switch - Google Patents
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- CN116828391B CN116828391B CN202311093952.8A CN202311093952A CN116828391B CN 116828391 B CN116828391 B CN 116828391B CN 202311093952 A CN202311093952 A CN 202311093952A CN 116828391 B CN116828391 B CN 116828391B
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Abstract
The application discloses a method for detecting an AIS abnormal switch. The method aims at solving the problem that the actual requirement of the abnormal switching scene of the automatic ship identification system cannot be rapidly detected; the application comprises the following steps: s1: dividing a sea area into a plurality of grid-shaped areas, establishing and recording each area and a document of the change quantity of the offshore mobile service identification number between each area, and setting a conservation rule; s2: performing collective operation according to each region and the variation among the regions to obtain a ship which is preliminarily judged to have illegal risks; s3: establishing a line segment tree taking the difference set base number of a single region as a node, updating each leaf node, and inquiring a root node; s4: and verifying signals before and after generation/disappearance by using an AIS transmitting rule, and judging whether the ship is a ship with illegal risks. And the track processing is not performed, and the point location information of the ship automatic identification system is directly utilized to process the problem of signal persistence of the ship automatic identification system.
Description
Technical Field
The application relates to the field of ship abnormal behavior identification, in particular to a method for detecting an AIS abnormal switch.
Background
The automatic ship identification system (Automatic Identification System, AIS) is a digital navigation aid system and equipment integrating network technology, modern communication technology, computer technology and electronic information display technology, and is connected with a GPS (global positioning system) positioning instrument, a depth finder, an electronic compass and the like on a ship through an interface of the system to acquire various ship navigation dynamic information, and the information is displayed on an electronic display screen to provide real-time navigation information of a ship for operators; at the same time, the static and dynamic information of the ship is transmitted to the nearby shore base station or satellite through the transmitting equipment of the system, and in the process, the surrounding ships and the traffic center can receive the information.
The information sent by the ship automatic identification system comprises ship identity, ship position, draft, navigational speed, bow direction, ship type, ship length and width. The use of the automatic ship identification system equipment accumulates a large amount of ship navigation data, and provides important basic data for modeling normal behaviors by utilizing a data mining technology and detecting abnormal behaviors of the ship according to the normal behaviors. The abnormal behavior detection of the ship based on the automatic ship identification system is to evaluate and early warn the normal sailing performance of the ship according to the input sailing information of the ship.
The popularization and application of the automatic ship identification system equipment enable the automatic ship identification system data to become one of important data sources of current ship traffic flow analysis, maritime supervision and accident analysis. However, the unreliability of the message of the automatic ship identification system brings difficulty to the upper layer application.
In daily supervision of the automatic ship identification system, in order to ensure sailing safety, the behavior of switching on and off the automatic ship identification system should not exist for the ship in the sailing course. Therefore, the continuous monitoring of the signals of the automatic ship identification system is an important link for sea law enforcement. Because the ship automatic identification system has huge information quantity, and the ship automatic identification system has emission delay and transmission delay, each ship is difficult to track.
At present, two main ways of track clustering and machine learning are adopted to realize the tracking of ships.
For example, in the method disclosed in the chinese patent literature, the publication number CN114756637a is used for dividing the track by the minimum description principle, describing the local characteristics of the track by the track segments of the ship, and combining the method of local density, and the method that the number of track segments in the field does not meet the set limit value is used as the determination criterion for dividing the abnormal track, so as to reduce the track searching frequency to improve the detection efficiency, and determine whether the abnormal track is the abnormal track according to the proportion of the abnormal track segments in the whole track.
However, track clustering has significant limitations in use. Firstly, the time sequence needs to have as high a signal-to-noise ratio as possible, does not want to have a large amount of noise, and introduces a large error, so that denoising, and the simplest is smoothing processing, which needs to be grasped according to actual conditions so as not to excessively lose information. Secondly, the time sequence is often in the situation of dislocation matching, a similarity measurement algorithm is needed to solve the problem, and in practice, some processing is additionally performed according to the scene. Finally, the method of clustering and parameter selection are also a problem. However, in time domain analysis, there are limited things that can be done, so that trace clustering is also a non-negligible choice.
For another example, a "ship sailing behavior online prediction method" disclosed in chinese patent literature, its bulletin number CN109214107a, includes a training learning phase and an online prediction phase, where the training learning phase first sorts and divides the historical AIS big data, and then uses a bidirectional long-short-term memory cyclic neural network to perform training learning on the intercepted data; in the online prediction stage, real-time AIS data are collected by an AIS receiver, key feature points of ship track are obtained by adopting a sliding window algorithm, 6 future ship track points are predicted, and six future ship track points finally predicted are used as predicted sailing behaviors of the ship after multiple predictions.
However, the machine learning related art is costly and time consuming in the initial training. If there is insufficient data, it may be difficult to operate. If hardware is set internally, machine learning is a computationally intensive process requiring a large initial investment. Without expert assistance, it may be difficult to interpret the results correctly and eliminate the uncertainty. In the requirements of the automatic ship identification system on-off abnormality analysis, the timely and accurate judgment of abnormal conditions is more needed, and as the signals of the related violations of the automatic ship identification system are less common than the normal signals and the special types of the signal sources of the various automatic ship identification systems are different, the machine learning training process has great difference, so that the judgment accuracy is greatly reduced and the cost of training the related model is greatly improved.
Disclosure of Invention
The method mainly solves the problem that the actual requirement of the abnormal switching scene of the automatic ship identification system cannot be rapidly detected in the prior art; the method for detecting the AIS abnormal switch is characterized in that the point location information of the ship automatic identification system is directly utilized to process the signal persistence problem of the ship automatic identification system, and the detection, calculation and judgment of the ship automatic identification system are separated into different data structures to be processed, so that the effect of real-time detection is achieved.
The technical problems of the application are mainly solved by the following technical proposal:
a method of detecting an AIS abnormal switch comprising the steps of:
s1: dividing a sea area into a plurality of grid-shaped areas by using longitude and latitude, establishing a document for recording each area and a change matrix of the marine mobile service identification number between the areas, and setting a conservation rule;
s2: performing collective operation according to each region and the change quantity matrix of the offshore mobile service identification number between regions to obtain a ship which is primarily judged to have illegal risks;
s3: respectively establishing line segment trees with the base numbers of the difference sets of the offshore mobile service identification numbers of the single area as nodes on a longitude axis and a latitude axis, updating each leaf node, and inquiring the root node;
s4: and verifying signals before and after the generation/disappearance of the offshore mobile service identification number by utilizing an AIS transmitting rule, and judging whether the ship is a ship with illegal risks.
The method has the advantages that the track processing is not carried out, the problem of signal persistence of the ship automatic identification system is directly processed by utilizing the point location information of the ship automatic identification system, and the detection, calculation and judgment of the ship automatic identification system are separated into different data structures for processing, so that the effect of real-time detection is achieved. In the judging stage, the algorithm combines the design of the ship automatic identification system with different signal transmission intervals for different running states and different speeds, and prejudges the number of the ship automatic identification systems which should appear in different time periods, wherein the preliminary judgment is that the number is increased and decreased. The marine mobile service identification number is assisted with intersection and difference set operation, and the result is utilized to clear the data of the automatic ship identification system, so that the purposes of removing repeated signals and finding abnormal signals are realized.
Preferably, the recorded document content includes:
the difference set of the contained offshore mobile service identification number sets in different time periods of each region; to represent the new presence/absence of signals in the area and to pay attention.
The adjacent areas are within the same time period, and the intersection of the contained offshore mobile service identification number sets. To represent ship information of the area entering and exiting the sea area for a certain period of time for canceling the new appearance/disappearance ship signals in the areas.
Preferably, the conservation rule includes:
the change amount of a certain area or area combination=the sum of the change amounts of adjacent areas=the number of offshore mobile service identities of the adjacent lines of the certain area or area combination.
Judging whether an abnormal signal exists according to the conservation rule.
Preferably, the process of the set operation is as follows:
a1: dividing a standard time as a detection time standard;
a2: calculating a difference set of the offshore mobile service identification number sets of each sea area at intervals of a standard time; if the sea area has a new generated/disappeared ship target, proceeding to step A3;
a3: performing intersection calculation on the offshore mobile service identification number contained in the target sea area and the offshore mobile service identification number of the adjacent sea area in the standard time period to obtain the offshore mobile service identification number confirmed to enter and exit the sea area;
a4: and (3) obtaining the offshore mobile service identification number which cannot be confirmed, and preliminarily confirming the offshore mobile service identification number as a ship with illegal risk.
The number of the automatic ship identification systems which should appear in different time periods is prejudged, and the preliminary judgment is that the number is increased and decreased. The marine mobile service identification number is assisted with intersection and difference set operation, and the result is utilized to clear the data of the automatic ship identification system, so that the purposes of removing repeated signals and finding abnormal signals are realized.
Preferably, the step S3 includes the following steps:
s301: on a longitude axis and a latitude axis, respectively establishing a line segment tree taking the base number of a difference set of the sea mobile service identification numbers of a single sea area as a node, wherein leaf nodes represent the change amount of the sea mobile service identification numbers of a certain area, and root nodes represent the change amount of the sea mobile service identification numbers of all sea areas;
s302: single-point updating operation is carried out on each leaf node at regular intervals, so that each node of the line segment tree keeps record of the latest variable quantity of the base number of the offshore mobile service identification number set of each region at all times;
s303: inquiring the line segment tree root nodes of the longitudes and the latitudes at the designated time nodes; if the root node variance is not zero, attention is paid to the non-conservative offshore mobile service identification number and is considered as a vessel with a risk of illegal use.
Preferably, the conservation rule further includes that the root node of the line segment tree is 0. Judging whether an abnormal signal exists according to the conservation rule.
Preferably, the AIS emission rule is utilized to verify the signals before and after the signal is generated/disappeared, if the signal is in a set reasonable area when the signal is generated/disappeared and the speed and the angular speed meet the requirements of departure and anchoring behaviors, the signal is considered to be a qualified signal, and the signal is moved out of a document recording the abnormal offshore mobile service identification number. The AIS must have the case of the generation and disappearance of the offshore mobile service identification number at the port, the docking station, etc.
Preferably, the sea area is divided into geohash areas by longitude and latitude. Accelerating retrieval of vessels.
The beneficial effects of the application are as follows:
1. the track processing is not carried out, the problem of signal persistence of the ship automatic identification system is directly processed by utilizing the point position information of the ship automatic identification system, and the huge limitation of track clustering in use is avoided.
2. In the judging stage, the algorithm combines the design of the ship automatic identification system with different signal transmission intervals for different running states and different speeds, and prejudges the number of the ship automatic identification systems which should appear in different time periods, wherein the preliminary judgment is that the number is increased and decreased. The offshore mobile service identification number is assisted with intersection and difference set operation, and the result is utilized to clear the data of the automatic ship identification system, so that the purposes of removing repeated signals and finding abnormal signals are realized; the accuracy of judgment is improved, and the cost of machine learning training is reduced.
Drawings
Fig. 1 is a flow chart of a method of detecting an AIS abnormal switch of the present application.
Fig. 2 is a schematic view of a segmented sea area according to the present application.
Fig. 3 is a schematic diagram of a line segment tree according to the present application.
Detailed Description
The technical scheme of the application is further specifically described below through examples and with reference to the accompanying drawings.
Examples:
the method for detecting an AIS abnormal switch in this embodiment, as shown in fig. 1, includes the following steps:
s1: dividing the sea area into a plurality of grid-shaped areas by using longitude and latitude, establishing a document for recording the change quantity matrix of the sea mobile service identification numbers in each area and between the areas, and setting conservation rules.
The sea area is divided into grid-like areas using longitude and latitude, as shown in fig. 2. The sea area is geohash partitioned for use in accelerating retrieval of vessels.
geohash is a type of geolocation code used to query nearby POIs (Point of Interest, points of interest). In a geographic information system, a POI may be a house, a shop, a post, a bus stop, etc.
geohash is also an algorithm idea by viewing the earth as a two-dimensional plan, recursively slicing the plane into smaller modules, then encoding the spatial longitude and latitude data to generate a binary string, and converting it to a string by base 32. Eventually, nearby target elements are queried by comparing the similarity of the values of the geohash.
geohash is capable of encoding two-dimensional spatial latitude and longitude data into a string. Each string represents a rectangular region where all points share the same geohash string. In this way, all AIS with the position falling in a certain area can be cached by taking the geohash of the area as a Key, and after the algorithm is activated, AIS signals in the area can be rapidly positioned only by the geohash calculation.
The recorded document content includes:
the difference set of the contained offshore mobile service identification number sets in different time periods of each region; to represent the new presence/absence of signals in the area and to pay attention.
The adjacent areas are within the same time period, and the intersection of the contained offshore mobile service identification number sets. To represent ship information of the area entering and exiting the sea area for a certain period of time for canceling the new appearance/disappearance ship signals in the areas.
The conservation rules include:
the change amount of a certain area or area combination=the sum of the change amounts of adjacent areas=the number of offshore mobile service identities of the adjacent lines of the certain area or area combination.
Judging whether an abnormal signal exists according to the conservation rule.
S2: and carrying out collective operation according to each region and the change quantity matrix of the offshore mobile service identification number among the regions to obtain the ship which is preliminarily judged to be at illegal risk.
And acquiring AIS data of each sea area, and cleaning the AIS data. And solving an intersection of adjacent sea areas, solving a difference set of each sea area, and performing a set operation.
The specific process of the collective operation is as follows:
a1: dividing a standard time as a detection time standard.
A2: calculating a difference set of the offshore mobile service identification number sets of each sea area at intervals of a standard time; if there is a new/disappeared ship target in the sea area, step A3 is entered.
A3: and carrying out intersection calculation on the offshore mobile service identification number contained in the target sea area and the offshore mobile service identification number of the adjacent sea area in the standard time period to obtain the offshore mobile service identification number confirmed to enter and exit the sea area.
A4: and (3) obtaining the offshore mobile service identification number which cannot be confirmed, and preliminarily confirming the offshore mobile service identification number as a ship with illegal risk.
S3: and respectively establishing line segment trees which take the base numbers of the difference sets of the offshore mobile service identification numbers of the single area as nodes on a longitude axis and a latitude axis, carrying out updating operation on each leaf node, and carrying out query operation on the root node.
The line segment tree is a binary search tree, which divides a segment into a plurality of unit segments, and each node stores a segment. The system has powerful functions and supports operations such as interval summation, interval maximum value, interval modification, single-point modification and the like. The idea of line segment tree is very similar to the idea of divide and conquer. Each node of the line segment tree stores information of a section [ l..r ], where the leaf node l=r. The general idea is that: a large segment is divided equally into 2 cells, each cell is divided equally into 2 smaller segments … …, and so on, until L in each segment is equal to R (such that the segment contains information for only one node and cannot be divided). Modification and inquiry of a large section are realized by modifying and inquiring the sections.
Maintaining a line segment tree by using the aggregate variable quantity, wherein the step S3 comprises the following steps:
s301: on the longitude axis and the latitude axis, a line segment tree with the base of the difference set of the individual sea area offshore mobile service identification numbers as the node is established, respectively.
As shown in fig. 3, the leaf node represents the amount of change of the offshore mobile service identification number of a certain area; the root node represents the amount of change in the offshore mobile service identification number for the entire sea area. The root node is therefore 0, which is yet another conservation factor in the setting.
S302: and (3) performing single-point updating operation on each leaf node at regular intervals, so that each node of the line segment tree keeps record of the latest change quantity of the base number of the offshore mobile service identification number set of each region.
S303: and at the designated time node, inquiring the line segment tree root node of the longitude and the latitude.
If the root node variance is not zero, attention is paid to the non-conservative offshore mobile service identification number and is considered as a vessel with a risk of illegal use.
S4: and verifying signals before and after the generation/disappearance of the offshore mobile service identification number by utilizing an AIS transmitting rule, and judging whether the ship is a ship with illegal risks.
The types of abnormal ships are inversely pushed by using the AIS emission time interval, and AIS update frequencies of different ship types are shown in table 1:
TABLE 1 AIS update frequency for different ship types
Because the AIS inevitably has the conditions of generating and disappearing of the offshore mobile service identification number at ports, poise points and the like, the AIS emission rule is utilized to verify the signals before and after the generation/disappearing of the signals, if the signals are in reasonable areas such as ports, anchor points and the like and the speed, the angular speed and the like meet the requirements of behaviors such as departure, anchoring and the like when the signals are generated/disappeared, the signals are considered to be qualified signals, and the signals are removed from a document recording the abnormal offshore mobile service identification number.
The scheme of the embodiment does not process the track, directly utilizes the point location information of the ship automatic identification system to process the signal persistence problem of the ship automatic identification system, and separates the detection, calculation and judgment of the ship automatic identification system into different data structures for processing so as to achieve the effect of real-time detection. In the judging stage, the algorithm combines the design of the ship automatic identification system with different signal transmission intervals for different running states and different speeds, and prejudges the number of the ship automatic identification systems which should appear in different time periods, wherein the preliminary judgment is that the number is increased and decreased. The marine mobile service identification number is assisted with intersection and difference set operation, and the result is utilized to clear the data of the automatic ship identification system, so that the purposes of removing repeated signals and finding abnormal signals are realized.
It should be understood that the examples are only for illustrating the present application and are not intended to limit the scope of the present application. Furthermore, it should be understood that various changes and modifications can be made by one skilled in the art after reading the teachings of the present application, and such equivalents are intended to fall within the scope of the application as defined in the appended claims.
Claims (6)
1. A method of detecting an AIS abnormal switch comprising the steps of:
s1: dividing a sea area into a plurality of grid-shaped areas by using longitude and latitude, establishing a document for recording each area and a change matrix of the marine mobile service identification number between the areas, and setting a conservation rule;
s2: performing collective operation according to each region and the change quantity matrix of the offshore mobile service identification number between regions to obtain a ship which is primarily judged to have illegal risks;
the process of the set operation is as follows:
a1: dividing a standard time as a detection time standard;
a2: calculating a difference set of the offshore mobile service identification number sets of each sea area at intervals of a standard time; if the sea area has a new generated/disappeared ship target, proceeding to step A3;
a3: performing intersection calculation on the offshore mobile service identification number contained in the target sea area and the offshore mobile service identification number of the adjacent sea area in the standard time period to obtain the offshore mobile service identification number confirmed to enter and exit the sea area;
a4: c, solving the difference set of the difference set and the intersection set obtained in the step A2 and the step A3 to obtain the marine mobile service identification number which cannot be confirmed, and preliminarily confirming the marine mobile service identification number as a ship with illegal risks;
s3: respectively establishing line segment trees with the base numbers of the difference sets of the offshore mobile service identification numbers of the single area as nodes on a longitude axis and a latitude axis, updating each leaf node, and inquiring the root node;
the step S3 comprises the following steps:
s301: on a longitude axis and a latitude axis, respectively establishing a line segment tree taking the base number of a difference set of the sea mobile service identification numbers of a single sea area as a node, wherein leaf nodes represent the change amount of the sea mobile service identification numbers of a certain area, and root nodes represent the change amount of the sea mobile service identification numbers of all sea areas;
s302: single-point updating operation is carried out on each leaf node at regular intervals, so that each node of the line segment tree keeps record of the latest variable quantity of the base number of the offshore mobile service identification number set of each region at all times;
s303: inquiring the line segment tree root nodes of the longitudes and the latitudes at the designated time nodes; if the change amount of the root node is not zero, paying attention to the non-conservation offshore mobile service identification number and considering the non-conservation offshore mobile service identification number as a ship with illegal risks;
s4: and verifying signals before and after the generation/disappearance of the offshore mobile service identification number by utilizing an AIS transmitting rule, and judging whether the ship is a ship with illegal risks.
2. The method of detecting an AIS anomaly switch of claim 1, wherein the recorded document content comprises:
the difference set of the contained offshore mobile service identification number sets in different time periods of each region;
the adjacent areas are within the same time period, and the intersection of the contained offshore mobile service identification number sets.
3. A method of detecting an AIS anomaly switch according to claim 1 or 2 wherein the conservation rules comprise:
the change amount of a certain area or area combination=the sum of the change amounts of adjacent areas=the number of offshore mobile service identities of the adjacent lines of the certain area or area combination.
4. A method of detecting an AIS anomaly switch according to claim 3 wherein the conservation rule further comprises a root node of the line segment tree of 0.
5. A method for detecting an AIS abnormal switch according to claim 1 or 2, wherein the signal before and after the signal is generated/disappeared is verified by the AIS emission rule, if the signal is in a set reasonable area when it is generated/disappeared, and the speed and angular speed meet the requirements of departure and anchoring, the signal is considered as a qualified signal, and is removed from the document recording the abnormal offshore mobile service identification number.
6. A method of detecting AIS anomaly switches according to claim 1 or 2, wherein the sea area is geohash partitioned using latitude and longitude.
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Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101702265A (en) * | 2009-10-22 | 2010-05-05 | 上海埃威航空电子有限公司 | Method for rapidly identifying ship name of ship-borne automatic identification system |
| CN102800215A (en) * | 2012-08-01 | 2012-11-28 | 上海海事大学 | AIS (Automatic Identification System)-data-based regional idle ship counting system and realization method thereof |
| KR20130093245A (en) * | 2012-02-14 | 2013-08-22 | (주)지엠티 | Suspected smuggling vessel ais analysis system and it's analysis method on the basis of multi-sensors and sailing pattern analysis |
| CN107566802A (en) * | 2017-09-27 | 2018-01-09 | 福建四创软件有限公司 | It is a kind of based on AIS, video monitoring port in ship GIS positional information searching methods |
| JP2019175462A (en) * | 2018-03-29 | 2019-10-10 | コリア インスティテュート オブ オーシャン サイエンス テクノロジー | System for automatically identifying abnormal navigation state of ship with unsupervised learning, and method for the same |
| CN110536116A (en) * | 2019-08-28 | 2019-12-03 | 海华电子企业(中国)有限公司 | A kind of ship sensing and tracking system and method |
| CN112347211A (en) * | 2020-11-05 | 2021-02-09 | 北明软件有限公司 | Method and system for processing data of automatic ship identification system and storage medium |
| CN112373642A (en) * | 2020-10-30 | 2021-02-19 | 东南大学 | Inland ship overtaking behavior detection and tracking method based on ship field |
| CN113870620A (en) * | 2021-10-19 | 2021-12-31 | 遨海科技有限公司 | Ship identification method for simultaneously starting multiple AIS (automatic identification system) devices |
| CN114742128A (en) * | 2022-03-10 | 2022-07-12 | 交通运输部水运科学研究所 | Method, device, terminal and storage medium for identifying ship abnormal behavior |
| CN116049208A (en) * | 2023-01-19 | 2023-05-02 | 中远海运科技股份有限公司 | Ship IMO number and MMSI number automatic updating and matching method and system |
| CN116257565A (en) * | 2022-12-22 | 2023-06-13 | 中国船舶集团有限公司系统工程研究院 | Ship abnormal behavior detection method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR2979736B1 (en) * | 2011-09-05 | 2013-08-30 | Centre Nat Etd Spatiales | METHOD FOR DETECTING SIA MESSAGES |
-
2023
- 2023-08-29 CN CN202311093952.8A patent/CN116828391B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101702265A (en) * | 2009-10-22 | 2010-05-05 | 上海埃威航空电子有限公司 | Method for rapidly identifying ship name of ship-borne automatic identification system |
| KR20130093245A (en) * | 2012-02-14 | 2013-08-22 | (주)지엠티 | Suspected smuggling vessel ais analysis system and it's analysis method on the basis of multi-sensors and sailing pattern analysis |
| CN102800215A (en) * | 2012-08-01 | 2012-11-28 | 上海海事大学 | AIS (Automatic Identification System)-data-based regional idle ship counting system and realization method thereof |
| CN107566802A (en) * | 2017-09-27 | 2018-01-09 | 福建四创软件有限公司 | It is a kind of based on AIS, video monitoring port in ship GIS positional information searching methods |
| JP2019175462A (en) * | 2018-03-29 | 2019-10-10 | コリア インスティテュート オブ オーシャン サイエンス テクノロジー | System for automatically identifying abnormal navigation state of ship with unsupervised learning, and method for the same |
| CN110536116A (en) * | 2019-08-28 | 2019-12-03 | 海华电子企业(中国)有限公司 | A kind of ship sensing and tracking system and method |
| CN112373642A (en) * | 2020-10-30 | 2021-02-19 | 东南大学 | Inland ship overtaking behavior detection and tracking method based on ship field |
| CN112347211A (en) * | 2020-11-05 | 2021-02-09 | 北明软件有限公司 | Method and system for processing data of automatic ship identification system and storage medium |
| CN113870620A (en) * | 2021-10-19 | 2021-12-31 | 遨海科技有限公司 | Ship identification method for simultaneously starting multiple AIS (automatic identification system) devices |
| CN114742128A (en) * | 2022-03-10 | 2022-07-12 | 交通运输部水运科学研究所 | Method, device, terminal and storage medium for identifying ship abnormal behavior |
| CN116257565A (en) * | 2022-12-22 | 2023-06-13 | 中国船舶集团有限公司系统工程研究院 | Ship abnormal behavior detection method |
| CN116049208A (en) * | 2023-01-19 | 2023-05-02 | 中远海运科技股份有限公司 | Ship IMO number and MMSI number automatic updating and matching method and system |
Non-Patent Citations (3)
| Title |
|---|
| AIS在多船实时避碰检测中的应用;解立志;潘启灏;;船海工程(第05期);全文 * |
| 基于网格热度值的船舶规律路径提取算法;李建江;陈玮;李明;张凯;刘雅俊;;计算机研究与发展(第05期);全文 * |
| 基于船舶自动识别系统信息和Hough变换的海上船舶航道提取;陈宏昆;察豪;刘立国;孟薇;;计算机应用(第11期);全文 * |
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