CN117664122A - Ship track monitoring method and system - Google Patents
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Abstract
The invention provides a ship track monitoring method and a ship track monitoring system, wherein ship history AIS data in a certain time period are collected firstly, a ship track point set is obtained according to ship longitude and latitude information in the history AIS data, then the starting time corresponding to the ship track point which runs first in the track point set is used as a current time variable, the distance between each track point and the last point and the position of a ship at a certain moment in the time period are calculated, the position of the ship at any moment is obtained by changing the time value of the current time variable, a ship history track is formed according to the position of the ship at any moment, a ship prediction track is calculated by adopting a path planning algorithm, whether collision risks exist in the history track and the ship prediction track or not is judged by adopting a ship collision recognition algorithm, and collision risk conditions are displayed, so that the ship history track and the prediction track are monitored, and the accident risk occurrence probability can be effectively reduced.
Description
Technical Field
The invention relates to the technical field of ship track playback and prediction display, in particular to a ship track monitoring method and a ship track monitoring system.
Background
As global commerce increases more frequently, import and export services continue to increase, so does the demand for shipping services. In this case, the number of marine vessels is also increasing, and the frequency of transmitting vessel safety accidents is also increasing.
The existing dynamic monitoring method is mainly used for carrying out position monitoring and displaying on the running track of the ship according to the existing AIS position information of the ship, so that the current position information of the ship can be only checked, and the previous track movement trend cannot be conveniently checked, monitored and displayed. And the future track of the ship cannot be predicted and planned by combining the track prediction method and the like, and the navigation track is adjusted, so that various safety accidents are avoided, and the accident risk occurrence probability is reduced.
Disclosure of Invention
The invention provides a ship track monitoring method, which aims to solve the problems that the prior dynamic monitoring process of the ship track cannot check the motion trend of the ship past track, and the future track of the ship cannot be predicted, planned and adjusted to cause the increase of accident risk and the like. The invention also relates to a ship track monitoring system.
The technical scheme of the invention is as follows:
the ship track monitoring method is characterized by comprising the following steps of:
and a data acquisition step: acquiring historical AIS data of the ship within a certain time period, sequentially carrying out sparsification processing and smoothing processing on the historical AIS data, and obtaining a ship track point set according to the longitude and latitude of the ship in the historical AIS data after the sparsification processing and smoothing processing;
a history track forming step: taking the starting time corresponding to the ship track point which runs first in the track point set as a current time variable, calculating the distance between each track point and the last point in the track point set according to the ship longitude and latitude in the history AIS data after the thinning processing and smoothing processing, finding out two track points which are positioned in the track set and are adjacent to the ship position at a certain time in the time period, calculating the sailing distance of the ship at the moment according to the current sailing speed of the two track points and the starting time and the ending time of the time period, calculating the position of the ship at the moment in the time period according to the sailing distance of the ship at the moment and the calculated distance between each point and the last point in all track points, and further obtaining the ship position at any moment in the time period by changing the time value of the current time variable, and forming a ship history track according to the ship position at any moment;
track prediction: training a ship track point set by adopting a path planning algorithm to obtain a track prediction model, predicting predicted track data of the ship in a certain future time period according to the track prediction model, performing sparsification processing and smoothing processing on the predicted track data in the certain future time period according to a data acquisition step and a history track forming step to obtain the ship track point set, calculating the distance between each track point and the last point in the track point set, further calculating the position of the ship in any moment in the certain future time period, and forming a ship predicted track according to the position of the ship in any moment in the certain future time period;
risk judging and displaying: based on the distance between each track point and the last point and the ship length and the ship width in the history AIS data, whether the ship in the ship history track and the ship prediction track has collision risk or not is automatically judged by adopting a ship collision recognition algorithm, if the ship has collision risk, the collision risk condition is displayed, and the monitoring of the ship history track and the ship prediction track is realized.
Preferably, in the data acquisition step, the thinning processing includes:
taking out an AIS data point every interval of time, acquiring the sailing speed of the ship corresponding to the AIS data point, judging whether the sailing speed is normal, if so, expanding the time interval range, judging whether the AIS data point is located at a key position or in a special sailing state, if so, marking and reserving, judging whether the ship sails in the ocean, and if so, continuing expanding the time interval range; and judging whether the ship is in the special area, if so, reducing the time interval range.
Preferably, in the data acquisition step, the smoothing processing includes: and carrying out track complement on adjacent track points with overlarge intervals after the sparsification treatment, and calculating the number of the complemented track points by adopting a great circle route algorithm.
Preferably, the critical locations include strait, canal, and land boundary locations, and the special sailing conditions include stranding, anchoring, berthing, and anomalies.
Preferably, in the data acquisition step, the data after the thinning processing is further cached by using a redis memory database, so as to speed up the query speed of the data.
The ship track monitoring system is characterized by comprising a data acquisition module, a history track forming module, a track prediction module and a risk judging and displaying module, wherein the data acquisition module is respectively connected with the history track forming module and the track prediction module, the history track forming module and the track prediction module are both connected with the risk judging and displaying module,
the data acquisition module acquires the historical AIS data of the ship in a certain time period, performs sparsification and smoothing on the historical AIS data, and obtains a ship track point set according to the longitude and latitude information of the ship in the historical AIS data after the sparsification and smoothing;
the historical track forming module takes the starting time corresponding to the track point of the ship which runs first in the track point set as a current time variable, calculates the distance between each track point and the last point in the track point set according to the longitude and latitude of the ship in the historical AIS data after the thinning processing and smoothing processing, finds out two track points which are positioned in the track set and are adjacent to the position of the ship in the time period at a certain moment, calculates the sailing distance of the ship in the moment according to the current sailing speed of the two track points and the starting time and the ending time of the time period, calculates the position of the ship in the time period according to the sailing distance of the ship in the moment and the calculated distance between each point and the last point in all track points, and further obtains the position of the ship in any moment in the time period by changing the time value of the current time variable in the time period, and forms the historical track of the ship according to the position of the ship in any moment;
the track prediction module is used for training the ship track point set by adopting a path planning algorithm to obtain a track prediction model, predicting predicted track data of the ship in a certain future time period according to the track prediction model, performing sparsification processing and smoothing processing on the predicted track data in the certain future time period according to the operation of the data acquisition module and the history track forming module to obtain the ship track point set, calculating the distance between each track point and the last point in the track point set, further calculating the position of the ship in any moment in the certain future time period, and forming a ship predicted track according to the position of the ship in any moment in the certain future time period;
and the risk judging and displaying module is used for automatically judging whether the ship in the ship historical track and the ship predicted track has collision risk or not by adopting a ship collision recognition algorithm based on the distance between each track point and the last point and the ship length and the ship width in the historical AIS data, and displaying the collision risk condition if the ship has collision risk, so that the monitoring of the ship historical track and the ship predicted track is realized.
Preferably, in the data acquisition module, the thinning processing includes:
taking out an AIS data point every interval of time, acquiring the sailing speed of the ship corresponding to the AIS data point, judging whether the sailing speed is normal, if so, expanding the time interval range, judging whether the AIS data point is located at a key position or in a special sailing state, if so, marking and reserving, judging whether the ship sails in the ocean, and if so, continuing expanding the time interval range; and judging whether the ship is in the special area, if so, reducing the time interval range.
Preferably, in the data acquisition module, the smoothing processing includes: and carrying out track complement on adjacent track points with overlarge intervals after the sparsification treatment, and calculating the number of the complemented track points by adopting a great circle route algorithm.
Preferably, the critical locations include strait, canal, and land boundary locations, and the special sailing conditions include stranding, anchoring, berthing, and anomalies.
Preferably, in the data acquisition module, the data after the thinning processing is further cached by using a redis memory database, so as to speed up the query speed of the data.
The beneficial effects of the invention are as follows:
the invention provides a ship track monitoring method, which is based on historical AIS data in a certain time period of a ship, performs sparsification processing and smoothing processing on the historical AIS data, obtains a ship track point set according to ship longitude and latitude information in the historical AIS data after the sparsification processing and the smoothing processing, then uses the starting time corresponding to the ship track point which runs first in the track point set as a current moment time variable, calculates the distance between each track point and the last point in the track point set according to the ship longitude and latitude in the historical AIS data, calculates the sailing distance of the ship at a certain moment, calculates the position of the ship at the moment in the time period according to the ratio of the sailing distance of the ship at the moment to the calculated distances between each point in all track points and the last point, and further obtains the position of the ship at any moment in the time period by changing the time value of the current moment time variable, and forms a ship history track according to the position of the ship at any moment in the history time; and then training the ship track point set by adopting a path planning algorithm to obtain a track prediction model, predicting the predicted track data of the ship in a certain time period in the future according to the track prediction model, further predicting the position of the ship at any moment in the future, forming a predicted track according to the predicted position, finally automatically judging whether the ship in the ship historical track and the predicted track has collision risk or not based on the distance between each track point and the last point and the ship length and the ship width in the historical AIS data by adopting a ship collision recognition algorithm, and displaying the collision risk condition if the ship has collision risk, so that the monitoring of the ship historical track and the predicted track is realized, the future navigation track can be planned and adjusted, and the accident risk occurrence probability is reduced. The invention has the time playback function, can more conveniently, intuitively and clearly check and monitor the historical track data of the ship, can predict various risk events in a period of time by combining the predicted track data, can avoid certain risk events to a certain extent, can forecast the path planning for the monitored ship, saves the running cost of the ship and avoids the navigation accident risk.
The invention also relates to a ship track monitoring system which corresponds to the ship track monitoring method and can be understood as a system for realizing the ship track monitoring method, and the system comprises a data acquisition module, a history track forming module, a track prediction module and a risk judgment and display module which are sequentially connected, wherein the modules work cooperatively with each other, the ship history track and the predicted track are obtained based on ship history AIS data and by adopting a specific calculation method and a path planning algorithm, and the ship collision recognition algorithm is adopted to judge, monitor and display risks of the ship history track and the predicted track, so that future navigation tracks can be planned and adjusted, and the accident risk occurrence probability is reduced.
Drawings
Fig. 1 is a flow chart of a ship track monitoring method of the present invention.
Detailed Description
The present invention will be described below with reference to the accompanying drawings.
The invention relates to a ship track monitoring method, the flow chart of which is shown in figure 1, comprising the following steps in sequence:
data acquisition and processing: and acquiring historical AIS data of the ship in a certain time period, performing sparsification processing and smoothing processing on the historical AIS data, and obtaining a ship track point set according to ship longitude and latitude information in the sparsified and smoothed historical AIS data.
Specifically, firstly, inquiring ship history AIS data in a certain time period from a database, and in order to improve the inquiring efficiency, the database preferably adopts a database supporting time slicing, such as Mysql, TSDB and the like; after historical AIS data are inquired, sparse processing is carried out on the inquired data by using a sparse algorithm, namely, one AIS data point is taken out every 5 minutes, the sailing speed of a ship corresponding to the AIS data point is obtained, whether the sailing speed of the ship is normal sailing speed or not is judged, if yes, the time interval range is enlarged, whether the current AIS data point is located near or in a critical position (strait, panama canal, suy canal and land boundary) or not is judged, if the current AIS data point is located near or in the strait, panama canal, suy canal and land boundary or not, marking and retaining are carried out, and meanwhile, whether the ship corresponding to the AIS data point is in a special sailing state, namely, whether the ship is in special sailing state such as strait is in sea, moored, berth, abnormal and the like is also required to mark and retain, and then whether the ship sails in sea is judged, if sailing in sea, the time interval range is continued to be enlarged, but special conditions such as low sailing speed and the like are required, and whether the ship is located in a special area, if the ship is located in the area such as Panama, the time interval range of the ship is required to be taken out is reduced; after the sparse processing, smoothing is further carried out on the historical AIS data, track complement is carried out on adjacent track points with overlarge intervals after the sparse processing, the number of the completed track points is calculated by adopting a great circle route algorithm, and then the smoothed historical AIS data can be displayed through a browser, a computer, a mobile phone and the like.
It should be noted that, before displaying, the history AIS data after the smoothing process needs to be preprocessed, which specifically includes:
1) Converting the ship longitude and latitude data in the history AIS data into longitude and latitude data required by a map library, namely converting the ship longitude and latitude data in the history AIS data into longitude and latitude data required by the map library according to the accuracy requirement of the map library on longitude and latitude, and converting a timestamp in the history AIS data into a millisecond timestamp from seconds;
2) Calculating the distance between any two track points through ship longitude and latitude data in historical AIS data, and writing the calculated distance into the AIS data object after preprocessing;
3) Converting units of the navigational speed data recorded in the historical AIS data;
4) And acquiring the data such as the length, the width and the like of the ship through MMSI codes in the historical AIS data.
Preferably, the data after the thinning processing is cached by using the redis memory database, and the data is cached by using the redis memory database, so that the data query speed is increased, the calculated amount is reduced, and the cache has an expiration time, and when the cache is repeatedly used, the expiration time is delayed backwards, and the unused data is preferentially expired.
A history track forming step: the method comprises the steps of taking the starting time corresponding to a ship track point which runs first in a track point set as a current time variable, calculating the distance between each track point and the last point in the track point set according to the ship longitude and latitude in the history AIS data after sparsification processing and smoothing processing, finding out two track points which are positioned in the track set and are adjacent to the ship position at a certain time in the time period, calculating the sailing distance of the ship at the moment according to the current sailing speed of the two track points and the starting time and the ending time of the time period, calculating the ship position at the moment according to the sailing distance of the ship at the moment and the calculated distance between each point and the last point in all track points, and further obtaining the ship position at any moment in the time period by changing the time value of the current time variable in the time period, and forming the ship history track according to the ship position at any moment.
Specifically, the recording time of each track point in the track point set is searched in a circulating way, the starting time of the first running ship corresponding to the track point in the track set is found out and used as the time variable of the current moment, and the ship is drawn into the map component according to the information such as the length and the width of the ship.
Then, calculating the distance between each track point and the last point in the track point set according to the ship longitude and latitude in the history AIS data after the sparsification treatment and the smoothing treatment, and obtaining two points P 1 、P 2 The distance between them is calculated according to the following formula:
wherein R is the earth radius of 6372.8km, p1:is longitude, lambda is latitude, delta lambda is two-point difference in altitude, +>Is a two-point longitude difference.
And finding out the track point p2 just above the time t and the track point p1 just above the time t by circularly finding out the recording time of the track points in the track point set, wherein the longitude and latitude range of the time t is positioned between the two points.
Then, the navigation speeds of the two found trajectory points are obtained, denoted as v2 and v1, and the speed change between the two points is regarded as a uniform speed change, so that the distance S travelled between the two points at the time t satisfies:
S＝v1*t+(at) 2 /2 (2)
where a is acceleration, satisfying a= (v 2-v 1)/(t 2-t 1), t1 is the start time of the time period, and t2 is the end time.
Calculating the sailing distance between two points at the moment of the ship t, obtaining the ratio of the sailing distance at the moment of the ship t to the distance between each point in all track points and the previous point, marking as p, calculating the position of the ship at the moment of the current t according to the ratio, and finally obtaining the position of the ship at any moment in the time period by changing the time value of the time variable at the current moment, and forming a ship historical track according to the position of the ship at any moment.
Track prediction: training a ship track point set by adopting a path planning algorithm to obtain a track prediction model, predicting predicted track data of the ship in a future time period according to the track prediction model, setting a current time variable, finding out the position of the ship in the predicted track set, wherein the calculation mode is similar to a historical track, namely, performing sparsification processing and smoothing processing on the predicted track data in the future time period according to a data acquisition step and a historical track formation step to obtain the ship track point set, calculating the distance between each track point and the last point in the track point set, further calculating the position of the ship in the future time period at any moment, and forming a ship predicted track according to the position of the ship in the future time period at any moment.
Risk judging and displaying: based on the distance between each track point and the last point and the ship length and the ship width in the history AIS data, whether the ship in the ship history track and the ship prediction track has collision risk or not is automatically judged by adopting a ship collision recognition algorithm, if the ship has collision risk, the collision risk condition is displayed, and the monitoring of the ship history track and the ship prediction track is realized.
In addition, the current ship position can be monitored according to the following steps:
1) Acquiring real-time AIS data of the ship, and acquiring current position information of the ship, length and width of the ship and other information in the real-time AIS data;
2) Processing the acquired information, converting the ship longitude and latitude data in the real-time AIS data into longitude and latitude data required by a map library, namely converting the ship longitude and latitude data in the real-time AIS data into longitude and latitude data required by the map library according to the precision requirement of the map library on longitude and latitude, and converting a timestamp in the real-time AIS data into a millisecond timestamp from second;
3) Drawing a current position graph of the ship;
4) The 1,2,3 steps were repeated every 5 minutes for data redrawing.
In the process of drawing the current position graph of the ship, risk events can be calculated and identified, for example, events such as typhoons, pirates, collisions and the like are identified.
It can be understood that in the risk judging process, whether collision risks exist in the historical track and the predicted track or not can be judged, and whether typhoon risks, wave heights and other weather risks exist in the historical track and the predicted track or not can be judged.
The method is characterized in that the method comprises the steps of collecting ship history AIS data, completing tracks, performing track sparse processing, preprocessing data, performing caching processing, forming a history track, displaying and performing track playback monitoring; the method is characterized in that a route prediction algorithm (path planning algorithm), a training track prediction model, route prediction data query, track completion, track sparse processing, data preprocessing, caching, ship prediction track formation, display, prediction monitoring, platform avoidance simulation and collision prediction are executed for the predicted track.
The invention also relates to a ship track monitoring system which corresponds to the ship track monitoring method and can be understood as a system for realizing the method, and the system comprises a data acquisition module, a history track forming module, a track prediction module and a risk judging and displaying module, wherein the data acquisition module is respectively connected with the history track forming module and the track prediction module, the history track forming module and the track prediction module are both connected with the risk judging and displaying module,
the data acquisition module acquires the historical AIS data of the ship in a certain time period, performs sparsification and smoothing on the historical AIS data, and obtains a ship track point set according to the longitude and latitude information of the ship in the historical AIS data after the sparsification and smoothing;
the history track forming module takes the starting time corresponding to the first running ship track point in the track point set as a current time variable, calculates the distance between each track point and the last point in the track point set according to the ship longitude and latitude in the history AIS data after the thinning processing and smoothing processing, finds out two track points positioned in the track set and adjacent to the ship position at a certain moment in the time period, calculates the sailing distance of the ship at the moment according to the current sailing speed of the two track points and the starting time and the ending time of the time period, calculates the ship position at the moment in the time period according to the sailing distance of the ship at the moment and the calculated distance between each point in all track points and the last point, and further obtains the ship position at any moment in the time period by changing the time value of the current time variable, and forms a ship history track according to the ship position at any moment;
the track prediction module is used for training the ship track point set by adopting a path planning algorithm to obtain a track prediction model, predicting predicted track data of the ship in a certain future time period according to the track prediction model, performing sparsification processing and smoothing processing on the predicted track data in the certain future time period according to the operation of the data acquisition module and the history track forming module to obtain the ship track point set, calculating the distance between each track point and the last point in the track point set, further calculating the position of the ship in any moment in the certain future time period, and forming a ship predicted track according to the position of the ship in any moment in the certain future time period;
and the risk judging and displaying module is used for automatically judging whether the ship in the ship historical track and the ship predicted track has collision risk or not by adopting a ship collision recognition algorithm based on the distance between each track point and the last point and the ship length and the ship width in the historical AIS data, and displaying the collision risk condition if the ship has collision risk, so that the monitoring of the ship historical track and the ship predicted track is realized.
Preferably, in the data acquisition module, the thinning processing of the historical AIS data and the predicted track data includes:
taking out an AIS data point every interval of time, acquiring the sailing speed of the ship corresponding to the AIS data point, judging whether the sailing speed is normal, if so, expanding the time interval range, judging whether the AIS data point is located at a key position or in a special sailing state, if so, marking and reserving, judging whether the ship sails in the ocean, and if so, continuing expanding the time interval range; and judging whether the ship is in the special area, if so, reducing the time interval range.
Preferably, in the data acquisition module, the smoothing processing includes: and carrying out track complement on adjacent track points with overlarge intervals after the sparsification treatment, and calculating the number of the complemented track points by adopting a great circle route algorithm.
Preferably, the critical locations include strait, canal, and land boundary locations, and the special sailing conditions include stranding, anchoring, berthing, and anomalies.
Preferably, in the data acquisition module, the data after the thinning processing is further cached by using the redis memory database so as to speed up the query speed of the data.
The invention provides an objective and scientific ship track monitoring method and system, which are based on ship history AIS data, acquire a ship history track and a predicted track by adopting a specific calculation method and a path planning algorithm, and perform risk judgment, monitoring and display on the ship history track and the predicted track by adopting a ship collision recognition algorithm, so that future navigation tracks can be planned and adjusted, the accident risk occurrence probability can be reduced, the future navigation tracks can be planned and adjusted, and the accident risk occurrence probability can be reduced.
It should be noted that the above-described embodiments will enable those skilled in the art to more fully understand the invention, but do not limit it in any way. Therefore, although the present invention has been described in detail with reference to the drawings and examples, it will be understood by those skilled in the art that the present invention may be modified or equivalent, and in all cases, all technical solutions and modifications which do not depart from the spirit and scope of the present invention are intended to be included in the scope of the present invention.
Claims (10)
1. The ship track monitoring method is characterized by comprising the following steps of:
and a data acquisition step: acquiring historical AIS data of the ship within a certain time period, sequentially carrying out sparsification processing and smoothing processing on the historical AIS data, and obtaining a ship track point set according to the longitude and latitude of the ship in the historical AIS data after the sparsification processing and smoothing processing;
a history track forming step: taking the starting time corresponding to the ship track point which runs first in the track point set as a current time variable, calculating the distance between each track point and the last point in the track point set according to the ship longitude and latitude in the history AIS data after the thinning processing and smoothing processing, finding out two track points which are positioned in the track set and are adjacent to the ship position at a certain time in the time period, calculating the sailing distance of the ship at the moment according to the current sailing speed of the two track points and the starting time and the ending time of the time period, calculating the position of the ship at the moment in the time period according to the sailing distance of the ship at the moment and the calculated distance between each point and the last point in all track points, and further obtaining the ship position at any moment in the time period by changing the time value of the current time variable, and forming a ship history track according to the ship position at any moment;
track prediction: training a ship track point set by adopting a path planning algorithm to obtain a track prediction model, predicting predicted track data of the ship in a certain future time period according to the track prediction model, performing sparsification processing and smoothing processing on the predicted track data in the certain future time period according to a data acquisition step and a history track forming step to obtain the ship track point set, calculating the distance between each track point and the last point in the track point set, further calculating the position of the ship in any moment in the certain future time period, and forming a ship predicted track according to the position of the ship in any moment in the certain future time period;
risk judging and displaying: based on the distance between each track point and the last point and the ship length and the ship width in the history AIS data, whether the ship in the ship history track and the ship prediction track has collision risk or not is automatically judged by adopting a ship collision recognition algorithm, if the ship has collision risk, the collision risk condition is displayed, and the monitoring of the ship history track and the ship prediction track is realized.
2. The ship track monitoring method according to claim 1, wherein in the data acquisition step, the thinning process includes:
taking out an AIS data point every interval of time, acquiring the sailing speed of the ship corresponding to the AIS data point, judging whether the sailing speed is normal, if so, expanding the time interval range, judging whether the AIS data point is located at a key position or in a special sailing state, if so, marking and reserving, judging whether the ship sails in the ocean, and if so, continuing expanding the time interval range; and judging whether the ship is in the special area, if so, reducing the time interval range.
3. The ship track monitoring method according to claim 1, wherein in the data acquisition step, the smoothing process includes: and carrying out track complement on adjacent track points with overlarge intervals after the sparsification treatment, and calculating the number of the complemented track points by adopting a great circle route algorithm.
4. The method of claim 2, wherein the critical locations include strait, canal, and land boundary locations and the special sailing conditions include stranding, anchoring, berthing, and anomalies.
5. The ship track monitoring method according to claim 1, wherein in the data acquisition step, the data after the thinning process is further cached by using a redis memory database, so as to increase the query speed of the data.
6. The ship track monitoring system is characterized by comprising a data acquisition module, a history track forming module, a track prediction module and a risk judging and displaying module, wherein the data acquisition module is respectively connected with the history track forming module and the track prediction module, the history track forming module and the track prediction module are both connected with the risk judging and displaying module,
the data acquisition module acquires the historical AIS data of the ship in a certain time period, performs sparsification and smoothing on the historical AIS data, and obtains a ship track point set according to the longitude and latitude information of the ship in the historical AIS data after the sparsification and smoothing;
the historical track forming module takes the starting time corresponding to the track point of the ship which runs first in the track point set as a current time variable, calculates the distance between each track point and the last point in the track point set according to the longitude and latitude of the ship in the historical AIS data after the thinning processing and smoothing processing, finds out two track points which are positioned in the track set and are adjacent to the position of the ship in the time period at a certain moment, calculates the sailing distance of the ship in the moment according to the current sailing speed of the two track points and the starting time and the ending time of the time period, calculates the position of the ship in the time period according to the sailing distance of the ship in the moment and the calculated distance between each point and the last point in all track points, and further obtains the position of the ship in any moment in the time period by changing the time value of the current time variable in the time period, and forms the historical track of the ship according to the position of the ship in any moment;
the track prediction module is used for training the ship track point set by adopting a path planning algorithm to obtain a track prediction model, predicting predicted track data of the ship in a certain future time period according to the track prediction model, performing sparsification processing and smoothing processing on the predicted track data in the certain future time period according to the operation of the data acquisition module and the history track forming module to obtain the ship track point set, calculating the distance between each track point and the last point in the track point set, further calculating the position of the ship in any moment in the certain future time period, and forming a ship predicted track according to the position of the ship in any moment in the certain future time period;
and the risk judging and displaying module is used for automatically judging whether the ship in the ship historical track and the ship predicted track has collision risk or not by adopting a ship collision recognition algorithm based on the distance between each track point and the last point and the ship length and the ship width in the historical AIS data, and displaying the collision risk condition if the ship has collision risk, so that the monitoring of the ship historical track and the ship predicted track is realized.
7. The ship track monitoring system of claim 6, wherein the sparsification process in the data acquisition module comprises:
taking out an AIS data point every interval of time, acquiring the sailing speed of the ship corresponding to the AIS data point, judging whether the sailing speed is normal, if so, expanding the time interval range, judging whether the AIS data point is located at a key position or in a special sailing state, if so, marking and reserving, judging whether the ship sails in the ocean, and if so, continuing expanding the time interval range; and judging whether the ship is in the special area, if so, reducing the time interval range.
8. The ship track monitoring system of claim 6, wherein the smoothing process in the data acquisition module comprises: and carrying out track complement on adjacent track points with overlarge intervals after the sparsification treatment, and calculating the number of the complemented track points by adopting a great circle route algorithm.
9. The ship track monitoring system of claim 7 wherein the critical locations include strait, canal, and land boundary locations and the special sailing conditions include stranding, anchoring, berthing, and anomalies.
10. The ship track monitoring system of claim 6, wherein the data acquisition module further uses a redis memory database to perform cache processing on the sparsely processed data to increase the data query speed.
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