CN113658452B - Course distance measuring and calculating method and system - Google Patents
Course distance measuring and calculating method and system Download PDFInfo
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Abstract
The invention provides a method and a system for measuring and calculating route distance, wherein the method comprises the following steps: obtaining an AIS dataset; establishing an interactive perception neural network, inputting the AIS data set and the time set into the interactive perception neural network to obtain the acceleration
Based on the acceleration
And the difference value between the time t and the time t + i, and calculating a first distance value S between the time t and the time t + i 1 (ii) a Extracting instantaneous speed parameters from the AIS data set, and establishing a speed set { V } based on a plurality of speed parameters 0 ,V 1 ,…,V i Calculating the average speed from time t to time t + i based on the speed set
Based on average speed
And the difference value between the time t and the time t + i, and calculating a second distance value S between the time t and the time t + i 2 (ii) a Based on the first distance value S 1 And a second distance value S 2 Calculating the range value S from time t to time t + i t And the sum of the plurality of range values forms the course distance.
Description
Technical Field
The invention relates to the technical field of route calculation, in particular to a route distance measuring and calculating method and system.
Background
In the process of ship navigation, due to the existence of marine environment interference or avoidance operation requirements, the actual course distance of a ship can have deviation through a theoretical course distance calculated by a speed-time equation, so that the course distance is not calculated accurately.
Disclosure of Invention
The invention aims to provide a route distance measuring and calculating method to solve the problems in the background technology.
The invention is realized by the following technical scheme: the invention provides a route distance measuring and calculating method in a first aspect, which comprises the following steps:
acquiring AIS (automatic identification system) original data, and preprocessing the AIS original data to obtain an AIS data set;
extracting time parameters from the AIS data set, establishing a time set { t, t +15, t +30, \8230;, t + i } based on a plurality of time parameters, wherein t is departure time, establishing an interactive perception neural network, inputting the AIS data set and the time set into the interactive perception neural network, and obtaining acceleration between time t and time t + i
Based on the acceleration
And the difference value between the time t and the time t + i, and calculating a first distance value S between the time t and the time t + i 1 ；
Extracting instantaneous speed parameters from the AIS data set, and establishing a speed set { V } based on a plurality of speed parameters 0 ,V 1 ,…，V i Calculating the average speed from time t to time t + i based on the speed set
Based on average speed
And the difference value between the time t and the time t + i, and calculating a second distance value S between the time t and the time t + i 2 ；
Based on the first distance value S 1 And a second distance value S 2 Calculating the range value S from time t to time t + i t And the sum of the range values forms the course distance.
Optionally, the AIS raw data is obtained, where the AIS raw data includes a destination, a departure place, a longitude, a latitude, a sampling time, and a navigational speed, all data including the same destination, the same departure place, and the same ship number in the AIS raw data are extracted to form route data, and the route data is interpolated and corrected by using a linear interpolation method to obtain an AIS data set.
Optionally, the interactionThe perception neural network comprises a convolutional layer, a fully-connected layer, an encoder LSTM and a decoder LSTM, wherein the convolutional layer is used as a social tensor extractor, the fully-connected layer is used as a mixer of social characteristics, the encoder LSTM is used for realizing combination of depth characteristics, and the decoder LSTM is used for outputting acceleration of a ship at delta t
Optionally, a first distance value S from time t to time t + i is calculated 1 ：
In the formula, V t For the velocity at time t, Δ t is the difference between time t and time t + i.
Optionally, the speed V 0 Corresponding to time t, said speed V i The corresponding time is t + i, so the average velocity from time t to time t + i is calculated by
Optionally, a plurality of range values S t And accumulating to obtain the final range value of the air route.
Optionally, the method further includes obtaining final range values of any one of the same ships in different time periods, and taking an average value of the final range values as the course distance.
A second aspect of the present invention provides a route distance estimation system that performs the route distance estimation method according to the first aspect of the present invention, the system including:
the data extraction module is used for accessing the automatic ship identification system, acquiring an AIS (automatic identification system) original data set, and extracting route data from the AIS original data set to acquire the AIS data set;
a neural network module for constructing a neural network, the neural network including a convolutional layer, a full connection layer, an encoder LSTM, a decoder LSTM, the neural network moduleThe convolution layer is used as a social tensor extractor, the full connection layer is used as a social feature mixer, the encoder LSTM is used for realizing the combination of depth features, and the decoder LSTM is used for outputting the acceleration of the ship at delta t
The first data collection module is used for extracting time parameters from the AIS data set and constructing a time set;
the second data collection module is used for extracting speed parameters from the AIS data set and constructing a speed set;
a first distance calculation module for calculating a first distance based on the acceleration
Calculating a first distance value;
the second distance calculation module is used for calculating a second distance value according to the speed parameter and the time parameter;
and the voyage calculation module is used for calculating the voyage according to the first distance value and the second distance value.
Compared with the prior art, the invention has the following beneficial effects:
according to the method and the system for measuring and calculating the route distance, provided by the invention, the actual navigation distance of each route can be calculated by analyzing a large amount of AIS and ship-age information of a ship on the same route and calculating the distance sum of all AIS nodes in a course by taking time t as a node, and the data deviation can be continuously corrected by continuous large-scale calculation, so that the actual navigation distance of each route is measured and calculated, and a good data basis is provided for ship-age planning and dynamic reminding of the ship.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only preferred embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive exercise.
FIG. 1 is a flow chart of a method for measuring and calculating route distance according to the present invention;
FIG. 2 is a diagram of a flight path distance estimation system according to the present invention.
Detailed Description
For a better understanding of the technical content of the present invention, the following detailed description is provided in conjunction with the accompanying drawings for further explanation of the present invention.
Referring to fig. 1, a first aspect of the present invention provides a route distance measuring method, including the steps of:
s1, AIS original data are obtained, and the AIS original data are preprocessed to obtain an AIS data set;
s2, extracting time parameters from the AIS data set, establishing a time set { t, t +15, t +30, \8230, t + i } based on a plurality of time parameters, wherein t is the departure time, establishing an interactive perception neural network, inputting the AIS data set and the time set into the interactive perception neural network, and obtaining the acceleration between time t and time t + i
S3, based on the acceleration
And the difference value between the time t and the time t + i, and calculating a first distance value S between the time t and the time t + i 1 ；
S4, extracting instantaneous speed parameters from the AIS data set, and establishing a speed set { V } based on a plurality of speed parameters 0 ,V 1 ,…,V i Calculating the average speed from time t to time t + i based on the speed set
Based on average speed
And the difference between time t to time t + i,calculating a second distance value S from time t to time t + i 2 ；
S5, based on the first distance value S 1 And a second distance value S 2 Calculating the range value S from time t to time t + i t And the sum of the plurality of range values forms the course distance.
In the embodiment of the application, the AIS raw data is acquired, for example, the AIS information acquired from the AIS system includes the Chinese ship name, the ship type, the position, the navigation speed, the destination, the departure place, the longitude, the latitude and the sampling time of the ship. The obvious error records in the AIS data are generally in the following categories: (1) The marine mobile service identification code (MMSI) length of a ship is not a 9-digit or unreasonable record; (2) The longitude and latitude of the ship exceed a reasonable range (if the longitude and latitude are negative values); (3) the speed and the course of the ship exceed a reasonable range; and (4) the acquisition time of the ship information exceeds a reasonable range. Meanwhile, AIS data is lost due to equipment aging, transmission system faults and the like, the lost data needs to be processed, and a linear interpolation method is adopted to interpolate and correct AIS original data;
and then taking the destination and the departure place as keywords, extracting the course information corresponding to the destination and the departure place, wherein the course information comprises a plurality of sampling information points, and each sampling information point comprises information such as instantaneous speed, acceleration, longitude and latitude of the ship at time t, so as to obtain an AIS data set.
In step S2, the mutual perception neural network includes a convolutional layer as a social tensor extractor, a fully-connected layer as a mixer of social features, an encoder LSTM for implementing depth feature merging, and a decoder LSTM for outputting an acceleration of the ship at Δ t
Cross reference theretoMutual sensing acceleration
The expression of (a) is:
wherein, the first and the second end of the pipe are connected with each other,
for the purpose of the recorded acceleration of the vessel,
for the length of the vessel to be recorded,
for the recorded accuracy of the vessel to be recorded,
for the recorded latitude of the vessel,
for the recorded yaw angle it is,
is a repulsive interaction force with the environment, and
can be expressed by the following formula:
in addition, for the overfitting problem that may occur in the neural network model, dropout method is adopted to process the overfitting problem, and the threshold value is set to 0.5.
In step S3, a first distance value S from time t to time t + i is calculated 1 ：
In the formula, V t At time t, Δ t is the difference between time t and time t + i.
In step S4, the speed V 0 Corresponding to time t, said speed V i The corresponding time is t + i, so the average velocity from time t to time t + i is calculated by
Further, by average speed
And calculating a second distance value by the time difference value delta t
In the previous step, the flight path is segmented, namely the flight path is divided into N small segments by taking the sampling time as an interval, and the distance of each small segment is determined by a first distance value S 1 A second distance value S 2 The calculation is carried out in the following way:
n range values S t And accumulating to obtain the final range value of the air route.
Optionally, the method further includes obtaining final range values of any one of the same ships in different time periods, and taking an average value of the final range values as the course distance.
As an example, the final voyage value of the ship 1 from the port a to the port B in morning No. 6 is calculated through the above steps while the final voyage value of the ship 1 from the port a to the port B in morning No. 8 is continuously calculated, and the average value of the plurality of final voyage values is taken as the course distance.
Referring to fig. 2, a second aspect of the present invention provides a flight path distance estimation system that performs the flight path distance estimation method according to the first aspect of the present invention, the system including:
the data extraction module is used for accessing the automatic ship identification system, acquiring an AIS (automatic identification system) original data set, and extracting route data from the AIS original data set to acquire the AIS data set;
the neural network module is used for constructing a neural network, the neural network comprises a convolutional layer, a full connection layer, an encoder LSTM and a decoder LSTM, the convolutional layer is used as a social tensor extractor, the full connection layer is used as a mixer of social characteristics, the encoder LSTM is used for realizing combination of depth characteristics, and the decoder LSTM is used for outputting acceleration of the ship at delta t
The first data collection module is used for extracting time parameters from the AIS data set and constructing a time set;
the second data collection module is used for extracting speed parameters from the AIS data set and constructing a speed set;
a first distance calculation module for calculating a first distance based on the acceleration
Calculating a first distance value;
the second distance calculation module is used for calculating a second distance value according to the speed parameter and the time parameter;
and the voyage calculation module is used for calculating the voyage according to the first distance value and the second distance value.
In summary, according to the method and the system for measuring and calculating the course distance disclosed by the application, the actual sailing distance of each course can be calculated by analyzing a large amount of AIS and ship-term information of a ship on the same course and calculating the distance sum of all AIS nodes in a course by taking time t as a node, and the actual sailing distance of each course can be measured and calculated by continuously calculating a large amount of data deviation, so that a good data basis is provided for planning the ship term and dynamically reminding the ship.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (5)
1. A method for measuring and calculating route distance is characterized by comprising the following steps:
acquiring AIS (automatic identification system) original data, and preprocessing the AIS original data to obtain an AIS data set;
extracting time parameters from the AIS data set, establishing a time set { t, t +15, t +30,.. T, t + i } based on a plurality of time parameters, wherein t is the departure time, establishing an interactive perception neural network, inputting the AIS data set and the time set into the interactive perception neural network, and obtaining the acceleration between the time t and the time t + i
Based on the acceleration
And the difference value between the time t and the time t + i, and calculating a first distance value S between the time t and the time t + i 1 ；
Extracting instantaneous speed parameters from the AIS data set, and establishing a speed set { V } based on a plurality of speed parameters 0 ，V 1 ，...，V i Calculating the average speed from time t to time t + i based on the speed set
Based on average speed
And the difference value between the time t and the time t + i, and calculating a second distance value between the time t and the time t + iS 2 ；
Based on the first distance value S 1 And a second distance value S 2 Calculating the range value S from time t to time t + i t The sum of the plurality of voyage values forms a course distance;
the interactive perception neural network comprises a convolutional layer, a full connection layer, an encoder LSTM and a decoder LSTM, wherein the convolutional layer is used as a social tensor extractor, the full connection layer is used as a mixer of social characteristics, the encoder LSTM is used for realizing combination of depth characteristics, and the decoder LSTM is used for outputting acceleration of a ship at delta t
Calculating a first distance value S from time t to time t + i 1 ：
In the formula, V t The speed at the moment t, Δ t is the difference between the time t and the time t + i;
said velocity V 0 Corresponding to time t, said speed V i The corresponding time is t + i, so the average velocity from time t to time t + i is calculated by
2. The method as claimed in claim 1, wherein AIS raw data including destination, origin, longitude, latitude, sampling time, and speed are obtained, all data including the same destination, the same origin, and the same ship number in the AIS raw data are extracted to form the route data, and the route data are interpolated and corrected by linear interpolation to obtain the AIS data set.
3. A method for route distance estimation according to claim 1, characterised in that a plurality of range values S are determined t And accumulating to obtain the final range value of the air route.
4. The method as claimed in claim 1, further comprising obtaining final range values of any one of the same vessels at different time periods, and taking an average of the final range values as the course distance.
5. An en-route distance estimation system characterized by performing the en-route distance estimation method according to any one of claims 1 to 4, the system comprising:
the data extraction module is used for accessing the automatic ship identification system, acquiring an AIS (automatic identification system) original data set, and extracting route data from the AIS original data set to acquire the AIS data set;
the neural network module is used for constructing a neural network, the neural network comprises a convolutional layer, a full connection layer, an encoder LSTM and a decoder LSTM, the convolutional layer is used as a social tensor extractor, the full connection layer is used as a mixer of social characteristics, the encoder LSTM is used for realizing the combination of depth characteristics, and the decoder LSTM is used for outputting the acceleration of the ship at delta t
The first data collection module is used for extracting time parameters from the AIS data set and constructing a time set;
the second data collection module is used for extracting speed parameters from the AIS data set and constructing a speed set;
a first distance calculation module for calculating a first distance based on the acceleration
Calculating a first distance value;
the second distance calculation module is used for calculating a second distance value according to the speed parameter and the time parameter;
and the voyage calculation module is used for calculating the voyage according to the first distance value and the second distance value.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2019108014A (en) * | 2017-12-19 | 2019-07-04 | ダイムラー・アクチェンゲゼルシャフトＤａｉｍｌｅｒ ＡＧ | Cruising distance estimation device |
| CN111882925A (en) * | 2020-07-27 | 2020-11-03 | 交通运输部水运科学研究所 | Shipping traffic flow prediction system based on information propagation diagram and recurrent neural network |
| CN111928855A (en) * | 2020-08-21 | 2020-11-13 | 上海船舶运输科学研究所 | Automatic shortest route planning method based on AIS data |
| CN112561148A (en) * | 2020-12-09 | 2021-03-26 | 北京京航计算通讯研究所 | Ship track prediction method and system based on one-dimensional convolutional neural network and LSTM |
| CN112562415A (en) * | 2020-11-26 | 2021-03-26 | 中远海运科技股份有限公司 | Ship route recommendation method |
| CN112862156A (en) * | 2021-01-08 | 2021-05-28 | 北京工业大学 | Ship path planning method based on ship track and ant colony algorithm |
| CN112906830A (en) * | 2021-04-14 | 2021-06-04 | 武汉理工大学 | Automatic generation method of optimal ship route based on AIS big data |
-
2021
- 2021-07-31 CN CN202110877106.XA patent/CN113658452B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2019108014A (en) * | 2017-12-19 | 2019-07-04 | ダイムラー・アクチェンゲゼルシャフトＤａｉｍｌｅｒ ＡＧ | Cruising distance estimation device |
| CN111882925A (en) * | 2020-07-27 | 2020-11-03 | 交通运输部水运科学研究所 | Shipping traffic flow prediction system based on information propagation diagram and recurrent neural network |
| CN111928855A (en) * | 2020-08-21 | 2020-11-13 | 上海船舶运输科学研究所 | Automatic shortest route planning method based on AIS data |
| CN112562415A (en) * | 2020-11-26 | 2021-03-26 | 中远海运科技股份有限公司 | Ship route recommendation method |
| CN112561148A (en) * | 2020-12-09 | 2021-03-26 | 北京京航计算通讯研究所 | Ship track prediction method and system based on one-dimensional convolutional neural network and LSTM |
| CN112862156A (en) * | 2021-01-08 | 2021-05-28 | 北京工业大学 | Ship path planning method based on ship track and ant colony algorithm |
| CN112906830A (en) * | 2021-04-14 | 2021-06-04 | 武汉理工大学 | Automatic generation method of optimal ship route based on AIS big data |
Non-Patent Citations (1)
| Title |
|---|
| Interaction-aware_Kalman_Neural_Networks_for_Trajectory_Prediction;Ju C, et al.;《2020 IEEE Intelligent Vehicles Symposium (IV)》;20201231;第1793-1800页 * |
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