CN112214721A - AIS data-based dynamic ship emission list establishing method - Google Patents

AIS data-based dynamic ship emission list establishing method Download PDF

Info

Publication number
CN112214721A
CN112214721A CN202011029288.7A CN202011029288A CN112214721A CN 112214721 A CN112214721 A CN 112214721A CN 202011029288 A CN202011029288 A CN 202011029288A CN 112214721 A CN112214721 A CN 112214721A
Authority
CN
China
Prior art keywords
ship
emission
ais
dynamic
track
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202011029288.7A
Other languages
Chinese (zh)
Other versions
CN112214721B (en
Inventor
郑君瑜
张志炜
黄志炯
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Jinan University
Original Assignee
Jinan University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Jinan University filed Critical Jinan University
Priority to CN202011029288.7A priority Critical patent/CN112214721B/en
Publication of CN112214721A publication Critical patent/CN112214721A/en
Application granted granted Critical
Publication of CN112214721B publication Critical patent/CN112214721B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services

Abstract

The invention discloses a method for establishing a dynamic ship emission list based on AIS data, which comprises the following steps: acquiring AIS data and integrating a ship information database; analyzing the AIS data time step, dynamically reading the AIS data, and cleaning the abnormal AIS data; identifying and supplementing the cleaned AIS data, dynamically identifying emission factors, and performing emission estimation by combining dynamic method emission calculation to obtain a dynamic ship emission representation; according to the dynamic ship emission characterization, AIS track analysis is carried out, and dynamic ship emission characterization correction is carried out according to different loss conditions to obtain a ship navigation track for repairing and supplementing; the method realizes quasi-real-time ship emission dynamic representation, improves the precision and the accuracy of ship emission representation, is favorable for providing technical support for fine management, and improves the accuracy of air quality forecast and early warning.

Description

AIS data-based dynamic ship emission list establishing method
Technical Field
The invention relates to the research field of environmental protection and air pollution prevention, in particular to a method for establishing a dynamic ship emission list based on AIS data.
Background
In recent years, with the development of shipping industry, ship emission increasingly becomes an important emission contribution source, the ship emission aggravates air pollution of ports and coastal areas, harms healthy life of coastal and inland residents and sustainable development of ecological environment, is one of key points and difficulties in prevention and control of air pollution of coastal cities in China, and needs to implement more fine management on the ship emission. The ship emission has the basic characteristics of dynamic emission, multi-source emission, strong liquidity and the like, and is wide in distribution and large in spatial difference. The ship emission list is the basic research of ship emission control. However, the accuracy and precision of the ship emission list are limited due to the ship emission list establishment technology and the AIS data quality problem. At present, the ship emission list is established, historical static data is mostly used, and partial research using AIS data fails to exert the advantage of dynamic reporting of AIS data, so that the ship emission cannot be represented in a quasi-real-time manner; calculation of ship emission based on AIS data and a power method is a mainstream research technology at present, but because AIS data communication is obstructed and other reasons cause ship track point errors and deletions, ship emission and time-air characteristic characterization are influenced, and research on ship emission characterization by combining AIS data correction is less at present.
At present, a lot of research is put into AIS data representation ship emission by a plurality of research institutions, for example, Chinese patent application No. 201911274146.4 relates to a method for calculating ship atmospheric pollutant emission and spatial distribution based on AIS data, Chinese patent application No. 201710577047.8 relates to a high-precision ship pollutant emission calculation method based on AIS data, and the methods are methods for calculating ship emission based on historical static AIS data, cannot exert the advantages of dynamic reporting of AIS data, realize quasi-real-time dynamic representation of ship emission, and do not modify AIS data to influence the space-time representation accuracy of ship emission.
Disclosure of Invention
The invention aims to overcome the defects and shortcomings of the prior art, provides a dynamic ship emission list establishing method based on AIS data, establishes a dynamic ship emission establishing technology combined with AIS data correction, accurately represents characteristics of ship dynamic emission, large space-time difference and the like, realizes quasi-real-time ship emission dynamic representation, improves the ship emission representation precision and accuracy, is favorable for providing technical support for fine management and improves the accuracy of air quality forecast early warning.
The purpose of the invention is realized by the following technical scheme:
a dynamic ship emission list establishing method based on AIS data is characterized by comprising the following steps:
acquiring AIS data and integrating a ship information database;
analyzing the AIS data time step, dynamically reading the AIS data, and cleaning the abnormal AIS data;
identifying and supplementing the cleaned AIS data according to ship information and a ship information database, dynamically identifying ship emission factors according to the ship information, and performing emission estimation by combining dynamic method emission calculation to obtain a dynamic ship emission representation;
and performing AIS track analysis according to the dynamic ship emission characteristics, performing dynamic ship emission characteristic correction according to different loss conditions to obtain a ship navigation track for repairing and supplementing, and calculating and correcting the ship emission characteristics.
Further, the integrated ship information database further includes supplement of ship information data, specifically: and based on the classification of the ship type and the ship length, filling missing information data by using the median of the complete information data identified in the ship information database, and supplementing the missing information data to the ship information database.
Further, the ship information comprises ship IMO number, ship MMSI number, ship name, ship engine type and fuel type.
Further, to AIS data time step analysis, dynamic reading AIS data, wash the processing to unusual AIS data, specifically do: analyzing the AIS data reporting time intervals in different navigation states according to the AIS data dynamic reporting time intervals, and taking the AIS data reporting proper time intervals in different navigation states as the time step length of dynamic processing; and dynamically reading AIS data, and cleaning error or repeated AIS data, wherein the error or repeated AIS data comprises course angle abnormity, speed abnormity, point position distribution abnormity and repeated data.
Further, the suitable time interval is the total number of AIS data reporting time intervals in different voyage states or the average AIS data reporting time interval in different voyage states.
Further, the performing of the emission estimation is specifically as follows: the method comprises the following steps of estimating the emission of a single ship and estimating regional emission, wherein the calculation model of the emission estimation of the single ship is as follows:
Figure BDA0002703077990000021
EF＝EF0×FCF，
LF＝(Vi/Vmax)3，
wherein, m is 1,2,3,4 is four navigation conditions of berthing, maneuvering, slow speed, normal navigation; p is 1,2 and 3 are three power devices of a main engine, an auxiliary engine and a boiler; em is pollutant discharge amount, MCR is engine rated power, LF is load factor, ViFor the ith AIS dynamic information ship's voyage speed, VmaxFor maximum speed of travel, T, of the shipiReporting time, T, for the ith AIS dynamic informationi-1The reporting time of the i-1 th AIS dynamic information, EF is an emission factor, and EF is0For the base emission factor, FCF is the combustion correction factor, LLA is the low load correction factor;
after the ship emission estimation of each track point of a single ship is finished, selecting an area through superposition map information, counting all AIS track point ship emissions in the area, and obtaining an area ship emission result.
Further, the AIS trajectory analysis is performed according to the emission estimation condition, and trajectory correction is performed according to different deficiency conditions, specifically: different track missing conditions are obtained through AIS track analysis, and track correction is carried out according to the different track missing conditions;
for missing tracks with AIS reporting time intervals larger than average reporting time intervals and completely opposite longitude and latitude directions of front and rear tracks, ship tracks are analyzed and processed based on similar tracks, the sailing track after steering is used as a new track, the original sailing state is kept for 10 minutes, and the rest of the time is processed as a berthing working condition;
for the AIS reporting time interval which is larger than the average reporting time interval but the longitude and latitude directions of the front track and the rear track are not completely opposite, the track is repaired by adopting a segmented cubic spline track repairing technology based on time and longitude and latitude; and (4) performing time-longitude, time-latitude and time-speed segmented cubic spline interpolation on the time periods respectively, solving an interpolation result, and obtaining longitude and latitude coordinates of the missing track points so as to obtain the ship navigation track repaired and supplemented.
Further, the interpolation result is obtained as follows:
establishing a segmented cubic spline interpolation model, and setting the time t of the point iiLongitude xiLatitude yiPiecewise cubic spline function S (t) at node tiThe second derivative of (A) is MiThen, there is a piecewise cubic spline expression as:
Figure BDA0002703077990000031
wherein M isiFor unknown parameters, hiIs a time interval, hi＝ti+1-ti；
Deriving S (t) to obtain S '(t), and using S' (t)i+0)＝S′(ti-0) available:
uiMi-1+2Mi+λiMi+1＝di，
wherein the content of the first and second substances,
Figure BDA0002703077990000032
Figure BDA0002703077990000033
Figure BDA0002703077990000034
supplementing i-1 and i-n as end point equations according to the first type of boundary conditions, the three-bending moment matrix equation is as follows:
Figure BDA0002703077990000041
calculating M from the above equation0,M1,…MnAnd further obtaining interpolation results of time-longitude and time-latitude, and obtaining longitude and latitude coordinates of the missing track points, thereby obtaining the ship navigation track for repairing and supplementing.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the invention provides a dynamic ship emission list establishing technology based on AIS data, which integrates database integration supplement, AIS data dynamic processing, dynamic method emission characterization and retrospective correction, realizes dynamic ship emission characterization, performs retrospective correction on emission by combining AIS trajectory analysis, and improves the ship emission control level and the precision;
2. the dynamic AIS processing and emission characterization are realized, AIS data emission characterization of twenty thousand rows in 10 minutes can be completed within 2 minutes, the timeliness is high, and the method can be applied to real-time dynamic ship emission characterization;
3. according to the invention, the ship is processed according to a moving point source to represent the ship discharge, and the spatial resolution is improved by 1500 times from national scale to hong Kong scale, so that the technology has high adaptability to different research resolution scales;
4. the invention can effectively represent the space-time difference of ship emission, and quantifies the effect of holidays by about 10-20% and the effect of special meteorological conditions by about 10-50%.
Drawings
FIG. 1 is a flow chart of a method for establishing a dynamic ship emission list based on AIS data according to the present invention;
FIG. 2 is a distribution diagram of the discharge space of the upscale vessel in accordance with an embodiment of the present invention;
FIG. 3 is a schematic illustration of a dynamic emissions characterization aging in accordance with an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating an analysis of different AIS trace missing situations in the embodiment of the present invention;
FIG. 5 is a diagram of retrospective correction for nationwide dynamic ships NO according to an embodiment of the inventionXA schematic diagram of the distribution of the discharge space; FIG. 5a shows the original AIS track ship NOXA schematic diagram of the distribution of the discharge space; FIG. 5b shows the corrected ship NOXA schematic diagram of the distribution of the discharge space; fig. 5c is a schematic view of the distribution of the discharge space of the ships throughout the country according to the conventional distribution method.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited thereto.
Example (b):
a method for establishing a dynamic ship emission list based on AIS data is disclosed, and as shown in figure 1, a dynamic ship emission list establishing technology based on AIS data is established through three main parts, namely database preprocessing, dynamic ship emission characterization, dynamic ship emission correction and the like. And when the AIS data is combined for ship emission characterization, the complete ship static information data is required to provide a data base. And after the database is improved for preprocessing, performing dynamic ship emission characterization according to AIS data, and performing dynamic ship emission correction after accumulation for a certain time. The method specifically comprises the following steps:
the first step is as follows: integrating and perfecting a ship static information database by adopting a ship information database integration and supplement technology; the method specifically comprises the following steps: based on the 2015 ship registration information database and the 2017 hong Kong official ship information database, a ship static information database is integrated. The ship information database provided by the hong Kong environmental protection agency covers more than 8 thousand ocean ships and inland river ships entering and exiting the hong Kong in 2017; in the 2015 ship registration information database, there are about 19 million ships, of which 70% of ship information is completely available. The ship static information database coverage information includes International Maritime Organization (IMO) number, call sign, ship name, ship type, ship length, Dead Weight Ton (DWT), total Ton, host power, auxiliary engine power, boiler information, host type, auxiliary engine type, fuel type, year of delivery, Mobile Service Identity (MMSI) number, and the like. For ship information missing or not contained in the database, the ship is classified according to types and classified according to the ship length, and according to the grade of the ship length with missing data, the number of bits in each item of static data of the ship with the same grade and complete static information and AIS data is used as filling of the missing static information data.
The second step is that: AIS data acquisition and dynamic processing technology is adopted to analyze the AIS data in 2017 in time step and realize dynamic processing; the method specifically comprises the following steps: in 2017, the AIS data contains about 6 hundred million lines of ship activity information in the sea area of 200 nautical miles in China, and the number of ships is more than 41 million. Each line of AIS ship information comprises static information such as IMO numbers, call signs, ship names, ship types, MMSI numbers, ship lengths and widths and dynamic information such as ship speed, longitude and latitude, navigation time and the like. It is assumed that both static and dynamic AIS information is automatically reported unless the AIS equipment is shut down. The reporting average time interval and the mode of AIS information of each ship are both 10 minutes, 49% of AIS information is reported within 10 minutes, and the dynamic ship emission representation is dynamically processed in a step-by-step mode by taking 10 minutes as time. And dynamically reading 10-minute AIS data for processing and emission calculation by taking the ship AIS information reporting average time interval as the time step length of the dynamic ship emission representation so as to simulate the real-time dynamic reporting condition of the AIS data.
In the AIS data, due to sensor characteristics, signal interference, congestion of a transmission channel and the like, abnormal conditions such as abnormal ship speed, abnormal course angle, abnormal track point position, repeated data reporting and the like can occur. And judging that the ship speed is abnormal according to the comparison between the real-time ship speed and the maximum ship speed, and if the real-time ship speed of the ship is greater than the maximum ship speed, eliminating track points for the abnormal ship speed. The range of the ship course angle is 0-360 degrees, and track points are removed when the range of the ship course angle exceeds the normal course angle range and the course angle is considered to be abnormal. According to the Chinese land map and the inland river map, a research range of the ship track points is drawn, and if the positions of the ship track points fall within the Chinese land range, the positions of the track points are considered to be abnormal and should be removed. And according to the information such as the IMO number of the ship, the name of the ship, the reporting time and the like, the AIS information which is repeatedly reported is eliminated.
The third step: a dynamic AIS data coupling-based dynamic method is adopted to represent a ship emission technology, relevant parameters such as ship information, emission factors and the like are identified and supplemented, and ship emission is represented by combining dynamic method emission calculation. Meanwhile, as the ship emission source is treated as a moving point source, the technology disclosed by the invention has higher precision in representing the ship emission, can be used for representing the ship emission with various regional spatial resolutions, and has higher adaptability to different research resolution scales. The distribution diagram of the discharge space of the ship in the liter scale is shown in fig. 2, and the spatial resolution from the national region to the hong kong region is improved by 1500 times.
The dynamic ship emission characterization and display is carried out, the time step is 10 minutes, the AIS ship data in each time step is about 2 ten thousand lines, the time consumption of the AIS ship emission program in each time step is represented and displayed through statistical analysis, and the dynamic emission characterization aging schematic diagram is shown in FIG. 3. The dynamic AIS reading and data processing time consumption is about 80 seconds, the dynamic AIS data coupling dynamic method based representation of ship emission technology consumes about 15 seconds, emission display lasts about 15 seconds, the total time consumption can complete dynamic ship emission representation within a time step within 2 minutes, namely AIS ship emission and representation within the last time step can be completed within the next time step, and the dynamic AIS data coupling dynamic method based representation of ship emission and data processing shows that the dynamic AIS data coupling dynamic ship emission and data processing method based representation of ship emission and data processing is high in timeliness on dynamic ship emission representation and display and can be used for real-time emission control of ship emission.
The fourth step: after the dynamic emission representation is accumulated for a period of time, a dynamic ship emission retrospective correction technology based on AIS track analysis is adopted, the AIS track of 2017 is analyzed, and ship track analysis processing based on similar tracks and a segmented cubic spline track restoration technology based on time and longitude and latitude are respectively adopted for retrospective restoration of different track missing conditions.
Since the AIS data needs to be dynamically and continuously read and processed in the process of dynamically characterizing the ship emission, it is difficult to analyze the AIS ship trajectory accumulated for a long time. Therefore, after the ship emission is dynamically characterized, the AIS data of one day are accumulated, and the dynamic ship emission list within the day is corrected aiming at the track section with the AIS reporting time interval being more than the average reporting time interval by 10 minutes.
When the AIS ship track of one day is analyzed, AIS missing data is mainly divided into two cases, as shown in fig. 4: firstly, a ship starts and stops in a relatively fixed navigation track, and the track is lost when berthing is carried out at one end but the berthing point signal is not good; and secondly, in the continuous sailing process of the ship, the track is lost due to poor signals of partial sea areas or AIS equipment problems of the ship. The ship track direction can be determined mainly by the front and back of the missing segment track point and the ship track direction. As shown in fig. 4(b), the ship sails back and forth between the two ends AB of the track 1, the ship sails at the track 1AB as shown in fig. 4(a), and as shown in fig. 4(c), the sailing conditions of the ship from the point 1 to the point 4 of the track 1A are shown, the time interval from the point 1 to the point 2 and from the point 3 to the point 4 is 10 minutes, and the time interval from the point 2 to the point 3 is 170 minutes. In the reference track 1B, after the ship sails from the point 1 to the point 2,3 track points exist between the point 2 and the point 5, berth for 30 minutes, and then start from the point 5 to the point 6. Therefore, the sailing to the point 3 is started after berthing of the ship is stopped at a point between the points 2 and 3 at the position from the point 2 to the point 3 on the track 1A, therefore, the AIS reporting time interval is larger than the average reporting time interval, the latitude and longitude directions of the front track and the rear track are completely opposite, the ship track analysis processing based on similar tracks is carried out, the sailing track after steering is used as a new track, the original sailing state is kept for 10 minutes, and the rest time is used as the berthing state processing.
For the missing track caused by the poor signal of part of sea area or the AIS equipment problem of the ship in the continuous sailing process, as shown in the track 2 and the track 3 of fig. 4(d) and 4(e), the reporting time interval of the missing track AIS is larger than the average reporting time interval, but the longitude and latitude directions of the front track and the rear track are not completely opposite, the invention adopts the segmented cubic spline track repairing technology based on time and longitude and latitude to carry out retrospective repairing. Through the analysis of all-year AIS data, 2% of track points have the missing condition that the AIS reporting time interval is larger than the average reporting time interval and the longitude and latitude directions of the front track and the rear track are completely opposite, and 3% of track points have the missing condition that the AIS reporting time interval is larger than the average reporting time interval but the longitude and latitude directions of the front track and the rear track are not completely opposite, and the ship track analysis processing based on the similar track and the segmented cubic spline track repairing technology based on the time and the longitude and latitude are respectively adopted for backtracking correction aiming at two main track missing conditions.
The AIS points and the ship discharge track can be effectively supplemented by retrospective correction of the figure 5, the original track points are distributed in a discrete point set form along the coast, the distribution in a farther sea area is sparse, the ship discharge has more discrete high-value points, and the discharge track is unclear, as shown in figure 5 (a); after retrospective correction, the original track points are distributed more continuously along the sea, are distributed widely in a distant sea area, have clear ship discharge tracks, and are distributed intensively and continuously along the south-east coast, as shown in fig. 5(b), and the ship discharge space distribution of the whole country in the traditional distribution method is shown in fig. 5 (c). For the one-day regional dynamic ship emission characterization, the retrospective correction effectively increases track points of small regions by more than 10 times, and the ship emission increases by more than 3 times. The retrospective correction effectively reduces the emission fluctuation of the ship caused by the AIS signal problem from 17% to 14%, and is beneficial to removing the emission time distribution error caused by AIS track loss.
In addition, the invention can characterize and quantify the influence of special weather on the ship emission and the space-time distribution. The influence of holidays on ship emission is about 10-20%, the influence of special weather is about 10-50%, the influence of areas such as typhoons is mainly special weather, and the influence on ship activities and emission in landing areas is higher.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.

Claims (8)

1. A dynamic ship emission list establishing method based on AIS data is characterized by comprising the following steps:
acquiring AIS data and integrating a ship information database;
analyzing the AIS data time step, dynamically reading the AIS data, and cleaning the abnormal AIS data;
identifying and supplementing the cleaned AIS data according to ship information and a ship information database, dynamically identifying ship emission factors according to the ship information, and performing emission estimation by combining dynamic method emission calculation to obtain a dynamic ship emission representation;
and performing AIS track analysis according to the dynamic ship emission characteristics, performing dynamic ship emission characteristic correction according to different loss conditions to obtain a ship navigation track for repairing and supplementing, and calculating and correcting the ship emission characteristics.
2. The method for establishing the dynamic ship emission list based on the AIS data according to claim 1, wherein the integrated ship information database further includes ship information data supplementation, specifically: and based on the classification of the ship type and the ship length, filling missing information data by using the median of the complete information data identified in the ship information database, and supplementing the missing information data to the ship information database.
3. The method according to claim 1, wherein the vessel information includes vessel IMO number, vessel MMSI number, vessel name, vessel engine type, and fuel type.
4. The method for establishing the dynamic ship emission list based on the AIS data according to claim 1, wherein the AIS data is analyzed in a time step, the AIS data is dynamically read, and abnormal AIS data is cleaned, specifically: analyzing the AIS data reporting time intervals in different navigation states according to the AIS data dynamic reporting time intervals, and taking the AIS data reporting proper time intervals in different navigation states as the time step length of dynamic processing; and dynamically reading AIS data, and cleaning error or repeated AIS data, wherein the error or repeated AIS data comprises course angle abnormity, speed abnormity, point position distribution abnormity and repeated data.
5. The AIS data-based dynamic ship emission list building method according to claim 4, wherein the suitable time interval is the total number of AIS data reporting time intervals in different voyages or the average AIS data reporting time interval in different voyages.
6. The method for establishing the dynamic ship emission list based on the AIS data according to claim 1, wherein the emission estimation is specifically performed as follows: the method comprises the following steps of estimating the emission of a single ship and estimating regional emission, wherein the calculation model of the emission estimation of the single ship is as follows:
Figure FDA0002703077980000021
EF＝EF0×FCF，
LF＝(Vi/Vmax)3，
wherein, m is 1,2,3,4 is four navigation conditions of berthing, maneuvering, slow speed, normal navigation; p is 1,2 and 3 are three power devices of a main engine, an auxiliary engine and a boiler; em is pollutant discharge amount, MCR is engine rated power, LF is load factor, ViFor the ith AIS dynamic information ship's voyage speed, VmaxFor maximum speed of travel, T, of the shipiReporting time, T, for the ith AIS dynamic informationi-1The reporting time of the i-1 th AIS dynamic information, EF is an emission factor, and EF is0Is a basic rowThe power factor, FCF is the combustion correction factor, LLA is the low load correction factor;
after the ship emission estimation of each track point of a single ship is finished, selecting an area through superposition map information, counting all AIS track point ship emissions in the area, and obtaining an area ship emission result.
7. The method for establishing the dynamic ship emission list based on the AIS data according to claim 1, wherein the AIS trajectory analysis is performed according to emission estimation conditions, and trajectory correction is performed according to different deficiency conditions, specifically: different track missing conditions are obtained through AIS track analysis, and track correction is carried out according to the different track missing conditions;
for missing tracks with AIS reporting time intervals larger than average reporting time intervals and completely opposite longitude and latitude directions of front and rear tracks, ship tracks are analyzed and processed based on similar tracks, the sailing track after steering is used as a new track, the original sailing state is kept for 10 minutes, and the rest of the time is processed as a berthing working condition;
for the AIS reporting time interval which is larger than the average reporting time interval but the longitude and latitude directions of the front track and the rear track are not completely opposite, the track is repaired by adopting a segmented cubic spline track repairing technology based on time and longitude and latitude; and (4) performing time-longitude, time-latitude and time-speed segmented cubic spline interpolation on the time periods respectively, solving an interpolation result, and obtaining longitude and latitude coordinates of the missing track points so as to obtain the ship navigation track repaired and supplemented.
8. The method according to claim 7, wherein the interpolation result is obtained by:
establishing a segmented cubic spline interpolation model, and setting the time t of the point iiLongitude xiLatitude yiPiecewise cubic spline function S (t) at node tiThe second derivative of (A) is MiThen, there is a piecewise cubic spline expression as:
Figure FDA0002703077980000022
wherein M isiFor unknown parameters, hiIs a time interval, hi＝ti+1-ti；
Deriving S (t) to obtain S '(t), and using S' (t)i+0)＝S′(ti-0) available:
uiMi-1+2Mi+λiMi+1＝di，
wherein the content of the first and second substances,
Figure FDA0002703077980000023
Figure FDA0002703077980000031
Figure FDA0002703077980000032
supplementing i-1 and i-n as end point equations according to the first type of boundary conditions, the three-bending moment matrix equation is as follows:
Figure FDA0002703077980000033
calculating M from the above equation0,M1,…MnAnd further obtaining interpolation results of time-longitude and time-latitude, and obtaining longitude and latitude coordinates of the missing track points, thereby obtaining the ship navigation track for repairing and supplementing.
CN202011029288.7A 2020-09-27 2020-09-27 AIS data-based dynamic ship emission list establishing method Active CN112214721B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011029288.7A CN112214721B (en) 2020-09-27 2020-09-27 AIS data-based dynamic ship emission list establishing method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011029288.7A CN112214721B (en) 2020-09-27 2020-09-27 AIS data-based dynamic ship emission list establishing method

Publications (2)

Publication Number Publication Date
CN112214721A true CN112214721A (en) 2021-01-12
CN112214721B CN112214721B (en) 2022-11-29

Family

ID=74052123

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011029288.7A Active CN112214721B (en) 2020-09-27 2020-09-27 AIS data-based dynamic ship emission list establishing method

Country Status (1)

Country Link
CN (1) CN112214721B (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113822630A (en) * 2021-09-14 2021-12-21 中远海运科技股份有限公司 AIS-based LNG ship transport tracking method and system
CN113987058A (en) * 2021-11-01 2022-01-28 中科三清科技有限公司 Method and device for determining emission list
CN115905770A (en) * 2022-10-28 2023-04-04 大连海事大学 Ship pollution emission track measuring and calculating method based on AIS data

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105590024A (en) * 2015-12-16 2016-05-18 河海大学 Method for compiling port atmospheric pollutant discharge inventory based on activities
CN107358048A (en) * 2017-07-14 2017-11-17 广东省环境科学研究院 A kind of high-precision Pollution From Ships thing Emission amount calculation method based on AIS data
EP3633318A1 (en) * 2018-10-05 2020-04-08 Offshore Navigation Limited An apparatus for determining an optimal route of a maritime ship
CN111159634A (en) * 2019-12-12 2020-05-15 天津大学 Method for calculating ship atmospheric pollutant emission and spatial distribution based on AIS data

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105590024A (en) * 2015-12-16 2016-05-18 河海大学 Method for compiling port atmospheric pollutant discharge inventory based on activities
CN107358048A (en) * 2017-07-14 2017-11-17 广东省环境科学研究院 A kind of high-precision Pollution From Ships thing Emission amount calculation method based on AIS data
EP3633318A1 (en) * 2018-10-05 2020-04-08 Offshore Navigation Limited An apparatus for determining an optimal route of a maritime ship
CN111159634A (en) * 2019-12-12 2020-05-15 天津大学 Method for calculating ship atmospheric pollutant emission and spatial distribution based on AIS data

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
张帆等: "基于Spark算法的船舶尾气排放实时计算方法研究", 《武汉理工大学学报（交通科学与工程版）》 *
张志炜等: "基于AIS轨迹修复的船舶排放空间表征改进方法与应用", 《环境科学学报》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113822630A (en) * 2021-09-14 2021-12-21 中远海运科技股份有限公司 AIS-based LNG ship transport tracking method and system
CN113822630B (en) * 2021-09-14 2022-04-12 中远海运科技股份有限公司 AIS-based LNG ship transport tracking method and system
CN113987058A (en) * 2021-11-01 2022-01-28 中科三清科技有限公司 Method and device for determining emission list
CN115905770A (en) * 2022-10-28 2023-04-04 大连海事大学 Ship pollution emission track measuring and calculating method based on AIS data

Also Published As

Publication number Publication date
CN112214721B (en) 2022-11-29

Similar Documents

Publication Publication Date Title
CN112214721B (en) AIS data-based dynamic ship emission list establishing method
CN108227041B (en) Horizontal visibility forecasting method based on site measured data and mode result
CN111814367A (en) Port ship emission monitoring and supervision cloud service system
CN110208880B (en) Sea fog detection method based on deep learning and satellite remote sensing technology
CN107358048A (en) A kind of high-precision Pollution From Ships thing Emission amount calculation method based on AIS data
CN109375290B (en) Cross-sea bridge fog monitoring system based on machine learning and application method thereof
CN112446096A (en) Ship host engine oil consumption dynamic estimation method based on big navigation data
CN112906955B (en) Tidal data fitting method based on multi-source data driving
CN105539797A (en) Navigation method and system of wind power assisting navigation ship based on ECDIS
CN109405831A (en) A kind of inland river network of rivers flight course planning method
CN107220521A (en) A kind of static data acquisition methods for Pollution From Ships thing Emission amount calculation
CN111103405A (en) Air quality forecast early warning system
CN115115138A (en) Offshore storm surge water increase prediction method
CN113985406B (en) Target track splicing method for marine radar
CN113935533B (en) Gale calculation method for yellow Bohai sea area
CN115017809A (en) Ship arrival amount prediction system and method
CN112215416B (en) Intelligent planning inspection route system and method
CN112561125A (en) Wind speed forecast multi-data fusion correction method
CN111723525A (en) PM2.5 inversion method based on multi-source data and neural network model
CN111638563A (en) Method for weather forecast of air route
CN111079595A (en) Novel concept and intelligent risk identification method for dynamic flood risk graph
Wan et al. Sea fog detection based on dynamic threshold algorithm at dawn and dusk time
CN114331042A (en) Ship collision risk analysis method
CN113743662A (en) Ship key technical parameter prediction method and system based on machine learning
CN102322864A (en) Airborne optic robust scene matching navigation and positioning method

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant