1. A ship water pollution prediction method based on AIS data is characterized by comprising the following specific steps:

s1: acquiring data, namely acquiring AIS data through related equipment, constructing an AIS data and static information association attribute database, uploading the AIS data and AIS message information through specific identifiers, and ensuring that the data cannot be read visually;

s2: decoding the data, namely decoding the data according to the special identifier translation book so as to translate the data into data which can be intuitively read;

s3: verifying missing data, and aiming at the current status information of the tonnage and the missing emission factor of the ship in the AIS static data, establishing a ship AIS static information associated attribute library by a method combining multi-source data matching and regression simulation calculation so as to supplement and perfect the missing data;

s4: acquiring ship motion data, namely acquiring information of running time, load rate, navigation speed, working condition division and navigation track of a ship by combining an AIS dynamic database;

s5: determining the emission pollution amount of the ship, calculating the emission pollution amount of the ship according to specific emission factors of the ship, then obtaining a networked emission list, determining the spatial distribution characteristics of the ship water pollution in a navigation area, and meanwhile, respectively counting the pollutant discharge amount of the ship in different time periods and determining the time distribution characteristics;

s6: analyzing and counting results, namely counting the change of the ship pollutant discharge amount in the research area by taking the calculation result of the ship water pollutant discharge amount and the corresponding time point as key information, so as to draw a time change trend graph, and obtaining a peak value change rule by referring to the change trend graph;

s7: and displaying a result space, namely displaying the ship discharge list result on a map in a thermodynamic diagram mode, analyzing the spatial distribution characteristics of each pollutant, and obtaining the water pollution distribution condition of a channel and a port so as to complete the prediction of the ship water pollution.

2. The AIS data-based ship water pollution prediction method according to claim 1, wherein the translated data in the S2 process is mainly divided into two categories, namely: and the dynamic data items and the static data items are stored respectively through the server to form a dynamic database of the ship and a static database of the ship.

3. The AIS data-based ship water pollution prediction method according to claim 1, wherein the running time in the S4 process is calculated according to the time difference of information reported dynamically before and after the ship, wherein the working conditions of the ship are divided into an anchoring state, a maneuvering state and a cruising state, respectively, wherein the speed of the anchoring state is less than 1kn, the speed of the maneuvering state is 1-7kn, and the speed of the cruising state is greater than 7 kn.

4. The method for predicting ship water pollution based on AIS data according to claim 1, wherein the specific calculation manner of the ship emission pollution amount in the S5 process is as follows: a first step; according to the operation stage of the ship, marine route discharge classified into a fixed working condition and port adjacent discharge classified into a variable working condition are carried out in space;

secondly, calculating the ship emission under each operation condition according to a specific emission factor,

thirdly, determining a calculation formula, namely the formula isWherein E is the pollutant emission, i is the batch number of the ship, E_MFor the discharge of ship domestic sewage, E_AThe residual oil discharge amount of the ship is obtained.

5. The AIS data-based ship water pollution prediction method according to claim 1, wherein the matching of the multi-source data in the S3 process respectively comprises multi-source query matching of database information of visa, entry and exit port and ship inspection of a ship to an AIS static database.

6. The AIS data-based ship water pollution prediction method according to claim 1, wherein the AIS data in the S1 process is obtained in a specific manner: and carrying out AIS data acquisition according to an AIS data protocol, wherein the acquired AIS data comprises AIS data acquired by a shore-based acquisition and AIS data acquired by an orbital satellite.

7. The AIS data-based ship water pollution prediction method according to claim 4, wherein the emission factors in the S5 process mainly comprise: residual oil discharge amount of ships and domestic sewage discharge amount.

8. The AIS data-based ship water pollution prediction method according to claim 1, wherein the time scales of the S6 are respectively month, day and hour as corresponding time nodes, so that the change of the emission of ship pollutants under three time parameters is obtained.

Background

Shipping refers to transporting people or goods by water or air transportation. Generally, the time required for water transportation is long, but the cost is low, which is not comparable to air transportation and land transportation. Water transport can carry a large amount of cargo per voyage, while air and land transport can carry a relatively small amount of cargo per voyage. Therefore, in international trade, water transportation is a common transportation method.

The ship is used as the most basic navigation tool in the water running process, has irreplaceability for wide application of traffic, brings great convenience, simultaneously causes non-negligible influence on the water environment in China by pollutants generated and discharged by a running ship, and has important significance for the works of ship pollutant emission reduction, running ship supervision, related policy control and the like by quickly and accurately measuring and calculating the production and discharge capacity and the production/pollution intensity of various pollution indexes in the running ship pollutants and the pollutants.

The existing AIS system is lack of detailed statistical data for calculating ship water pollutant discharge, certain errors can be brought to the prediction result of the water pollutants, and particularly for a specific measuring and calculating area, specific statistical calculation needs to be carried out aiming at ship discharge factor parameters, so that the accuracy of ship water pollution prediction is ensured.

Therefore, a ship water pollution prediction method based on AIS data is provided to solve the problems.

Disclosure of Invention

The invention aims to solve the defects in the prior art and provides a ship water pollution prediction method based on AIS data.

In order to achieve the purpose, the invention adopts the following technical scheme:

a ship water pollution prediction method based on AIS data comprises the following specific steps.

S1: acquiring data, namely acquiring AIS data through related equipment, constructing an AIS data and static information association attribute database, uploading the AIS data and AIS message information through specific identifiers, and ensuring that the data cannot be read visually;

s2: decoding the data, namely decoding the data according to the special identifier translation book so as to translate the data into data which can be intuitively read;

s3: verifying missing data, and aiming at the current status information of the tonnage and the missing emission factor of the ship in the AIS static data, establishing a ship AIS static information associated attribute library by a method combining multi-source data matching and regression simulation calculation so as to supplement and perfect the missing data;

s4: acquiring ship motion data, namely acquiring information of running time, load rate, navigation speed, working condition division and navigation track of a ship by combining an AIS dynamic database;

s5: determining the emission pollution amount of the ship, calculating the emission pollution amount of the ship according to specific emission factors of the ship, then obtaining a networked emission list, determining the spatial distribution characteristics of the ship water pollution in a navigation area, and meanwhile, respectively counting the pollutant discharge amount of the ship in different time periods and determining the time distribution characteristics;

s6: analyzing and counting results, namely counting the change of the ship pollutant discharge amount in the research area by taking the calculation result of the ship water pollutant discharge amount and the corresponding time point as key information, so as to draw a time change trend graph, and obtaining a peak value change rule by referring to the change trend graph;

s7: and displaying a result space, namely displaying the ship discharge list result on a map in a thermodynamic diagram mode, analyzing the spatial distribution characteristics of each pollutant, and obtaining the water pollution distribution condition of a channel and a port so as to complete the prediction of the ship water pollution.

Preferably, the data translated in the S2 process is mainly divided into two categories, which are: and the dynamic data items and the static data items are stored respectively through the server to form a dynamic database of the ship and a static database of the ship.

Preferably, the running time calculation mode in the S4 process is a time difference value of information dynamically reported before and after the ship, where the working conditions of the ship are divided into an anchoring state, a maneuvering state and a cruising state, respectively, where the speed of the anchoring state is less than 1kn, the speed of the maneuvering state is 1-7kn, and the speed of the cruising state is greater than 7 kn.

Preferably, the specific calculation method of the ship emission pollution amount in the S5 process is as follows: a first step; according to the operation stage of the ship, marine route discharge classified into a fixed working condition and port adjacent discharge classified into a variable working condition are carried out in space;

secondly, calculating the ship emission under each operation condition according to a specific emission factor,

thirdly, determining a calculation formula, namely the formula isWherein E is the pollutant emission, i is the batch number of the ship, E_MFor the discharge of ship domestic sewage, E_AThe residual oil discharge amount of the ship is obtained.

Preferably, the matching of the multi-source data in the S3 process includes performing multi-source query matching on the AIS static database for the database information of the visa, the entry and exit port, and the ship inspection of the ship, respectively.

Preferably, the AIS data in the S1 process is obtained in a specific manner: and carrying out AIS data acquisition according to an AIS data protocol, wherein the acquired AIS data comprises AIS data acquired by a shore-based acquisition and AIS data acquired by an orbital satellite.

Preferably, the emission factor in the S5 process mainly includes: residual oil discharge amount of ships and domestic sewage discharge amount.

Preferably, in the S6 process, the time scales of month, day, and hour are respectively used as corresponding time nodes, so as to obtain the emission amount variation of the ship pollutants under three time parameters.

Compared with the prior art, the invention has the beneficial effects that:

1. the method is based on AIS data, utilizes dynamic reported information of the ship to compile the discharge amount of the ship water pollutants, realizes effective supplement of AIS data by establishing an AIS static information database and an AIS dynamic data information database, utilizes the remote data to match missing data, perfects the discharge factor information of the ship, accurately predicts the discharge amount change of the ship pollutants under different time parameters by different time nodes, and has higher accuracy of water pollution prediction.

2. According to the method, AIS data processing experience, space distribution of emission and time distribution are summarized, and the pollutant emission relations of the ship in an anchoring state, a maneuvering state and a cruising state are respectively counted and combed through analysis of a sailing area, so that necessary work support is provided for ship pollution supervision;

3. according to the invention, the calculation result is displayed on the map in the form of thermodynamic diagram, and the spatial distribution characteristics of each pollutant are analyzed, so that the water pollution distribution conditions of the channel and the port are obtained, and the method is more intuitive and is convenient for workers to master pollution information at the first time.

Drawings

Fig. 1 is a schematic flow chart of a ship water pollution prediction method based on AIS data according to the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.

Referring to fig. 1, a ship water pollution prediction method based on AIS data includes the following specific steps:

s1: acquiring data, namely acquiring AIS data through related equipment, wherein the AIS data is acquired in a specific mode: the method comprises the steps of collecting AIS data according to an AIS data protocol, wherein the collected AIS data comprises AIS data collected on a shore base and AIS data collected by an orbit satellite, constructing an AIS data and static information association attribute database, and uploading the AIS data and AIS message information through specific identifiers to ensure that the data cannot be read visually;

s2: and data decoding, namely decoding the data according to the special identifier translation book so as to translate the data into data which can be visually read, wherein the translated data mainly comprises two types, namely: the dynamic data items and the static data items are stored respectively through the server to form a dynamic database of the ship and a static database of the ship;

s3: verifying missing data, and aiming at the current status information of the tonnage of the ship and the missing emission factor in the AIS static data, establishing a ship AIS static information associated attribute library by a method combining multi-source data matching and regression simulation calculation so as to supplement and perfect the missing data, wherein the matching of the multi-source data respectively comprises the multi-source query matching of the database information of visa, entry and exit port and ship inspection of the ship to the AIS static database;

s4: acquiring ship motion data, and acquiring information of running time, load rate, sailing speed, working condition division and sailing track of a ship by combining an AIS dynamic database, wherein the running time calculation mode is a time difference value according to dynamic reporting information before and after the ship, the working condition division of the ship is respectively in an anchoring state, a maneuvering state and a cruising state, the sailing speed in the anchoring state is less than 1kn, the speed in the maneuvering state is 1-7kn, and the sailing speed in the cruising state is greater than 7 kn;

s5: determining the emission pollution amount of the ship, combing the pollutant emission amount relations of the ship under the anchoring state, the maneuvering state and the cruising state by combining different divisions of the working conditions of the ship, and calculating the emission pollution amount of the ship according to specific emission factors of the ship, wherein the emission factors in the process mainly comprise: the specific calculation mode of the residual oil discharge amount and the domestic sewage discharge amount of the ship and the pollution discharge amount of the ship is as follows: a first step; according to the operation stage of the ship, marine route discharge classified into a fixed working condition and port adjacent discharge classified into a variable working condition are carried out in space;

secondly, calculating the ship emission under each operation condition according to a specific emission factor,

thirdly, determining a calculation formula, namely the formula isWherein E is the pollutant emission, i is the batch number of the ship, E_MFor the discharge of ship domestic sewage, E_AThe residual oil discharge amount of the ship is obtained.

Then, a networked discharge list is obtained, the spatial distribution characteristics of the ship water pollution in the navigation area are determined, meanwhile, the pollutant discharge amount of the ship in different time periods can be respectively counted, and the time distribution characteristics are determined;

s6: analyzing and counting results, namely respectively taking months, days and hours as corresponding time nodes, and taking the calculation result of the ship water pollutant discharge amount and corresponding time points as key information to count the change of the ship pollutant discharge amount under three time parameters in a research area, thereby drawing a time change trend graph, obtaining a peak value change rule by referring to the change trend graph, and accurately predicting the discharge amount change of the ship pollutants under different time parameters through different time nodes, so that the accuracy of water pollution prediction is higher;

s7: and displaying a result space, namely displaying the ship discharge list result on a map in a thermodynamic diagram mode, analyzing the spatial distribution characteristics of each pollutant, and obtaining the water pollution distribution condition of a channel and a port so as to complete the prediction of the ship water pollution.

The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.