CN115310533A - AIS-based offshore wind farm identification method and system - Google Patents
AIS-based offshore wind farm identification method and system Download PDFInfo
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Abstract
The invention provides an AIS-based offshore wind farm identification method and system, based on ship AIS dynamic data and ship data, a suspected wind power installation ship and AIS characteristics of the wind power installation ship when fans are installed are determined by combining service logic, then, a DBSCAN clustering algorithm is adopted to cluster longitude and latitude position information of the suspected wind power installation ship meeting the AIS characteristics to obtain a plurality of first clusters, a Kmeans clustering algorithm is adopted to cluster each obtained first cluster again to obtain a plurality of second clusters, then, the clusters of the suspected fans and the clusters of the suspected offshore wind power plants are screened out by combining the service logic, the distance between each fan and other fans in each offshore wind power plant and the slope of each fan and the fan closest to the fan are calculated, and the offshore wind farm area on the ship is identified according to the number of fans in each suspected wind power plant, the distance between the fans, the slope and a ship destination port, the suspected wind power plant is warned to forbid to enter a related area and the development condition of offshore wind power is evaluated.
Description
Technical Field
The invention relates to the technical field of big data mining processing of offshore wind farms, in particular to an identification method and system of an offshore wind farm based on AIS.
Background
Wind power generation is one of the most mature and most scale development potential discovery modes in renewable energy power generation technology, and while the construction of land wind power plants is rapidly developed, the land wind energy utilization is limited, such as large occupied area, noise pollution and other problems, so that the wind power generation is converted to the offshore wind power generation.
The offshore wind power generation system has the advantages of abundant wind energy resources, stable unit operation, large unit capacity, high annual utilization hours, large energy output, small land area occupation, small environmental negative influence and the like. Offshore wind farms are important components for developing clean low-carbon energy and optimizing energy structures.
In 2021, the newly increased production scale of offshore wind power in China reaches 1690 kilowatts, the increased production scale of the same-ratio wind power is 340 percent, and the accumulated installed scale reaches 2638 kilowatts. However, at present, data about the offshore wind field are few, and accurate offshore wind field position information cannot be obtained, so that some ships mistakenly enter the offshore wind field area.
Disclosure of Invention
The invention provides an offshore wind farm identification method based on AIS (automatic identification system), aiming at solving the problems that the position information of the offshore wind farm is not accurate enough in the prior art, wherein the method is based on AIS data and ship data, adopts a DBSCAN (direct data storage controller area network) clustering algorithm and a Kmeans clustering algorithm to perform multi-stage clustering, finds and identifies the offshore wind farm by combining service logic, and determines the position of the wind farm. The method can calibrate the range of the offshore wind power plant, prevent ships from entering by mistake and evaluate the development condition of offshore wind power. The invention also relates to an AIS-based offshore wind farm identification system.
The technical scheme of the invention is as follows:
an AIS-based offshore wind farm identification method is characterized by comprising the following steps:
a data acquisition step: collecting ship data, and mining characteristics of the wind power installation ship from the ship data according to business logic to determine a suspected wind power installation ship;
a characteristic determination step: collecting AIS dynamic data of a suspected wind power installation ship, and determining AIS characteristics of the wind power installation ship when a fan is installed according to the AIS dynamic data; the AIS dynamic data comprises ship longitude and latitude position information, ground speed, ship heading, ship draught and ship destination;
clustering and calculating: clustering longitude and latitude position information of a suspected wind power installation ship meeting AIS characteristics by adopting a DBSCAN clustering algorithm to obtain a plurality of first clusters, clustering each obtained first cluster again by adopting a Kmeans clustering algorithm to obtain a plurality of second clusters, obtaining a central point longitude and latitude coordinate of each second cluster, calculating the radius of each second cluster according to the central point longitude and latitude coordinate, and calculating the variance of ship heading, the mean value of ship draught and the variance in each second cluster according to AIS dynamic data;
a fan identification step: intelligently screening clusters with the radius, the variance of the ship heading and the mean value and the variance of the ship draught which all meet respective preset conditions from the plurality of second clusters, and storing the clusters serving as suspected fans in a database;
wind power plant identification: and clustering the longitude and latitude coordinates of the central point of each cluster screened out as the suspected fans in the database by adopting a DBSCAN clustering algorithm to obtain a plurality of third clusters, taking the third clusters as suspected offshore wind power plants, calculating the distance between each fan and other fans in each suspected offshore wind power plant and the slope of each fan and the fan closest to the fan, and automatically identifying the offshore wind power plants according to the number of fans in each suspected offshore wind power plant, the distance between the fans, the slope and the destination port of the ship.
Preferably, in the data collecting step, the ship data includes a ship state field, a ship equipment field and a ship type field, the ship state field includes an operation field, a disassembly field and an order field, and the ship type field includes an offshore service providing field and a support platform field.
Preferably, in the characteristic determining step, the AIS characteristics include a speed to ground and a sailing state.
Preferably, in the clustering and calculating step, when clustering is performed by using a DBSCAN clustering algorithm, the contour coefficient is further used as an evaluation index of the clustering effect.
Preferably, in the clustering and calculating step, after a plurality of second clusters are obtained, the minimum time and the maximum time in the clusters are reserved, and the time difference is calculated according to the minimum time and the maximum time and is used as an evaluation index of the installation efficiency of the wind power installation vessel.
An AIS-based offshore wind farm identification system is characterized by comprising a data acquisition module, a characteristic determination module, a clustering and calculation module, a fan identification module and a wind farm identification module which are sequentially connected,
the data acquisition module acquires ship data, and performs wind power installation ship feature mining on the ship data according to business logic to determine a suspected wind power installation ship;
the characteristic determining module is used for acquiring suspected AIS dynamic data of the wind power installation ship and determining AIS characteristics of the wind power installation ship when a fan is installed according to the AIS dynamic data; the AIS dynamic data comprises ship longitude and latitude position information, ground speed, ship heading, ship draught and ship destination;
the clustering and calculating module is used for clustering longitude and latitude position information of a suspected wind power installation ship meeting AIS characteristics by adopting a DBSCAN clustering algorithm to obtain a plurality of first clusters, clustering each obtained first cluster again by adopting a Kmeans clustering algorithm to obtain a plurality of second clusters, obtaining longitude and latitude coordinates of a central point of each second cluster, calculating the radius of each second cluster according to the longitude and latitude coordinates of the central point, and calculating the variance of the ship heading, the mean value and the variance of ship draught in each second cluster according to AIS dynamic data;
the fan identification module intelligently screens clusters with the radius, the variance of the ship heading and the mean value and the variance of ship draught which all meet respective preset conditions from the second clusters, and stores the clusters as suspected fans in a database;
the wind power plant identification module is used for clustering longitude and latitude coordinates of a central point of each cluster screened out from the database as suspected fans by adopting a DBSCAN clustering algorithm to obtain a plurality of third clusters, the third clusters are used as suspected offshore wind power plants, the distance between each fan and other fans in each suspected offshore wind power plant and the slope of each fan and the fan closest to the fan are calculated, and the offshore wind power plants are automatically identified according to the number of fans in each suspected offshore wind power plant, the distance between the fans, the slope and the ship destination port.
Preferably, the vessel profile data includes a vessel status field including an operation field, a disassembly field and an order field, a vessel equipment field and a vessel type field including a marine service provision field and a support platform field.
Preferably, the AIS characteristics include speed over ground and sail status.
Preferably, in the clustering and calculating module, when clustering is performed by using a DBSCAN clustering algorithm, the contour coefficient is further used as an evaluation index of the clustering effect.
Preferably, in the clustering and calculating module, after a plurality of second clusters are obtained, the minimum time and the maximum time in the clusters are reserved, and a time difference is calculated according to the minimum time and the maximum time and is used as an evaluation index of the installation efficiency of the wind power installation vessel.
The invention has the beneficial effects that:
the invention provides an AIS-based offshore wind farm identification method, which is based on ship AIS dynamic data and ship data, determines a suspected wind power installation ship and AIS characteristics of the wind power installation ship when a fan is installed by combining business logic, then clusters longitude and latitude position information of the suspected wind power installation ship meeting the AIS characteristics by adopting a DBSCAN clustering algorithm to obtain a plurality of first clusters (namely fan clusters), and re-clusters each obtained fan cluster by adopting a Kmeans clustering algorithm to obtain a plurality of second clusters, so that the accurate position of each fan can be obtained, and the longitude and latitude coordinates of the central point of each second cluster are obtained and used as the accurate position of each fan. Clustering the central point of each fan by adopting a DBSCAN clustering algorithm, clustering a plurality of fan points into a plurality of wind power plants, screening out clusters of suspected fans and clusters of suspected offshore wind power plants by combining related service logics, calculating the distance between each fan and other fans in each suspected offshore wind power plant and the slope of each fan and the fan closest to the fan, automatically identifying an offshore wind field area according to the number of fans in each suspected offshore wind power plant, the distance between the fans, the slope and a ship destination port, warning that a ship is prohibited from entering the related area, further intelligently evaluating the installation efficiency of the wind power installation ship and evaluating the development condition of offshore wind power.
The invention also relates to an AIS-based offshore wind farm identification system, which corresponds to the AIS-based offshore wind farm identification method and can be understood as a system for realizing the AIS-based offshore wind farm identification method, and the system comprises a data acquisition module, a characteristic determination module, a clustering and calculation module, a fan identification module and a wind farm identification module which are sequentially connected, wherein the modules work in a mutual cooperation mode, on the basis of AIS data and ship data, a DBSS clustering algorithm and a Kmeans clustering algorithm are adopted for carrying out multi-stage clustering, the offshore wind farm is found and automatically identified by combining service logic, the position of the wind farm is determined, an offshore wind farm area can be automatically calibrated, a ship is warned to be forbidden to enter a related area, and the development condition of offshore wind power can be intelligently evaluated.
Drawings
FIG. 1 is a flow chart of the AIS-based offshore wind farm identification method of the present invention.
FIG. 2 is a schematic view of the sailing trajectory of a bridge ship.
FIG. 3 is a schematic diagram of a wind farm at Qingzhou, yueYangjiang.
Detailed Description
The present invention will be described with reference to the accompanying drawings.
The invention relates to an AIS-based offshore wind farm identification method, the flow chart of which is shown in figure 1, and the method sequentially comprises the following steps:
a data acquisition step: collecting ship data, and mining characteristics of the wind power installation ship from the ship data according to business logic to determine a suspected wind power installation ship; specifically, the method includes the steps of firstly connecting psypog 2 (a PostgreSQL database interface of Python language) in Python language with a PostgreSQL database, inquiring ship data from the PostgreSQL database, and after the ship data are obtained, specifically using the ship data: a ship status shipstatus field, a ship equipment geordescriptnarrative field and a ship type shiptype field. The shipstatus field mainly describes the current operating state of the ship, and includes: fields in operation, disassembly and order; the getdescriptivenarrative field mainly describes the equipment condition of the ship; the shiptype field mainly describes the detailed type of the ship, including: offshore Support Vessel Offshore Support Vessel, support Platform, etc.
By inquiring related data and referring to business logic (or called business knowledge), the wind power installation vessel is determined to have a hoisting function, mainly work as offshore service and possibly have a self-elevating platform. And inquiring the data to determine some wind power installation ships and the time periods for installing the wind turbines. By collating the ship data of the known wind power installation ships and the ship data of the fans which are installed in parallel with the known wind power installation ships, the ship of the wind power installation ship is determined to be a ship with the following characteristics: the Geardescriptivenarrativ field contains 'SWL', meaning crane; the shipstatus field contains 'In Service/Commission' meaning that the ship is currently In operation; the shipptype field contains: 'offset Support Vessel', 'jack up', etc. 'offset Support Vessel' indicates that the Vessel is mainly dedicated to providing Offshore services, and 'jack up' indicates that the Vessel is provided with a jack-up platform. And inquiring all ships with the characteristics in the database according to the conditions to use as suspected wind power installation ships.
A characteristic determination step: collecting AIS dynamic data of a suspected wind power installation ship, and determining AIS characteristics of the wind power installation ship when a fan is installed according to the AIS dynamic data and by combining business knowledge; the AIS dynamic data comprises ship longitude and latitude position information, ground speed, ship heading, ship draught and ship destination;
specifically, AIS dynamic data of all suspected wind power installation ships are inquired from a PostgreSQL database, after the data are obtained, the AIS dynamic data are preprocessed, and fields used by the AIS dynamic data comprise: status, lon, lat, sog, cog, hdg, draught, and dest fields. Where status = {0,1,5}, and the status field is: 1 is anchoring, 5 is anchoring, and 0 is sailing; lon and lat are longitude and latitude locations; the sog is the real-time ground speed of the ship; hdg is the heading of the bow, i.e. the heading of the bow; draught is the real-time draft of the ship; dest is the destination port of the ship.
According to the method, by combining relevant business knowledge, when a fan is installed on a ship, the navigational speed is close to 0, the bow of the ship keeps unchanged in a certain time period, the offshore operation draft is not too large, and AIS state data of the fan installed on the wind power installation ship are mined according to the known wind power installation ship and the AIS state data in the time period for installing the fan on the known wind power installation ship, the AIS state data are determined to have the following AIS characteristics, the AIS state data are from the suspected wind power installation ship, and the AIS characteristics comprise the speed to the ground and the navigation state, wherein the speed to the ground sog is 0, the navigation state is status =1, namely the current state is 1, namely the anchoring state.
Clustering and calculating: clustering longitude and latitude position information of a suspected wind power installation ship meeting AIS characteristics by adopting a DBSCAN clustering algorithm to obtain a plurality of first clusters serving as fan clusters, clustering each obtained fan cluster again by adopting a Kmeans clustering algorithm to obtain a plurality of second clusters, obtaining a longitude and latitude coordinate of a central point of each fan cluster, calculating the radius of each fan cluster according to the longitude and latitude coordinate of the central point, and calculating the variance of ship heading, the mean value and the variance of ship draught in each fan cluster according to AIS dynamic data;
specifically, clustering longitude and latitude position information of a suspected wind power installation ship with the ground speed sog of 0 and the anchored sailing state by adopting a DBSCAN clustering algorithm to obtain a plurality of fan clusters, clustering each fan cluster formed by clustering again by adopting a Kmeans clustering algorithm to obtain a plurality of second clusters, obtaining a longitude and latitude coordinate of a central point of each second cluster, calculating the longitude and latitude distance from each point in the cluster to the central point according to the position coordinate of the central point, and taking the maximum value of the distance as the radius of the cluster. Meanwhile, the minimum time and the maximum time in the second cluster are reserved, the time difference is calculated according to the minimum time and the maximum time and is used as an evaluation index of the installation efficiency of the wind power installation ship, the variance of the ship heading hdg in the second cluster and the mean value and the variance of the ship draught are calculated according to AIS dynamic data and are all used as parameters of each second cluster, and each second cluster is suspected to be a fan.
The DBSCAN clustering algorithm is an algorithm in machine learning unsupervised learning, compared with other clustering algorithms, the DBSCAN clustering algorithm is high in clustering speed, can effectively process noise points and find spatial clusters of any shape, and does not need to specify the number of clusters. The parameters only need to pay attention to neighborhood parameters (epsilon, minPts), wherein epsilon is the radius of a neighborhood, the neighborhood of the number minPts in the radius of one neighborhood is considered as a cluster, and the algorithm is more convenient and quicker to find the optimal parameters. When the clustering is carried out by using the DBSCAN clustering algorithm, the Euclidean distance of longitude and latitude numerical values is directly adopted, and the longitude and latitude distance is not adopted, because: 1) The latitude and longitude distances are complex to calculate, and the algorithm is slowed down for a large amount of data; 2) The latitude and longitude distances and the Euclidean distances of the latitude and longitude values are not very different in the invention. In consideration of algorithm efficiency, the Euclidean distance of the decimal longitude and latitude value is adopted as a distance function of the DBSCAN cluster. And the contour coefficient in machine learning is used as an evaluation index of the clustering effect. The closer the contour coefficient is to 1, the better the clustering effect is, the closer to-1, the more the sample i should be classified into other clusters.
The Kmeans clustering algorithm is an algorithm in machine learning unsupervised learning, and compared with other clustering algorithms, the Kmeans clustering algorithm is only used for clustering regular shape clusters and needs to appoint the number of classes in advance. The principle of the Kmeans clustering algorithm is as follows: x = { x for a given sample set 1 ,x 2 ,...,x n And dividing the sample set into K clusters according to the distance between samples. The points within the clusters are connected as close together as possible, while the distance between the clusters is as large as possible. I.e., assume that the goal is to divide k clusters C 1 ,C 2 ,C 3 ,...,C k The goal is to minimize the squared error E:
in the above formula, x is the sample set, k is the number of clusters, μ i Is a cluster C i Mean vector of (u) i The expression is as follows:
a fan identification step: intelligently screening clusters with the radius, the variance of the ship heading and the mean value and the variance of the ship draught meeting respective preset conditions from the second clusters by combining with business knowledge, and storing the clusters serving as suspected fans into a database;
specifically, by inquiring relevant data and combining business knowledge, the radius of each offshore wind turbine is about 45-60 meters, the heading of the wind power installation vessel can not be adjusted too much when the wind power installation vessel is used for installing the wind turbines, and the draught of the offshore wind turbines is not too large and approximately constant in offshore areas, so that a second cluster with the radius within 100 meters, the mean value of the draught within 7 meters and the variance of the draught within 5 is screened out and stored in a database as a suspected wind turbine for subsequent use.
Wind power plant identification: clustering longitude and latitude coordinates of a central point of each cluster screened out from the database as suspected fans by adopting a DBSCAN clustering algorithm to obtain a plurality of third clusters, taking the third clusters as suspected offshore wind plants, calculating the distance between each fan and other fans in each suspected offshore wind plant and the slope of each fan and the fan closest to the fan, and automatically identifying the offshore wind plants according to the number of fans in each suspected offshore wind plant, the distance between the fans, the slope and a ship destination port.
Specifically, a DBSCAN clustering algorithm is adopted to cluster longitude and latitude coordinates of a central point of each cluster screened out from a database to serve as a suspected fan to obtain a plurality of third clusters, and the formed third clusters are used as suspected offshore wind power plants. The fans in the offshore wind farm are regularly arranged in a linear form, the number of the fans in the wind farm is more than 10, the intervals between the fans are equal, the distance between each fan and other fans in each suspected offshore wind farm and the slope of each fan and the fan closest to the fan are calculated, and if the number of the fans in a certain cluster (namely a certain suspected offshore wind farm) in a third cluster is more than 10, the distances between the fans are approximately equal, the slopes are approximately the same, and the port of ship destination is the same, the cluster is automatically determined to be an offshore wind farm.
The embodiment is as follows:
a data acquisition step: by inquiring relevant news of offshore wind farms in China, ship names and fan installation time of a plurality of wind power installation ships are obtained. The ship data of the ships are inquired in the database, the ships have the same common points and are special ships, and the Geardescriptivenarrativ fields all contain 'SWL', which indicates that the ships are equipped with cranes for hoisting fan blades. The shipptype field contains: 'offset Support Vessel', 'jack up', etc. 'offset Support Vessel' means that the Vessel is primarily dedicated to providing Offshore services, 'jack up' illustrates that the Vessel is provided with a jack-up platform.
Taking a bridge ship as an example, the bridge ship completes the installation of a No. T31 fan in the three-stage of Sanxia Yangjiang sand raking in 2021 and 27 days, and the track of the ship forms an annular cluster about 3 months and 27 days, so that the track characteristic of the fan installation is met. A characteristic determination step: the database is inquired about the AIS data of the bridge good ships 2021.03.01-2021.04.10, and the trajectory data in the time period is plotted and represented by using a Plotly visualization module in a Python language. As shown in fig. 2, it is obvious that, in this time period, the tracks form 3 circular clusters, and for the tracks when the wind turbine is installed, by mining the ground speed sog, (status) field status, heading hdg and destination dest data of the ship in the 3 clusters, the following features are found: sog is approximately 0,status =1, i.e. the vessel state is moored. Therefore, the AIS characteristic of the wind power installation vessel is set to sog close to 0 and status =1 when the wind turbine is installed.
Clustering and calculating: the fields meeting Geardescriptivenarrativ in the query database all contain 'SWL' and the fields of the script include: the ships such as 'offset superior vessel' and 'jack up' inquire AIS dynamic data of the ships which are close to wind farms at second seas in Qingzhou, guangdong, and Yangtze, namely, longitude lon is in an interval [111.45,111.68] and latitude lat is in an interval [21.22,21.38] all year round in 2021 year and meet sog =0, status =1, and cluster longitude and latitude position information by using a CAN (controller area network) clustering algorithm to obtain a plurality of first clusters as fan clusters. And using the contour coefficients as an evaluation method to select parameters, wherein the optimal parameters are epsilon =0.01 and minpts =10. For each fan cluster obtained by clustering, in order to obtain the central point and the radius of the fan cluster and other data information in the cluster, clustering each fan cluster formed by clustering again by using a Kmeans clustering algorithm to obtain a plurality of second clusters, setting the number k of the second clusters to be 1, obtaining the central point position coordinate of each second cluster, calculating the longitude and latitude distance from each point in the second clusters to the central point according to the central point position coordinate, and taking the maximum value of the distance as the radius of the second cluster. Meanwhile, the minimum time and the maximum time in the second clusters are reserved, the time difference is calculated to be used as the duration, the variance of the ship heading hdg and the variance and the mean of the ship real-time draft are calculated, and the results are reserved as the parameters of each second cluster. A fan identification step: according to the related business knowledge, when the wind power installation vessel is provided with the fan, the bow direction of the vessel is basically kept unchanged, the length of the fan blade is 45-60 meters, and the draft is not too large because the wind field is in the offshore area. Therefore, preferably, the class with the radius within 100 meters, the variance of hdg within 5 and the mean of draught within 7 is selected, and is regarded as a wind turbine and stored in a database for the subsequent identification of the offshore wind farm.
Wind power plant identification: and clustering longitude and latitude coordinates of the central points of the clusters screened out from the database as suspected fans by adopting a DBSCAN clustering algorithm to obtain a plurality of third clusters, namely the wind power plant clusters in the wind power plant range of Qingzhou, yangjiang, yue, in 2021.
As shown in fig. 3, it can be clearly seen that the fans in the offshore wind farm are regularly arranged in a straight line, the number of fans in the wind farm is large, more than 10 fans are provided, the intervals between the fans are equal, the distance between each fan is approximately 530 meters, both dest are 'YANG JIANG', and it can be determined that the cluster is an offshore wind farm. More installed groups of fans can also be seen in the range through the opentreet map.
The invention also relates to an AIS-based offshore wind farm identification system, which corresponds to the AIS-based offshore wind farm identification method and can be understood as a system for realizing the method, the system comprises a data acquisition module, a characteristic determination module, a clustering and calculating module, a fan identification module and a wind farm identification module which are connected in sequence, specifically,
the data acquisition module is used for acquiring ship data and mining the characteristics of the wind power installation ship from the ship data according to business logic so as to determine a suspected wind power installation ship;
the characteristic determining module is used for acquiring suspected AIS dynamic data of the wind power installation vessel and determining AIS characteristics of the wind power installation vessel when a fan is installed according to the AIS dynamic data and by combining business knowledge; the AIS dynamic data comprises ship longitude and latitude position information, ground speed, ship heading, ship draught and ship destination;
the clustering and calculating module is used for clustering longitude and latitude position information of a suspected wind power installation ship meeting AIS characteristics by adopting a DBSCAN clustering algorithm to obtain a plurality of first clusters, clustering each obtained first cluster again by adopting a Kmeans clustering algorithm to obtain a plurality of second clusters, obtaining a longitude and latitude coordinate of a central point of each second cluster, calculating the radius of each second cluster according to the longitude and latitude coordinate of the central point, and calculating the variance of the ship heading, the mean value and the variance of ship draught in each second cluster according to AIS dynamic data;
the fan identification module is used for screening clusters with the radius, the variance of the ship heading and the mean value and the variance of the ship draught which all meet respective preset conditions from the second clusters by combining with business knowledge, and storing the clusters as suspected fans in a database;
the wind power plant identification module is used for clustering longitude and latitude coordinates of a central point of each cluster screened out from the database as suspected fans by adopting a DBSCAN clustering algorithm to obtain a plurality of third clusters, using the third clusters as suspected offshore wind power plants, calculating the distance between each fan and other fans in each suspected offshore wind power plant and the slope of each fan and the fan closest to the fan, and automatically identifying the offshore wind power plants according to the number of fans in each suspected offshore wind power plant, the distance between the fans, the slope and the destination port of a ship.
Preferably, the vessel profile data includes a vessel status field including an operation field, a disassembly field and an order field, a vessel equipment field and a vessel type field including a provision offshore service field and a support platform field.
Preferably, the AIS characteristics include speed over ground and sail status.
Preferably, in the clustering and calculating module, when clustering is performed by using a DBSCAN clustering algorithm, the contour coefficient is also used as an evaluation index of the clustering effect.
Preferably, in the clustering and calculating module, after a plurality of second clusters are obtained, the minimum time and the maximum time in the clusters are reserved, and the time difference is calculated according to the minimum time and the maximum time and is used as an evaluation index of the installation efficiency of the wind power installation vessel.
The invention provides an objective and scientific AIS-based offshore wind farm identification method and system, which are based on AIS data and ship data, adopt a DBSCAN clustering algorithm and a Kmeans clustering algorithm to perform multi-level clustering, find and identify an offshore wind farm by combining business knowledge, determine the position of the wind farm, can calibrate the range of the offshore wind farm, prevent ships from entering by mistake and evaluate the development condition of offshore wind power.
It should be noted that the above-mentioned embodiments enable a person skilled in the art to more fully understand the invention, without restricting it in any way. Therefore, although the present invention has been described in detail with reference to the drawings and examples, it should be understood by those skilled in the art that the present invention may be modified and replaced by other embodiments, and in any case, the technical solutions and modifications thereof without departing from the spirit and scope of the present invention should be covered by the protection scope of the present invention.
Claims (10)
1. An AIS-based offshore wind farm identification method is characterized by comprising the following steps:
a data acquisition step: collecting ship data, and mining characteristics of the wind power installation ship from the ship data according to business logic to determine a suspected wind power installation ship;
a characteristic determination step: collecting AIS dynamic data of a suspected wind power installation ship, and determining AIS characteristics of the wind power installation ship when a fan is installed according to the AIS dynamic data; the AIS dynamic data comprises ship longitude and latitude position information, ground speed, ship heading, ship draught and ship destination;
clustering and calculating: clustering longitude and latitude position information of a suspected wind power installation ship meeting AIS characteristics by adopting a DBSCAN clustering algorithm to obtain a plurality of first clusters, clustering each obtained first cluster again by adopting a Kmeans clustering algorithm to obtain a plurality of second clusters, obtaining a central point longitude and latitude coordinate of each second cluster, calculating the radius of each second cluster according to the central point longitude and latitude coordinate, and calculating the variance of ship heading, the mean value and the variance of ship draught in each second cluster according to AIS dynamic data;
a fan identification step: intelligently screening clusters with the radius, the variance of the ship heading and the mean value and the variance of the ship draught which all meet respective preset conditions from the plurality of second clusters, and storing the clusters serving as suspected fans in a database;
a wind power plant identification step: clustering longitude and latitude coordinates of a central point of each cluster screened out from the database as suspected fans by adopting a DBSCAN clustering algorithm to obtain a plurality of third clusters, taking the third clusters as suspected offshore wind plants, calculating the distance between each fan and other fans in each suspected offshore wind plant and the slope of each fan and the fan closest to the fan, and automatically identifying the offshore wind plants according to the number of fans in each suspected offshore wind plant, the distance between the fans, the slope and a ship destination port.
2. The AIS-based offshore wind farm identification method according to claim 1, wherein in the data collection step, the vessel profile data includes a vessel status field including an operation field, a decommissioning field and an order field, a vessel equipment field and a vessel type field including a provision offshore service field and a support platform field.
3. The AIS-based offshore wind farm identification method according to claim 1, wherein in the characteristic determining step, the AIS characteristics include speed over ground and sail status.
4. The AIS-based offshore wind farm identification method according to any of the claims 1 to 3, wherein in the clustering and calculating step, when clustering is performed by using a DBSCAN clustering algorithm, profile coefficients are also used as evaluation indexes of clustering effect.
5. The AIS-based offshore wind farm identification method according to claim 4, wherein in the clustering and calculating step, after a plurality of second clusters are obtained, the minimum time and the maximum time in the clusters are retained, and the time difference is calculated according to the minimum time and the maximum time and used as an evaluation index of the installation efficiency of the wind power installation vessel.
6. An AIS-based offshore wind farm identification system is characterized by comprising a data acquisition module, a characteristic determination module, a clustering and calculating module, a fan identification module and a wind farm identification module which are connected in sequence,
the data acquisition module acquires ship data, and performs wind power installation ship feature mining on the ship data according to business logic to determine a suspected wind power installation ship;
the characteristic determination module is used for acquiring suspected AIS dynamic data of the wind power installation ship and determining AIS characteristics of the wind power installation ship when a fan is installed according to the AIS dynamic data; the AIS dynamic data comprises ship longitude and latitude position information, ground speed, ship heading, ship draught and ship destination;
the clustering and calculating module is used for clustering longitude and latitude position information of a suspected wind power installation ship meeting AIS characteristics by adopting a DBSCAN clustering algorithm to obtain a plurality of first clusters, clustering each obtained first cluster again by adopting a Kmeans clustering algorithm to obtain a plurality of second clusters, obtaining a longitude and latitude coordinate of a central point of each second cluster, calculating the radius of each second cluster according to the longitude and latitude coordinate of the central point, and calculating the variance of the ship heading, the mean value and the variance of ship draught in each second cluster according to AIS dynamic data;
the fan identification module intelligently screens clusters with the radius, the variance of the ship heading and the mean value and the variance of ship draught which all meet respective preset conditions from the second clusters, and stores the clusters as suspected fans in a database;
the wind power plant identification module is used for clustering longitude and latitude coordinates of a central point of each cluster screened out from the database as suspected fans by adopting a DBSCAN clustering algorithm to obtain a plurality of third clusters, the third clusters are used as suspected offshore wind power plants, the distance between each fan and other fans in each suspected offshore wind power plant and the slope of each fan and the fan closest to the fan are calculated, and the offshore wind power plants are automatically identified according to the number of fans in each suspected offshore wind power plant, the distance between the fans, the slope and the ship destination port.
7. The AIS-based offshore wind farm identification system according to claim 6, wherein the vessel profile data includes a vessel status field including an on-going field, a decommissioning field and an order field, a vessel equipment field and a vessel type field including an on-shore services provided field and a support platform field.
8. The AIS-based offshore wind farm identification system according to claim 6, wherein the AIS characteristics include speed over ground and state of travel.
9. The AIS-based offshore wind farm identification system according to any of claims 6 to 8, wherein in the clustering and calculating module, when clustering is performed by using a DBSCAN clustering algorithm, profile coefficients are also used as evaluation indexes of clustering effects.
10. The AIS-based offshore wind farm identification system according to claim 9, wherein in the clustering and calculating module, after a plurality of second clusters are obtained, the minimum time and the maximum time in the clusters are retained, and the time difference is calculated according to the minimum time and the maximum time and used as an evaluation index of the installation efficiency of the wind power installation vessel.
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