CN112241819A - Frequent activity pattern mining method, device, equipment and storage medium for ship - Google Patents
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Abstract
The application discloses a frequent activity pattern mining method, device, equipment and storage medium for a ship. The method comprises the following steps: identifying a first set of berthing behaviors of the vessel based on the vessel historical trajectory data, the first set of berthing behaviors including at least one berthing behavior; determining a parking position area corresponding to the parking behavior based on the occurrence position of each parking behavior; determining a frequent activity pattern of the vessel based on the berthing position areas, the frequent activity pattern being represented by a berthing position area number subsequence having a frequency greater than or equal to a frequency threshold, the berthing position area number subsequence being obtained by arranging berthing position area numbers corresponding to berthing behaviors of the vessel in a time series. According to the technical scheme provided by the embodiment of the application, the frequent activity pattern of the ship is excavated in the berthing position area of the berthing behavior, so that the accuracy of the frequent activity pattern of the excavated ship is ensured, and the error of the frequent activity pattern of the excavated ship is reduced.
Description
Technical Field
The present application relates generally to the field of data processing technologies, and in particular, to a method, an apparatus, a device, and a storage medium for mining frequent activity patterns of a ship.
Background
Ocean transportation is an important transportation mode in international trade and round-trip, and the activity mode of boats and ships in the ocean transportation process is known, so that the activity law and the activity change characteristics of boats and ships can be known, and information data about the aspect of ocean transportation can be obtained.
In the related art, frequent activity patterns of a ship can be mined based on manually filled information or historical track data of the ship.
However, the method for manually filling information (1) is mainly to process the manually collected ship navigation information to obtain the frequent activity pattern of the ship. The problem of low accuracy and comprehensiveness of the manually collected information causes poor reliability of the obtained frequent activity mode of the ship; (2) the method mainly comprises the steps of rasterizing each historical track data of the ship by a grid dividing method, obtaining the migration condition of the ship in each grid according to the time sequence of the historical track of the ship, and mining the frequent activity mode of the ship by using a related data mining algorithm. In the process, a reasonable grid size is difficult to determine, and errors exist in the obtained frequent activity mode of the ship.
Disclosure of Invention
In view of the above-mentioned drawbacks or deficiencies in the prior art, it is desirable to provide a frequent activity pattern excavation method, apparatus, device, and storage medium for a ship that improve accuracy of a frequent activity pattern of an excavation ship.
In a first aspect, an embodiment of the present application provides a frequent activity pattern mining method for a ship, where the method includes:
identifying a first set of berthing behaviors of the vessel based on the vessel historical trajectory data, the first set of berthing behaviors including at least one berthing behavior;
determining a parking position area corresponding to the parking behavior based on the occurrence position of each parking behavior;
and determining a frequent activity pattern of the ship based on the berthing position area, wherein the frequent activity pattern is represented by a berthing position area number subsequence with the frequency greater than or equal to a frequency threshold value, and the berthing position area number subsequence is obtained by arranging berthing position area numbers corresponding to the berthing behaviors of the ship according to a time sequence.
In a second aspect, an embodiment of the present application provides a frequent activity pattern digging device for a ship, the device including:
an identification module configured to identify a first set of berthing behaviors of the vessel based on vessel historical trajectory data, the first set of berthing behaviors including at least one berthing behavior;
a first determination module configured to determine a parking position area corresponding to each parking behavior based on an occurrence position of the parking behavior;
a second determination module configured to determine a frequent activity pattern of the vessel based on the berthing position areas, the frequent activity pattern being represented by a subsequence of berthing position area numbers having a frequency greater than or equal to a frequency threshold, the subsequence of berthing position area numbers being obtained by arranging berthing position area numbers corresponding to berthing behaviors of the vessel in a time series.
In a third aspect, an embodiment of the present application provides a computer device, where the computer device includes:
a processor;
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the frequent activity pattern mining method of the vessel of the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the method for mining frequent activity patterns of a ship according to the first aspect is implemented.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
according to the method, the device, the equipment and the storage medium for mining the frequent activity patterns of the ship, the first berthing behavior set of the ship can be identified based on the acquired historical track data of the ship, the berthing position area corresponding to each berthing behavior can be determined based on the occurrence position of the berthing behavior, and the frequent activity patterns of the ship can be mined by utilizing the position area information. Compared with the frequent activity mode mining method for the ship in the prior art, the technical scheme provided by the embodiment of the application improves the accuracy of the frequent activity mode of the ship and reduces the error of acquiring the frequent activity mode of the ship.
Based on the ship berthing behaviors, the berthing behaviors are combined, the data processing amount in the frequent activity pattern mining process of the ship is reduced, and the mining efficiency of the frequent activity pattern of the ship is improved.
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Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
fig. 1 is a flowchart illustrating a frequent activity pattern mining method for a ship according to an embodiment of the present disclosure;
FIG. 2 is a flow chart illustrating another method for frequent activity pattern mining of a vessel according to an embodiment of the present disclosure;
FIG. 3 is a flow chart illustrating a method for frequent activity pattern mining of a vessel according to an embodiment of the present disclosure;
FIG. 4 is a flow chart illustrating a further method for frequent activity pattern mining of a vessel according to an embodiment of the present disclosure;
FIG. 5 is a schematic structural diagram illustrating a frequent activity pattern digging device of a ship according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a computer system according to an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
The embodiment of the application provides a frequent activity pattern mining method for a ship, which can solve the problems of low reliability and large error when a frequent activity pattern of the ship is mined in the related technology. The frequent activity pattern mining method for the ship can be applied to mobile terminal equipment (such as a smart phone, a tablet personal computer and the like) and can also be applied to a fixed terminal (a desktop computer) or a server. As shown in fig. 1, a frequent activity pattern mining method for a ship provided by an embodiment of the present application includes:
In this step, the historical track data of the ship is used for indicating the navigation characteristics of the ship in the historical time, and for example, the track data may include information of the navigation position of the ship, the time and the navigation speed of the ship at the navigation position, and the like. The historical track data of the ship can be acquired based on various means such as an Automatic Identification System (AIS) of the ship, a radar, a Beidou, a Global Positioning System (GPS) and the like, and a first berthing behavior set of the ship can be identified based on the historical track data of the ship, wherein the first berthing behavior set comprises at least one berthing behavior. Based on historical track data of boats and ships that AIS, radar, big dipper, GPS etc. obtained, compare in the historical track data of boats and ships of artifical collection among the prior art, its accuracy is also showing the progress for prior art.
In this step, the frequent activity pattern is represented by a berth position area number subsequence having a frequency greater than or equal to a frequency threshold value, the berth position area number subsequence being obtained by arranging berth position area numbers corresponding to the berthing behavior of the ship in time series. The berthing position area is determined based on the occurrence position of the berthing behavior, and the calculation error of the frequent activity mode of the ship caused by the fact that historical track data are divided through rasterization in the prior art is effectively overcome.
In summary, according to the method for mining the frequent activity patterns of the ship provided by the embodiment of the present application, the first set of berthing behaviors of the ship may be identified based on the acquired historical track data of the ship, the berthing position area corresponding to each berthing behavior may be determined based on the occurrence position of the berthing behavior, and the frequent activity patterns of the ship may be mined by using the position area information. The reliability of the frequent activity mode of the ship is ensured through accurate historical track data, and meanwhile, the error of the frequent activity mode of the ship is reduced.
An embodiment of the present application provides a method for mining a frequent activity pattern of a ship, and as shown in fig. 2, the method for mining the frequent activity pattern of the ship provided by the embodiment of the present application includes:
a first set of berthing behaviors of a vessel is identified based on vessel historical trajectory data, step 201.
In the embodiment of the application, historical track data of the ship can be acquired based on various means such as an automatic identification system AIS (automatic identification system), a radar, a Beidou and a GPS (global positioning system), so that diversified data support can be provided for the frequent activity mode of the excavated ship, and the frequent activity mode of the excavated ship can better meet the actual situation. Meanwhile, the historical track data of the ship acquired by various means has wide sources, is not influenced by human factors and has higher reliability.
Optionally, since the historical track data of the ship with different levels and different characteristics can be acquired by using a plurality of means, the historical track data of the ship can be effectively fused by performing identification, detection, tracking, association, estimation and other processing on the plurality of historical track data of the ship, so that the accuracy of the finally acquired historical track data of the ship is improved.
In an embodiment of the present application, as shown in fig. 3, a process for identifying a first set of berthing behaviors of a vessel based on historical track data of the vessel includes:
and step 2011, ship historical track data is preprocessed.
In the embodiment of the application, abnormal data may exist in the acquired historical track data of the ship, the reliability of the mining result of the frequent activity mode of the ship is influenced by the abnormal data, and the acquired historical track data of the ship has large data volume, so that the efficiency of mining the frequent activity mode of the ship is low. Therefore, the acquired ship historical track data needs to be preprocessed. The preprocessing process includes, but is not limited to, screening the ship navigation position, the time and the navigation speed of the navigation position in the ship historical track data based on certain rules, and further sampling the screened ship historical track data to further reduce the data processing amount.
Optionally, the pretreatment process may be: and filtering the ship navigation positions with the longitude range not within the degrees of minus 180, 180 degrees or the latitude range not within the degrees of minus 90, 90 degrees. A time period can also be designated to acquire the track data in the designated time period and filter the track data not in the designated time period, and the designated time period can be determined based on actual needs. Illustratively, the specified time period may be a time from time UNIX EPOCH (00 min 00 s 00 h 01/1970). Further, historical track data of the ship can be sampled based on preset sampling intervals, and the first track in each sampling interval is reserved as the historical track data of the ship. The sampling interval may take any value between 1 minute and 24 hours, for example.
In this step, in order to further reduce the data processing amount in the frequent activity pattern mining process of the ship, it may be determined whether the sampled historical track data of the ship has a segmentation phenomenon, and the identification process includes: acquiring the navigation position of the ship and the time of the ship at the navigation position in the historical track data of the ship, judging whether the distance between the current navigation position of the ship and the next/previous navigation position is greater than a distance threshold value, and judging whether the time difference value between the time of the ship at the current navigation position and the time of the next/previous navigation position is greater than a time threshold value; when the distance between the current navigation position of the ship and the next/previous navigation position is larger than a distance threshold value, and the time difference between the current navigation position of the ship and the next/previous navigation position is larger than a time threshold value, determining that the historical track data of the ship is segmented at the current navigation position, taking the historical track data of the ship before the time corresponding to the current position of the ship as a historical track data section of the ship, and taking the historical track data of the ship after the time corresponding to the current position of the ship as another historical track data section of the ship. Furthermore, the segmented ship historical track data can be numbered according to the sequence of the segment head position time of each ship historical track data. Wherein the distance threshold and the time threshold may be determined based on actual needs. For example, the distance threshold may take a value between [ 1km, 30km ] (km: kilometer), and the time threshold may take data between [ 1h, 120h ] (h: hour), which is not limited in this embodiment of the present application.
And 2013, preprocessing the ship historical track data of each segment.
In this step, each ship historical track data segment may be preprocessed, where the preprocessing includes: and filtering abnormal track data in the historical track data section of the ship, resampling the filtered historical track data section of the ship, and determining ship track information of the historical track data section of the ship after resampling, wherein the ship track information can comprise information such as the navigation position of the ship, the time and the navigation direction at the position, and the like.
Wherein, the filtering process of the abnormal trajectory data may include: and determining a speed difference value between two adjacent ship historical track data in the ship historical track data section, and filtering two adjacent tracks with the speed difference value larger than a first speed threshold value. The determination process of the speed difference value comprises the following steps: and determining a position distance difference value and a time difference value of two adjacent ship tracks, taking the ratio of the position distance difference value to the time difference value as a speed difference value, and taking the value of the first speed threshold value between 10m/s and 30m/s (m/s: meter/second).
The process of resampling the ship historical track data segment may include: determining the starting time t1 and the ending time t2 of each preprocessed ship historical track data segment, acquiring a target time point in the ship historical track data segment, taking the target time point as an interpolation target time point, and acquiring the navigation position of the ship at the target time point by using a space linear interpolation method. The target time point is a time point selected between [ t1, t2 ] at intervals of a specific time interval, and the specific time interval can be any value between [ 1m, 1440m ] (m: min).
In this step, the berthing behavior identification can be performed on each ship historical track data segment, and the identification process comprises the following steps: and judging whether the navigation speed at the current navigation position is smaller than a second speed threshold value or not based on the navigation speed corresponding to each navigation position in the historical track data section of the ship, and if the navigation speed is smaller than the second speed threshold value, preliminarily determining that the ship is in a berthing state at the current navigation position. Wherein the second velocity threshold may take a value between [ 0.065m/s, 0.4m/s ] (m/s: m/s), exemplarily, the second velocity threshold may be 0.2 m/s; further, two navigation speeds at two previous and subsequent navigation positions adjacent to the current berthing position are determined, when the two navigation speeds are both smaller than a second speed threshold value, it is finally determined that the ship is in a berthing state at the current navigation position, and attribute information of the berthing behavior of the ship is obtained, where the attribute information may include: berthing position information and time information of berthing behavior of the vessel. Wherein the time information includes: the berth start-stop time and berth duration of the berthing activity.
A parking position area corresponding to each parking action is determined based on the location of occurrence of the parking action, step 202.
In this step, as shown in fig. 4, the process of determining the parking position area corresponding to each parking action based on the occurrence position of the parking action may include:
Since the historical track data of the ship only contains ship track information and the historical track of the ship is not associated with a wharf near the ship navigation position, the frequent activity pattern of the ship is excavated by dividing the historical track of the ship into grids, so that the stable frequent activity pattern of the ship is difficult to obtain by excavation. In the embodiment of the application, the berthing behavior of the ship can be associated with the geographic position of the wharf, a stable frequent activity mode of the ship is obtained, the berthing position of the ship is expanded to a position area formed by a larger wharf set from a certain smaller longitude and latitude position point, and when the wharf range data is missing or the berthing position of the ship has certain positioning inaccuracy, the position area of the berthing behavior of the ship is determined as much as possible based on the position area formed by the associated wharf set.
The acquisition process of the second set of parking behaviors may include:
and step S11, filtering the first parking behavior set according to the length of the parking time to obtain a filtered first parking behavior set.
In order to ensure that the frequent activity pattern of the excavated vessel is more in line with the actual situation, the berthing duration of each berthing behavior in the first berthing behavior set may be obtained, and a part of berthing behaviors with shorter berthing durations are filtered out to obtain the filtered first berthing behavior set. Wherein the parking actions of the first set of parking actions for which the parking duration is less than or equal to a parking duration threshold, which may be determined based on actual experience, may be filtered. Alternatively, the berthing duration threshold may be a number between [ 0.5h,12h ] (h: hours). Illustratively, the parking duration threshold is 1 h.
Step S12, determining whether there is an associated set of quays for each of the filtered first set of parking actions based on the location of occurrence of the parking action.
For the filtered first set of parking behaviors, it may be determined whether there is a set of docks associated with each parking behavior based on the location of occurrence of the each parking behavior, and the process may include: acquiring the occurrence position of each berthing behavior of a ship, and acquiring at least one wharf position and a corresponding wharf name; and for each berthing behavior, determining at least one distance difference between the position of the at least one wharf and the position of the berthing behavior, judging whether the at least one distance difference is smaller than or equal to a distance threshold, if the distance difference is smaller than or equal to the distance threshold, establishing association between the at least one wharf and the berthing behavior, and determining the at least one wharf as a wharf set associated with the berthing behavior, wherein the wharf set comprises the wharf position of the at least one wharf and a corresponding wharf name. Wherein the dock location and the corresponding dock name can be obtained based on existing dock profile information, and the distance threshold can be a value between [ 0km, 20km ] (km: km), for example, the distance threshold is 1 km.
And step S13, filtering the parking behaviors without the associated wharf set to obtain a second parking behavior set.
In this step, the filtered first set of parking behaviors for which there is no associated set of wharfs may be filtered, and at least one parking behavior for which there is an associated set of wharfs may be determined as a second set of parking behaviors, each parking behavior in the second set of parking behaviors having a set of wharfs associated therewith.
Optionally, for a second set of parking behaviors, it may be further determined whether the occurrence position of each parking behavior in the second set of parking behaviors is in the position area of any one of the set of wharfs associated with the parking behavior, and if so, it is determined that the any one of the wharfs is the core wharf of the set of wharfs. Wherein the core terminal is a berthing terminal of a ship.
Further, in the embodiment of the present application, in order to reduce the data processing amount in the frequent activity pattern mining process of the ship, based on the obtained sequence of the berthing behaviors and the wharf set associated with the berthing behaviors, two adjacent berthing behaviors that meet the condition may be merged. The merging process of the eligible two-adjacent berthing behaviors may include: determining whether two berthing behaviors of adjacent times in the second set of berthing behaviors are associated with the same set of terminals; if so, merging the two parking behaviors of the adjacent time into the same parking behavior to obtain a third parking behavior set. The set of wharves associated with each mooring action in the third set of mooring actions is: a union of the sets of docks associated with the two adjacent berthing behaviors corresponding to the each berthing behavior. Further, attribute information of the combined parking behaviors can be obtained, and in the attribute information, the position information is an average value of longitude and latitude of positions where two adjacent parking behaviors occur. The time information includes: the starting time of the earlier-occurring mooring behavior and the ending time of the later-occurring mooring behavior in the two mooring behaviors at adjacent times are respectively the starting and ending times of the combined mooring behavior, and the mooring duration is the sum of the durations of the two mooring behaviors.
Optionally, if there is a core wharf in the wharf set associated with at least one of the two adjacent ship berthing behaviors, the union of the core wharf sets is used as the core wharf of the merged berthing behavior.
For example, assuming that there is a case in which two berthing behaviors of adjacent times in the second berthing behavior set are associated with the same quay set, the two berthing behaviors of the adjacent times are merged into one berthing behavior. Assuming that the two-berth behavior-related set of docks of adjacent times includes docks a1, a2, and A3, the merged berth behavior-related set of docks also includes docks a1, a2, and A3. Further, if the core wharf of the wharf set associated with one of the two berthing behaviors of the adjacent time is a2, and the core wharf of the wharf set associated with the other berthing behavior is a1, the core wharfs of the wharf set associated with the merged berthing behaviors are the wharfs a1 and a 2.
In an embodiment of the present application, to further reduce the complexity of the active mode mining process of the ship, the set of docks associated with each of the third set of berthing behaviors may be grouped, resulting in at least one dock group, the docks of the group of docks being connected to each other.
In this step, the wharf set may be grouped by using a graph theory method, and the process includes:
and step S21, constructing a connection relation graph corresponding to the plurality of wharf sets.
In this step, each node in the connection relation graph corresponds to a wharf in the set of wharfs associated with each mooring action in the third set of mooring actions, and each edge in the connection relation graph is used for connecting any two wharfs associated with each mooring action. The process of constructing the connection relationship graph corresponding to the plurality of wharf sets includes: and adding the wharves in the wharf set associated with each mooring behavior in the third mooring behavior set into the connection relation graph, wherein each wharf is a vertex of the connection relation graph, and a connecting line of any two wharves is an edge of the connection relation graph. In this process, if the same vertex or edge already exists in the connection relationship graph, the dock is not repeatedly added. Alternatively, the connection relation graph may be a graph G.
And step S22, determining connected subgraphs with connectivity in the connection relation graph, wherein each connected subgraph comprises a wharf group.
In this step, the process of determining a connected subgraph with connectivity in the connection relationship graph includes: starting from any vertex, searching other vertices which can form edges of the connection relation graph with the vertex in the connection relation graph, and determining the vertex and the other vertices as connected subgraphs with connectivity in the connection relation graph, wherein wharfs corresponding to the vertices contained in the connected subgraphs form a wharf group. And processing the residual vertexes in the connection relation graph by using the method, and determining at least one wharf group in the connection relation graph.
And step S23, assigning the number associated with the wharf grouping.
In this step, a corresponding number may be assigned to the determined at least one wharf group, and a one-to-one correspondence between the at least one wharf group and the corresponding number may be established.
In this step, the process of determining the parking position area corresponding to the parking action may be: obtaining a set of terminals associated with each of the third set of berthing activities, for any berthing activity, obtaining all terminals in the set of terminals associated with the any berthing activity, determining a terminal group including all terminals in at least one terminal group, and determining the terminal group as the terminal group corresponding to the any berthing activity. Determining a wharf group corresponding to each berthing behavior based on the method, and further determining a berthing position area corresponding to each berthing behavior based on the wharf group, wherein the berthing position area is a position area formed by intercommunicating wharfs in the wharf group corresponding to each berthing behavior.
Alternatively, a representative quay of each quay group, which is a quay in the quay group at which a ship frequently stops, may be excavated based on ship-stopping behavior, and a quay at which a ship frequently stops in the quay group may be determined based on the representative quay. For any wharf group, the mining manner of the representative wharf of any wharf group may be: counting a first number of berthing times and at least one second number of berthing times, wherein the first number of berthing times is the number of berthing behaviors of which the wharf group corresponding to the berthing behaviors is any one, the second number of berthing times is the number of berthing behaviors of which the occurrence positions are any one core wharf in any one wharf group, and determining at least one ratio between the at least one second number of berthing times and the first number of berthing times; and determining the ratio of the at least one ratio which is larger than a first threshold value, and determining the core wharf corresponding to the ratio as the representative wharf corresponding to any wharf group. Wherein the first threshold may be a value between [ 1/8, 2/3 ], for example, the first threshold is 1/3.
Optionally, in this embodiment of the present application, in order to further reduce the data processing amount during the frequent activity pattern mining process of the ship, the eligible berthing behaviors may be merged based on the precedence order of the berthing behaviors and the dock group associated with the berthing behaviors. The merging process of the mooring behavior may include: determining whether there is an identical quay group for two berthing behaviors of adjacent times in the third set of berthing behaviors; if so, combining the two parking behaviors of adjacent times into the same parking behavior to obtain a fourth set of parking behaviors. The quays associated with each berthing activity in the fourth set of berthing activities are grouped as: a union of the quay groups associated with the two adjacent berthing behaviors corresponding to the each berthing behavior. The method for obtaining attribute information may refer to the method for obtaining attribute information of each parking behavior in a third parking behavior set after the second parking behavior set is merged to obtain the third parking behavior set, which is not described in detail in this embodiment of the present application.
In this step, the frequent activity pattern of the ship is represented by a subsequence of berth position zone numbers having a frequency greater than or equal to a frequency threshold, the subsequence of berth position zone numbers being obtained by arranging berth position zone numbers corresponding to berthing behaviors of the ship in time series.
The process of determining the frequent activity pattern of the ship may include: acquiring the occurrence time of each parking behavior and a parking position area number corresponding to each parking behavior, the parking position area number being the number of the wharf group corresponding to the parking behavior, which may be a parking behavior in the fourth set of parking behaviors; determining a berthing position area number sequence corresponding to the berthing behaviors according to the occurrence time sequence of the berthing behaviors, dividing the berthing position area number sequence based on at least two adjacent occurrence times to obtain a berthing position area number subsequence set, counting the frequency of each berthing position area number subsequence in the berthing position area number subsequence set, and determining at least one berthing position area number subsequence with the frequency greater than or equal to a frequency threshold value as a frequent activity mode of the ship. Wherein, the frequency threshold value can be a numerical value between [ 3,10 ].
Further, attribute information of the frequent activity pattern of the vessel may be determined, which may be a periodicity of the frequent activity pattern of the vessel referring to a characteristic of the frequent activity pattern of the vessel repeatedly appearing in the set of berthing position area numbering subsequences or a continuity of the same subsequences appearing continuously in the set of berthing position area numbering subsequences.
For example, it is assumed that the acquired mooring position area number sequence is R based on the occurrence time of each mooring action of the ship W and the mooring position area number corresponding to each mooring action1,R2,R1,R8,R7……R8,R2,R4(ii) a Dividing the sequence of berthing position area numbers based on two adjacent occurrence times to obtain a subsequence of berthing position area numbers [ R [ ]1,R2】，【R2,R1】，【R1,R8】……【R2,R4H ]; dividing the sequence of berthing position area numbers based on three adjacent occurrence times to obtain a subsequence of berthing position area numbers [ R [ ]1,R2,R1】，【R2,R1,R8】，【R1,R8,R7】……【R8,R2,R4Until the sequence of the parking position area numbers is divided based on fifteen adjacent occurrence times. Extracting a plurality of parking position area number subsequences and parking the plurality of parking positionsAnd combining the area number subsequences to obtain a parking position area number subsequence set, and counting the occurrence frequency of each parking position area number subsequence. Assuming that the frequency threshold is 5, the parking position zone number subsequence in the set of parking position zone number subsequences is [ R ]1,R2,R1And [ R ]2,R1The frequencies are 8 and 7 respectively, then [ R ]1,R2,R1And [ R ]2,R1Is determined as a frequent activity pattern of the vessel.
Further, assume that in the set of berth position area number subsequences, the frequent activity pattern of the vessel W [ R ] is every 2 berth position area number subsequences1,R2,R1Record the periodicity of the ship W when the ship W appears once; assume that in the set of berth position area number subsequences, a berth position area number subsequence [ R ] of the ship W1,R8The successive repetitions are recorded as the continuity of the ship W.
It should be noted that, the above steps 201 to 203 are frequent activity pattern mining methods provided for one ship, and may be based on the same method to mine frequent activity patterns of a plurality of ships and to establish profile information of the frequent activity patterns of the plurality of ships.
In summary, according to the method for mining the frequent activity patterns of the ship provided by the embodiment of the present application, the first set of berthing behaviors of the ship may be identified based on the acquired historical track data of the ship, the berthing position area corresponding to each berthing behavior may be determined based on the occurrence position of the berthing behavior, and the frequent activity patterns of the ship may be mined by using the position area information. The reliability of the frequent activity mode of the ship is improved, and the error of the frequent activity mode of the ship is reduced.
Based on the ship berthing behaviors, the continuous twice berthing behaviors meeting the conditions are combined, the data processing amount in the frequent activity pattern mining process of the ship is reduced, and the mining efficiency of the frequent activity pattern of the ship is improved.
The embodiment of the present application provides a frequently-active mode excavating device for a ship, as shown in fig. 5, the device 30 includes:
an identification module 301 configured to identify a first set of berthing behaviors of a vessel based on vessel historical trajectory data, the first set of berthing behaviors including at least one berthing behavior;
a first determination module 302 configured to determine a parking position area corresponding to each parking action based on an occurrence position of the parking action;
a second determining module 303 configured to determine a frequent activity pattern of the vessel based on the berthing position areas, the frequent activity pattern being represented by a subsequence of berthing position area numbers with a frequency greater than or equal to a frequency threshold, the subsequence of berthing position area numbers being arranged in time series of berthing position area numbers corresponding to berthing behaviors of the vessel.
Optionally, the first determining module 302 is configured to:
filtering the first berthing behavior set to obtain a second berthing behavior set with an associated wharf set, wherein each berthing behavior in the second berthing behavior set has the wharf set associated with the berthing behavior;
grouping and processing a plurality of wharf sets to obtain at least one wharf group, wherein the wharfs in the wharf group are communicated with each other;
determining a parking position area corresponding to the parking behaviors, wherein the parking position area is a position area formed by communicating wharves in the wharf group corresponding to each parking behavior.
Optionally, the first determining module 302 is configured to:
filtering the first parking behavior set according to the length of the parking time to obtain a filtered first parking behavior set;
determining whether an associated set of wharfs exists for each of the filtered first set of berthing behaviors based on a location of occurrence of the berthing behavior;
and filtering the parking behaviors of which the associated wharf set does not exist to obtain a second parking behavior set.
Optionally, the first determining module 302 is configured to:
constructing a connection relation graph corresponding to a plurality of wharf sets, wherein each node in the connection relation graph corresponds to a wharf in the wharf set associated with each mooring behavior in the second mooring behavior set, and each edge in the connection relation graph is used for connecting any two wharfs associated with each mooring behavior;
determining connected subgraphs with connectivity in the connection relation graph, wherein the wharf contained in each connected subgraph is a wharf group;
the dock group is assigned a number associated therewith.
Optionally, the second determining module 303 is configured to:
acquiring the occurrence time of each parking behavior and the parking position area number corresponding to each parking behavior;
determining a sequence of berthing position zone numbers corresponding to berthing behaviors in the order of occurrence time of the berthing behaviors;
dividing the parking position area number sequence based on at least two adjacent occurrence times to obtain a parking position area number subsequence set;
counting the frequency of each parking position area number subsequence in the parking position area number subsequence set;
determining at least one berth position area number subsequence having a frequency greater than or equal to a frequency threshold as a frequent activity pattern of the vessel.
Optionally, the first determining module 301 is further configured to:
determining whether the occurrence of each of the set of berthing activities is in the location area of any of the set of terminals associated with the berthing activity;
and if so, determining that any wharf is a core wharf of the wharf set.
Optionally, for any quay group, the first determining module 301 is further configured to:
counting a first berthing time and at least one second berthing time, wherein the first berthing time is the number of berthing behaviors of any one wharf group of wharf groups corresponding to the berthing behaviors, and the second berthing time is the number of berthing behaviors of any one core wharf in any one wharf group of the berthing behaviors at the occurrence positions of the berthing behaviors;
determining at least one ratio between at least one second number of berthings and the first number of berthings;
determining a ratio of the at least one ratio that is greater than a first threshold;
and determining the core wharf corresponding to the ratio as the representative wharf corresponding to any wharf group.
Optionally, the first determining module 301 is further configured to:
determining whether two berthing behaviors of adjacent times in the second set of berthing behaviors are associated with the same set of terminals;
if so, merging the two parking behaviors of the adjacent time into the same parking behavior to obtain a third parking behavior set.
Optionally, the first determining module 301 is further configured to:
determining whether there is an identical quay group for two berthing behaviors of adjacent times in the third set of berthing behaviors;
if so, combining the two parking behaviors of adjacent times into the same parking behavior to obtain a fourth set of parking behaviors.
In summary, according to the device for mining the frequent activity patterns of the ship provided by the embodiment of the present application, the identification module may identify the first set of berthing behaviors of the ship based on the acquired historical track data of the ship, the first determination module may determine a berthing position area corresponding to each berthing behavior based on the occurrence position of the berthing behavior, and the second determination module mines the frequent activity patterns of the ship by using the position area information. The reliability of the frequent activity mode of the ship is improved, and the error of the frequent activity mode of the ship is reduced.
The first determining module can also combine two continuous berthing behaviors meeting the conditions based on the ship berthing behaviors, reduce the data processing amount in the frequent activity pattern mining process of the ship and improve the mining efficiency of the frequent activity pattern of the ship.
Fig. 6 is a diagram illustrating a computer system according to an exemplary embodiment, which includes a Central Processing Unit (CPU)401 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage section into a Random Access Memory (RAM) 403. In the RAM403, various programs and data necessary for system operation are also stored. The CPU401, ROM402, and RAM403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
The following components are connected to the I/O interface 405: an input section 406 including a keyboard, a mouse, and the like; an output section including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 408 including a hard disk and the like; and a communication section 409 including a network interface card such as a LAN card, a modem, or the like. The communication section 409 performs communication processing via a network such as the internet. Drivers are also connected to the I/O interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 410 as necessary, so that a computer program read out therefrom is mounted into the storage section 408 as necessary.
In particular, the processes described above in fig. 1-4 may be implemented as computer software programs, according to embodiments of the present application. For example, various embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication section, and/or installed from a removable medium. The above-described functions defined in the system of the present application are executed when the computer program is executed by a Central Processing Unit (CPU) 401.
It should be noted that the computer readable medium shown in the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods, apparatus, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present application may be implemented by software, or may be implemented by hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves. The described units or modules may also be provided in a processor, and may be described as: a processor includes an identification module, a first determination module, and a second determination module. Where the names of these units or modules do not in some cases constitute a limitation of the units or modules themselves, the first acquisition module may also be described as "identification module for identifying a first set of berthing behaviors of a vessel based on vessel historical trajectory data", for example.
As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to implement the frequent activity pattern mining method of a ship as described in the above embodiments.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by a person skilled in the art that the scope of the invention as referred to in the present application is not limited to the embodiments with a specific combination of the above-mentioned features, but also covers other embodiments with any combination of the above-mentioned features or their equivalents without departing from the inventive concept. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (20)
1. A method of frequent activity pattern mining of a vessel, the method comprising:
identifying a first set of berthing behaviors of the vessel based on historical track data of the vessel, the first set of berthing behaviors including at least one berthing behavior;
determining a parking position area corresponding to each of the parking behaviors based on the occurrence position of the parking behavior;
determining a frequent activity pattern of the ship based on the berthing position area, wherein the frequent activity pattern is represented by a berthing position area number subsequence with frequency greater than or equal to a frequency threshold value, and the berthing position area number subsequence is obtained by arranging berthing position area numbers corresponding to the berthing behaviors of the ship according to a time sequence.
2. The method of claim 1, wherein determining a parking location area corresponding to each of the parking activities based on the location of occurrence of the parking activity comprises:
filtering the first berthing behavior set to obtain a second berthing behavior set with an associated wharf set, wherein each berthing behavior in the second berthing behavior set has the wharf set associated with the berthing behavior;
grouping and processing a plurality of wharf sets to obtain at least one wharf group, wherein the wharfs in the wharf group are communicated with each other;
determining a parking position area corresponding to the parking behaviors, wherein the parking position area is a position area formed by communicating wharves in the wharf group corresponding to each parking behavior.
3. The method of claim 2, wherein the filtering the first set of parking behaviors resulting in a second set of parking behaviors having an associated set of terminals comprises:
filtering the first parking behavior set according to the length of the parking time to obtain a filtered first parking behavior set;
determining whether an associated set of docks exists for each of the berthing behaviors in the filtered first set of berthing behaviors based on a location of occurrence of the berthing behavior;
and filtering the parking behaviors of which no associated wharf set exists to obtain the second parking behavior set.
4. The method of claim 2, wherein said packet processing a plurality of said quay sets resulting in at least one quay packet, comprises:
constructing a connection relation graph corresponding to the plurality of wharf sets, wherein each node in the connection relation graph corresponds to a wharf in the wharf set associated with each mooring behavior in the second mooring behavior set, and each edge in the connection relation graph is used for connecting any two wharfs associated with each mooring behavior;
determining connected subgraphs with connectivity in the connection relation graph, wherein the wharf contained in each connected subgraph is a wharf group;
assigning the dock component a number associated therewith.
5. The method of any one of claims 1 to 4, wherein determining the frequent activity pattern of the vessel based on the berthing location area comprises:
acquiring the occurrence time of each parking behavior and the parking position area number corresponding to each parking behavior;
determining a sequence of berthing position zone numbers corresponding to the berthing behaviors in chronological order of occurrence of the berthing behaviors;
dividing the parking position area number sequence based on at least two adjacent occurrence times to obtain a parking position area number subsequence set;
counting the frequency of each of the set of berthing position zone number subsequences;
determining at least one of the berth location area number subsequences having a frequency greater than or equal to a frequency threshold as a frequent activity pattern for the vessel.
6. The method of claim 2, wherein after obtaining a second set of berthing behaviors for which there is an associated quay, the method further comprises:
determining whether the occurrence of each of the second set of berthing activities is in the location area of any of the set of terminals associated with the berthing activity;
and if the wharf is located, determining that the any wharf is a core wharf of the wharf set.
7. The method of claim 6, wherein after determining the berthing position area corresponding to the berthing behavior, for any of the quay groupings, the method further comprises:
counting a first berthing time and at least one second berthing time, wherein the first berthing time is the number of berthing behaviors of a wharf group corresponding to the berthing behaviors into any one wharf group, and the second berthing time is the number of berthing behaviors of any core wharf in any one wharf group at the occurrence position of the berthing behaviors;
determining at least one ratio between said at least one second number of berthings and said first number of berthings;
determining a ratio of the at least one ratio that is greater than a first threshold;
and determining the core wharf corresponding to the ratio as the representative wharf corresponding to any wharf group.
8. The method of claim 2, wherein after obtaining a second set of berthing behaviors for which there is a set of associated terminals, the method further comprises:
determining whether two berthing behaviors of adjacent times in the second set of berthing behaviors are associated with the same set of terminals;
if so, merging the two parking behaviors of the adjacent time into the same parking behavior to obtain a third parking behavior set.
9. The method of claim 8, wherein after determining a parking location area corresponding to the parking action, the method further comprises:
determining whether there is an identical quay group for two berthing behaviors of adjacent times in the third set of berthing behaviors;
if so, combining the two parking behaviors of adjacent times into the same parking behavior to obtain a fourth set of parking behaviors.
10. A frequent activity pattern digging device of a ship, characterized in that the device comprises:
an identification module configured to identify a first set of berthing behaviors of a vessel based on vessel historical trajectory data, the first set of berthing behaviors including at least one berthing behavior;
a first determination module configured to determine a parking position area corresponding to each of the parking behaviors based on an occurrence position of the parking behavior;
a second determination module configured to determine a frequent activity pattern of the vessel based on the berthing position areas, the frequent activity pattern being represented by a subsequence of berthing position area numbers having a frequency greater than or equal to a frequency threshold, the subsequence of berthing position area numbers being obtained by arranging berthing position area numbers corresponding to the berthing behavior of the vessel in a time series.
11. The apparatus of claim 10, wherein the first determining module is configured to:
filtering the first berthing behavior set to obtain a second berthing behavior set with an associated wharf set, wherein each berthing behavior in the second berthing behavior set has the wharf set associated with the berthing behavior;
grouping and processing a plurality of wharf sets to obtain at least one wharf group, wherein the wharfs in the wharf group are communicated with each other;
determining a parking position area corresponding to the parking behaviors, wherein the parking position area is a position area formed by communicating wharves in the wharf group corresponding to each parking behavior.
12. The apparatus of claim 11, wherein the first determining module is configured to:
filtering the first parking behavior set according to the length of the parking time to obtain a filtered first parking behavior set;
determining whether an associated set of docks exists for each of the berthing behaviors in the filtered first set of berthing behaviors based on a location of occurrence of the berthing behavior;
and filtering the parking behaviors of which no associated wharf set exists to obtain the second parking behavior set.
13. The apparatus of claim 11, wherein the first determining module is configured to:
constructing a connection relation graph corresponding to the plurality of wharf sets, wherein each node in the connection relation graph corresponds to a wharf in the wharf set associated with each mooring behavior in the second mooring behavior set, and each edge in the connection relation graph is used for connecting any two wharfs associated with each mooring behavior;
determining connected subgraphs with connectivity in the connection relation graph, wherein the wharf contained in each connected subgraph is a wharf group;
assigning the dock component a number associated therewith.
14. The apparatus of any of claims 10 to 13, wherein the second determining module is configured to:
acquiring the occurrence time of each parking behavior and the parking position area number corresponding to each parking behavior;
determining a sequence of berthing position zone numbers corresponding to the berthing behaviors in chronological order of occurrence of the berthing behaviors;
dividing the parking position area number sequence based on at least two adjacent occurrence times to obtain a parking position area number subsequence set;
counting the frequency of each of the set of berthing position zone number subsequences;
determining at least one of the berth location area number subsequences having a frequency greater than or equal to a frequency threshold as a frequent activity pattern for the vessel.
15. The apparatus of claim 11, wherein the first determining module is further configured to:
determining whether the occurrence of each of the second set of berthing activities is in the location area of any of the set of terminals associated with the berthing activity;
and if the wharf is located, determining that the any wharf is a core wharf of the wharf set.
16. The apparatus of claim 15, wherein for any quay group, the first determination module is further configured to:
counting a first berthing time and at least one second berthing time, wherein the first berthing time is the number of berthing behaviors of a wharf group corresponding to the berthing behaviors into any one wharf group, and the second berthing time is the number of berthing behaviors of any core wharf in any one wharf group at the occurrence position of the berthing behaviors;
determining at least one ratio between said at least one second number of berthings and said first number of berthings;
determining a ratio of the at least one ratio that is greater than a first threshold;
and determining the core wharf corresponding to the ratio as the representative wharf corresponding to any wharf group.
17. The apparatus of claim 11, wherein the first determining module is further configured to:
determining whether two berthing behaviors of adjacent times in the second set of berthing behaviors are associated with the same set of terminals;
if so, merging the two parking behaviors of the adjacent time into the same parking behavior to obtain a third parking behavior set.
18. The apparatus of claim 17, wherein the first determining module is further configured to:
determining whether there is an identical quay group for two berthing behaviors of adjacent times in the third set of berthing behaviors;
if so, combining the two parking behaviors of adjacent times into the same parking behavior to obtain a fourth set of parking behaviors.
19. A computer device, characterized in that the computer device comprises:
a processor;
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the frequent activity pattern mining method of a vessel of any of claims 1 to 9.
20. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out a frequent activity pattern mining method of a vessel according to any one of claims 1 to 9.
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| CN109961169A (en) * | 2018-09-30 | 2019-07-02 | 大连永航科技有限公司 | Ship entry Optimal Scheduling based on tardiness cost management information |
| CN109740494A (en) * | 2018-12-27 | 2019-05-10 | 中国船舶重工集团公司第七研究院 | A kind of harbour recognition methods based on ship track big data |
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