CN112016730B - Port berth loading and unloading efficiency mining method, device, equipment and storage medium - Google Patents
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Abstract
The application discloses a port berth loading and unloading efficiency mining method, device, equipment and storage medium. The method comprises the following steps: acquiring at least one piece of ship basic information, wherein each piece of ship basic information comprises historical ship draft data, a ship model, maximum draft data and maximum load data corresponding to the ship model; inputting at least one historical ship draft data into a pre-constructed cargo estimation model to determine a maximum cargo handling capacity of the ship berthed at the port berth, the cargo handling capacity being a cargo capacity loaded or unloaded by the ship berthed at the port berth; acquiring a minimum ship berthing time corresponding to the maximum cargo handling capacity; and calculating the loading and unloading efficiency of the port berth based on the maximum cargo loading and unloading amount and the minimum ship berthing time. The difficulty in mining the loading and unloading efficiency of the port berths is reduced, and the accuracy of the information of the loading and unloading efficiency of the port berths is improved.
Description
Technical Field
The present application relates generally to the field of big data analysis, and in particular, to a method, apparatus, device, and storage medium for port berth loading and unloading efficiency mining.
Background
With the recent trend of international trade, ocean transportation is an important way of transporting goods, and plays an increasing role in international trade.
In marine transport transportation networks, ports are important hub nodes, and in port operation systems, berths are the most basic units of work for ports. The loading and unloading efficiency of the port berth reflects the level of the infrastructure construction, resource allocation and operation management of the port from the side, so that the acquisition of the loading and unloading efficiency information of the port berth has important significance.
However, since the port berth loading and unloading efficiency information belongs to private information of the port, the port berth loading and unloading efficiency information is not generally disclosed externally, so that the difficulty of acquiring the port berth loading and unloading efficiency is high; or even if the port operator is willing to disclose the port berth loading and unloading efficiency, the port berth loading and unloading efficiency is difficult to effectively mine because the information is mainly collected manually and the information sources are limited.
Disclosure of Invention
In view of the foregoing drawbacks or shortcomings in the prior art, it is desirable to provide a method, apparatus, device and storage medium for mining port berth loading efficiency that reduces the difficulty of mining port berth loading efficiency and improves the accuracy of the information of the mining port berth loading efficiency.
In a first aspect, an embodiment of the present application provides a method for mining efficiency of loading and unloading at a port berth, where the method includes:
acquiring at least one piece of ship basic information, wherein each piece of ship basic information comprises historical ship draft data, a ship model, maximum draft data and maximum load data corresponding to the ship model;
inputting at least one historical ship draft data into a pre-constructed cargo estimation model to determine a maximum cargo handling capacity of the ship berthed at the port berth, the cargo handling capacity being a cargo capacity loaded or unloaded by the ship berthed at the port berth;
acquiring a minimum ship berthing time corresponding to the maximum cargo handling capacity;
and calculating the loading and unloading efficiency of the port berth based on the maximum cargo loading and unloading amount and the minimum ship berthing time.
In a second aspect, embodiments of the present application provide a port berth loading and unloading efficiency excavating device, the device comprising:
a first acquisition module configured to acquire at least one ship base information, each ship base information including historical ship draft data, a ship model, maximum draft data corresponding to the ship model, and maximum load data;
a first determination module configured to input at least one historical ship draft data to a pre-constructed cargo estimation model to determine a maximum cargo handling capacity of the berthing ship at the port berth, the cargo handling capacity being an amount of cargo loaded or unloaded by the berthing ship at the port berth;
A second acquisition module configured to acquire a minimum ship berthing time corresponding to the maximum cargo handling capacity;
a calculation module configured to calculate a loading and unloading efficiency of the port berth based on the maximum cargo loading and unloading amount and the minimum ship berthing time.
In a third aspect, embodiments of the present application provide a computer device, comprising:
a processor;
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the port berth handling efficiency mining method of the first aspect.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium having instructions stored therein that, when executed on a processing component, cause the processing component to perform a port berth handling efficiency mining method as in the first aspect.
The technical scheme provided by the embodiment of the application can comprise the following beneficial effects:
the method, the device, the equipment and the storage medium for mining the loading and unloading efficiency of the port berth can determine the maximum cargo loading and unloading amount of the ship berthed at the port berth based on at least one ship draft data in the basic information of at least one ship and a pre-constructed cargo loading estimation model, acquire the minimum ship berthing time of the ship corresponding to the maximum cargo loading and unloading amount, calculate the loading and unloading efficiency of the port berth based on the maximum cargo loading and unloading amount and the minimum ship berthing time, and realize mining the loading and unloading efficiency information of the port berth.
And determining the geographical range of the port berth based on the acquired global ship track data and the port range data, establishing a corresponding relation among the port berth, the geographical range and the loading and unloading efficiency, and improving the informatization level of the port berth.
And the port berth loading and unloading efficiency can be updated after the preset time, so that the accuracy of the acquired port berth loading and unloading efficiency is improved.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the following drawings, in which:
FIG. 1 is a flow chart of a method for mining port berth handling efficiency, according to an embodiment of the present disclosure;
FIG. 2 is a flow chart of another method of harbour berth handling efficiency mining shown in an embodiment of the present application;
fig. 3 is a schematic structural diagram of a port berth loading and unloading efficiency excavating device according to an embodiment of the present application;
FIG. 4 is a schematic structural view of another berth handling efficiency mining device according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a computer system according to an embodiment of the present application.
Detailed Description
The present application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be noted that, for convenience of description, only the portions related to the invention are shown in the drawings.
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
The port is used as an important hub node in the ocean transportation network, and the operation capability of the port operation system is an important index for determining the core competitiveness of the port. Knowing the operation capability of the port operation system, for the port itself, the advantages and disadvantages of the port itself can be clarified, the port reinforcement infrastructure and informatization construction are promoted, the operation and service level is improved, and more potential customers are attracted; for the freight agent, the freight agent can conveniently select a proper port for consignment, and the freight cost is reduced.
In a port operation system, an important index for measuring the port operation capability is loading and unloading efficiency of a port berth, the berth refers to berthing positions of ships in the port, the berthing efficiency refers to the most basic working unit of the port, and the port berthing efficiency refers to the ratio between the maximum cargo loading and unloading amount of a ship carried by a berth and the minimum ship berthing time of the ship corresponding to the maximum cargo loading and unloading amount.
In the related art, loading and unloading efficiency information of the port berths can be collected manually, but the loading and unloading efficiency of the port berths collected manually is derived from data reported by each port, and the loading and unloading efficiency information of the port berths belongs to private information of the port, so that all the loading and unloading efficiency information of the port berths cannot be collected; and since port berth loading and unloading efficiency is estimated based on port infrastructure, there is no unified standard data. Therefore, when the statistical analysis is performed on the manually collected port berth loading and unloading efficiency, the data source and the data quality are difficult to ensure, so that the port berth loading and unloading efficiency is difficult to effectively mine.
The embodiment of the application provides a port berth loading and unloading efficiency mining method, which can solve the problem of difficulty in acquiring port berth loading and unloading efficiency information. As shown in fig. 1, the method includes:
and 101, acquiring at least one piece of ship basic information.
In this step, the ship basic information includes historical ship draft data and ship profile information, which may include a ship model, maximum draft data and maximum load data corresponding to the ship model. Wherein the historical ship draft data represents a set of draft data when the ship enters the berth and draft data when the ship leaves the berth in each berthing action of the ship at the port berth within a specific period of time, and the draft data when the ship enters the port berth is typically: in the course of a ship entering a port berth, the draft data in the latter half course is concentrated with higher frequency draft data; the draft data when the vessel leaves the port berth is typically: in the course of a ship leaving a port berth, the draft data in the latter half course is concentrated with higher frequency draft data; the maximum draft data and the maximum load data corresponding to the ship model are the maximum draft data and the maximum load data of different ships in the theoretical state.
Step 102, inputting at least one historical ship draft data into a pre-constructed cargo estimation model to determine a maximum cargo handling capacity for berthing the ship at the port berth.
In this step, the cargo estimation model is used to represent the relationship between the draft data of the vessel and the actual cargo capacity of the vessel, and the actual cargo capacity of the vessel may be determined based on the cargo estimation model and the acquired historical vessel draft data.
Step 103, obtaining the minimum ship berthing time corresponding to the maximum cargo handling capacity.
In this step, the minimum ship berthing time is the difference between the time the ship enters the port berth and the time the ship exits the port berth.
And 104, calculating the loading and unloading efficiency of the port berth based on the maximum cargo loading and unloading amount and the minimum ship berthing time.
In summary, according to the method for mining loading and unloading efficiency of port berths provided in the embodiments of the present application, the maximum cargo loading and unloading amount of a ship berthed at a port berth can be determined based on at least one ship draft data in at least one ship basic information and a pre-constructed cargo estimation model, the minimum ship berthing time corresponding to the maximum cargo loading and unloading amount is obtained, and the loading and unloading efficiency of the port berth is calculated based on the maximum cargo loading and unloading amount and the minimum ship berthing time. The method realizes the excavation of port berth loading and unloading efficiency information, and improves the informatization level of ports.
The port berth loading and unloading efficiency mining method provided by the embodiment of the application can be applied to mobile terminal equipment (such as a smart phone, a tablet personal computer and the like) and also can be applied to a fixed terminal (a desktop computer) or a server.
As shown in fig. 2, the method for mining port berth loading and unloading efficiency provided in the embodiment of the present application includes:
step 201, identifying the berthing behavior of the ship at the port according to the acquired global ship track data and port range data.
In the embodiment of the application, the ship track data can be acquired based on various means such as an automatic ship identification system (English: automatic Identification System, abbreviated as AIS), a radar, a Beidou, a global positioning system (English: global Positioning System, abbreviated as GPS) and the like, and the ship track data with different levels and different characteristics can be acquired by utilizing various means, and the ship track data can be effectively fused through the processes such as identification, detection, tracking, association, estimation and the like of the ship track data, so that the accuracy of the finally acquired ship track data is improved. Meanwhile, the port range data can be mined based on the ship track data and port center point position data.
Further, determining a vessel berthing behavior in the port based on the acquired global vessel trajectory data and port range data, comprising: acquiring global ship track data and preprocessing the global ship track data; carrying out behavior identification of the ship on-port or off-port according to the preprocessed global ship track data and port range data; and carrying out port parking behavior identification on the data at the port. Wherein the global ship track data preprocessing includes, but is not limited to, at least one of: data fusion, data correction, data filtering and data sorting.
And 202, determining the geographical range of the port berth according to berthing behaviors and ship attribute information.
In the embodiment of the present application, the process of determining the geographical range of the port berth may be implemented by the following steps 2021 to 2022.
Step 2021, performing cluster analysis on the berth data formed in the berthing behavior of the harbor to obtain the berthing behavior point of the harbor.
In the step, a clustering algorithm can be adopted to perform clustering analysis on berthing data formed by berthing behaviors in the harbor, so as to obtain berthing behavior points of the ship in the harbor. The berthing behavior refers to the behavior of a ship entering a port berth and leaving the port berth. Alternatively, the clustering algorithm may be a Density-based clustering algorithm (English: density-Based Spatial Clustering of Applications with Noise, abbreviated as DBSCAN). Specifically, the DBSCAN algorithm may be used to cluster the berth data to obtain a plurality of classification results, where the plurality of classification results include a core point set, an edge point set, and a noise point set, where the core point set is a berthing behavior point in the harbor. Wherein, the radius threshold value of the DBSCAN algorithm is 20 meters, and the density threshold value is 50.
And 2022, performing convex hull operation on the identified berthing behavior points in the harbor, and determining the geographic range of berthing in the harbor by combining ship attribute information.
Specifically, convex hull operation is carried out on the identified harbor berthing behavior points, boundary points of the convex hulls are obtained according to operation results, object range data of the convex hulls are generated according to the boundary points, and finally the center point of the object is obtained. And further determining a rectangular area by combining ship attribute information such as the ship length, the ship width, the ship direction and the like of the ship berthing at the berthing action point, wherein the rectangular area is the geographical range of the berthing of the port.
It should be noted that, since there is no clear boundary between port berths, range data of a plurality of port berths acquired based on attribute information of different ships may have a crossover. Therefore, the method of the embodiment of the application can also combine the berth range data so as to reduce the data volume of the port berth geographic range data and facilitate the data processing. Specifically, the process of merging the berth range data includes: if the crossing area exists in the two berth geographic ranges in any port and the crossing area is larger than the preset threshold, merging the two berths and updating the original berth geographic ranges. Optionally, the preset threshold may be: the intersection area of two berths is 80% of the area of any one berth.
Further, in order to ensure accuracy of the port berth geographic range data, the port berth geographic range data may be periodically updated. For example, the obtained geographical range data of the port berth may be updated once every three months, and the update method may refer to the steps 201 to 202, which is not described in detail in the embodiment of the present application.
Optionally, in the present application, a one-to-one correspondence relationship between the port berths and the geographic range data may be established based on the port berth geographic range data obtained in the steps 2021 to 2022, so as to facilitate the review and storage of the port berth geographic range. By way of example, a port berth geographic scope table may be established as shown in table 1, with table 1 including the number of port berths and geographic scope data, wherein the port berths are generally rectangular areas, and the geographic scope data is a dataset made up of the locations of the four vertices of the rectangular areas.
TABLE 1
Step 203, obtaining at least one ship basic information, wherein the ship basic information comprises historical ship draft data, a ship model, maximum draft data and maximum load data corresponding to the ship model.
In an embodiment of the present application, the ship basic information includes historical ship draft data and ship profile information, and the ship profile information refers to basic attribute information of a ship.
The ship archive information can comprise information such as ship model, ship length, ship width, maximum draft data and maximum load data corresponding to the ship model. The maximum draft data and the maximum load data corresponding to the ship model are the maximum draft data and the maximum load data of different ships in a theoretical state. Alternatively, the ship profile information may be obtained based on a rowing ship profile, which is an internationally recognized provider of basic attribute information for ships with higher authorities.
The historical ship draft data represents a set of draft data when the ship enters the port berth and draft data when the ship exits the port berth during each berthing action of the ship at the port berth for a particular period of time, and the draft data when the ship enters the port berth is typically: in the course of a ship entering a port berth, the draft data in the latter half course is concentrated with higher frequency draft data, and the draft data when the ship leaves the port berth is usually: in the course of a ship leaving a port berth, the draft data in the latter half course is concentrated with higher frequency draft data. Wherein the specific period may be determined based on actual requirements. By way of example, the specific period may be 3 years.
It should be noted that, because the historical ship draft data is acquired based on the ship draft data reported by the AIS, there may be a certain error or delay when the AIS reports the data, so that the draft data with higher frequency in the second half draft data set of the ship entering the port berth and leaving the port berth is respectively used as the historical ship draft data of the ship entering the port berth and leaving the port berth, the error of the acquired historical ship draft data when the ship leaves or enters the port berth can be reduced, and the reliability of the acquired historical ship draft data is improved.
Optionally, in this step, the obtained ship archive information may have an incomplete phenomenon, so that in order to ensure the integrity of the ship archive information, the ship archive information may be supplemented based on a machine learning algorithm, so as to obtain relatively complete ship archive information. The supplementing process of the ship archive information can be as follows: for the ship with missing ship archive information, predicting the missing ship archive information by utilizing the ship archive information known by the ship based on a machine learning algorithm, taking a prediction result as supplementary information, and supplementing the ship archive information of the ship by utilizing the supplementary information. Alternatively, the machine learning algorithm may be a nearest neighbor algorithm or a random forest algorithm.
Step 204, inputting at least one historical ship draft data into a pre-constructed cargo estimation model to determine a maximum cargo handling capacity for berthing the ship at the port berth.
Before determining the maximum cargo handling capacity for berthing a vessel at a port berth, a cargo estimation model needs to be constructed, which may be constructed based on vessel basic information. Alternatively, the ship basic information may be ship basic information of a global ship acquired in a specific period. The construction process of the cargo estimation model includes: and acquiring a sample set formed by the maximum load data, the maximum draft data and the draft data of the ship in the idle state, and training and learning according to a machine learning algorithm or a deep learning algorithm to establish a mapping relation between the ship draft data and the ship load data, wherein the mapping relation is a load estimation model.
Wherein the draft data for the vessel in the empty state may be determined based on the vessel model, the vessel maximum draft data, and the historical vessel draft data, the process comprising:
and S11, calculating first no-load draft data based on the ship model and the maximum draft data of the ship.
In this step, for each ship, the first no-load draft data of the ship, which is theoretical draft data when the ship is empty, may be calculated using a ship no-load draft data calculation formula based on the ship model of the ship and the maximum draft data corresponding to the ship model. Wherein, the calculation formula of the no-load draft data of the ship is as follows,
Wherein T is max For maximum draft data, the values of α and β are determined based on the vessel type. Optionally, if the ship is a tanker, the values of α and β are 0.548 and 0.966, respectively; if the ship type is a bulk cargo ship, the values of alpha and beta are respectively 0.551 and 0.993; for other types of vessels, the values of α and β are 0.352 and 1.172, respectively.
And step S12, determining the draft data of the ship in the no-load state based on the historical ship draft data of the ship model and the first no-load draft data.
In this step, it is determined whether or not there is a value of [ 0.8xT ] in the historical ship draft data based on the historical ship draft data of each ship B ，1.2×T B If the data exist, determining the minimum data with the largest occurrence number in the range as draft data of the ship in an empty load state; if not, the first empty draft data is determined as draft data of the vessel in an empty state.
The draft data of the ship in the no-load state is corrected and determined based on the actual historical draft data of the ship and the theoretical draft data of the ship in the no-load state, so that the finally determined draft data of the ship in the no-load state is more in line with the actual situation. Furthermore, the cargo estimation model established based on the draft data of the ship in the no-load state also has higher accuracy and actual reference value.
In an embodiment of the present application, the process of determining the maximum cargo handling amount of the vessel berthed at the port berth may include: each historical ship draft data is input to a pre-constructed cargo estimation model, respectively, to determine an actual cargo handling capacity of the ship corresponding to the historical ship draft data, and further, a maximum cargo handling capacity of the ship berthing at the port berth is determined based on the actual cargo handling capacity of the ship and the maximum load data of the ship. Wherein the cargo handling amount is the amount of cargo loaded or unloaded by berthing the ship at the port berth.
Optionally, the determining of the maximum cargo handling capacity of the moored vessel at the port berth may comprise at least two alternative implementations:
in a first alternative implementation, when only one vessel is berthed at the port berth, determining the maximum cargo handling capacity for berthing vessels at the port berth comprises:
step S21, inputting the historical ship draft data into a pre-constructed cargo estimation model to determine the actual cargo handling capacity of the ship corresponding to the historical ship draft data.
In the step, the historical ship draft data can be input into a pre-constructed cargo estimation model based on the acquired historical ship draft data when the ship enters the port berth, so as to determine the first cargo carrying capacity of the ship; based on the obtained historical ship draft data when the ship leaves the port berth, inputting the historical ship draft data into a pre-constructed cargo estimation model, and determining a second cargo carrying capacity of the ship; further, an actual cargo handling capacity of the vessel is determined based on the first cargo capacity and the second cargo capacity, the actual cargo handling capacity being a difference between the first cargo capacity and the second cargo capacity.
Step S22, determining the maximum cargo handling capacity of the ship berthing at the port berth based on the actual cargo handling capacity of the ship and the maximum load data of the ship.
In this step, the maximum cargo handling capacity of the vessel berthing at the port berth may be determined by judging whether the actual cargo handling capacity of the vessel determined in the above step S21 is greater than or equal to a cargo handling capacity threshold of the vessel, wherein the cargo handling capacity threshold of the vessel is 0.9 times the maximum load data of the vessel. Optionally, if the actual cargo handling capacity of the vessel is greater than or equal to the cargo handling capacity threshold of the vessel, determining the actual cargo handling capacity of the vessel as a maximum cargo handling capacity for berthing the vessel at the port berth; and if the actual cargo handling capacity of the vessel is less than the cargo handling capacity threshold of the vessel, filtering the actual cargo handling capacity of the vessel.
By way of example, assume that port berth A is moored within three years 1 Is of ship B 1 Ship B can be acquired within three years based on basic ship information 1 Historical vessel draft data C of (2) 1 And maximum load data E 1 。
Further, it can be based on ship B 1 Historical vessel draft data C of (2) 1 And a cargo estimation model for determining vessel B 1 Berth A at a harbor 1 The actual cargo handling capacity at berthing is D 1 And judge the realityQuantity D of inter-cargo handling 1 Greater than the maximum load data E of the ship 1 Is determined to be at port berth A by a factor of 0.9 1 The maximum cargo handling capacity of the berthed ship is D 1 。
In a second alternative implementation, when there are a plurality of vessels berthed at the port berth, determining a maximum cargo handling capacity to berth the vessels at the port berth comprises: the method comprises the steps of acquiring a plurality of basic information of ships berthing at the port berth, respectively inputting each historical ship draft data into a pre-constructed cargo estimation model to determine the actual cargo handling capacity of each ship corresponding to each historical ship draft data, and further, determining the maximum cargo handling capacity of the ships berthing at the port berth based on the actual cargo handling capacity of each ship and the corresponding maximum load data.
The method for obtaining the actual cargo handling capacity of each ship may include: acquiring historical ship draft data when a ship enters a port berth from each historical ship draft data, inputting the historical ship draft data into a pre-constructed cargo estimation model, and determining a first cargo carrying capacity of the ship; acquiring historical ship draft data when the ship leaves the port berth from each historical ship draft data, inputting the historical ship draft data into a pre-constructed cargo estimation model, and determining a second cargo carrying capacity of the ship; the actual cargo handling capacity of each ship is determined based on the first cargo carrying capacity and the second cargo carrying capacity, the actual cargo handling capacity being a difference between the first cargo carrying capacity and the second cargo carrying capacity.
The determining of the maximum cargo handling capacity of the moored vessel at the port berth may comprise: judging whether the actual cargo handling capacity of each ship is greater than or equal to a cargo handling capacity threshold of the ship; if there is at least one vessel having an actual cargo handling capacity greater than or equal to the cargo handling capacity threshold of the vessel, determining the actual cargo handling capacity of the at least one vessel as a maximum cargo handling capacity for berthing the vessel at the port berth; and if the actual cargo handling capacity of the vessel is less than the cargo handling capacity threshold of the vessel, filtering the actual cargo handling capacity of the vessel.
Alternatively, when there are a plurality of vessels berthed at the port berth, the process of determining the maximum cargo handling capacity for berthing vessels at the port berth may be at least one of three alternative implementations:
in a first alternative implementation, if no vessel of the plurality of vessels has an actual cargo handling capacity of the vessel that is greater than or equal to the cargo handling capacity threshold of the vessel, the actual cargo handling capacity of the plurality of vessels is filtered.
In a second alternative implementation, if there is a vessel in the plurality of vessels whose actual cargo handling capacity is greater than or equal to the vessel's cargo handling capacity threshold, the actual cargo handling capacity of the vessel is determined to be the maximum cargo handling capacity of the vessel moored at the port berth.
In a third alternative implementation, if there is a plurality of vessels in the plurality of vessels having an actual cargo handling capacity greater than or equal to the cargo handling capacity threshold of the corresponding vessel, the actual cargo handling capacity of the plurality of vessels is determined to be the maximum cargo handling capacity of the vessel moored at the port berth.
By way of example, assume that port berth A is moored within three years 1 Has B on the vessel 1 、B 2 、B 3 And B 4 Ship B can be acquired within three years based on basic ship information 1 、B 2 、B 3 And B 4 Respective historical vessel draft data C 1 、C 2 、C 3 And C 4 And maximum load data E 1 、E 2 、E 3 And E is 4 。
Further, it can be based on ship B 1 、B 2 、B 3 And B 4 Historical vessel draft data C of (2) 1 、C 2 、C 3 And C 4 And a cargo estimation model for determining vessel B 1 、B 2 、B 3 And B 4 Berth A at a harbor 1 The actual cargo handling amounts at berthing are respectively D 1 、D 2 、D 3 And D 4 And judging the actual cargo handling capacity D 1 、D 2 And D 4 Greater than the maximum load data E of the respective vessel 1 、E 2 And E is 4 Is 0.9 times that ofDetermining berth A at a Port 1 The maximum cargo handling capacity of the berthed ship is D 1 、D 2 And D 4 。
Step 205, obtaining a minimum ship berthing time corresponding to the maximum cargo handling capacity.
In this step, when only one vessel is moored at the port berth and the actual cargo handling capacity of the vessel is greater than or equal to the cargo handling capacity threshold of the vessel, the berth time of the vessel at the port berth is determined as the minimum vessel berth time.
When a plurality of ships are berthed at the port berth and the actual cargo handling capacity of only one ship in the plurality of ships is greater than or equal to the cargo handling capacity threshold of the ship, determining the berthing time of the ship at the port berth as the minimum ship berthing time; or, if the actual cargo handling capacity of a plurality of vessels among the plurality of vessels is greater than or equal to the cargo handling capacity threshold of the corresponding vessel, determining the minimum value among the plurality of vessel berthing times as the minimum vessel berthing time.
Illustratively, at port berth A 1 Berthing vessel B 1 、B 2 、B 3 And B 4 In vessel B 1 、B 2 And B 4 Actual cargo handling capacity D of (2) 1 、D 2 And D 4 Greater than the maximum load data E of the respective vessel 1 、E 2 And E is 4 Is 0.9 times of and vessel B 2 Berth A at a harbor 1 Berthing time t at 2 Are all smaller than vessel B 1 And B 4 Is t of the parking time of (2) 1 And t 4 The minimum ship berthing time corresponding to the maximum cargo handling capacity is t 2 。
It should be noted that, in the embodiment of the present application, the obtained berthing time of the ship may be screened based on actual experience, so as to filter time data with errors, and improve reliability of the obtained minimum berthing time of the ship. By way of example, the screening method may be: assuming that the berthing time of a ship whose actual cargo handling capacity is 20 tons at the port berth is generally 12 hours to 24 hours, if the berthing time of a ship whose actual cargo handling capacity is 20 tons at the port berth is 2 hours, it is possible to determine that the berthing time data is error data and filter the berthing time data of the ship.
Step 206, calculating the loading and unloading efficiency of the port berth based on the maximum cargo loading and unloading amount and the minimum ship berthing time.
In this step, the ratio of the maximum cargo handling amount to the minimum vessel berthing time is taken as the handling efficiency of the port berth based on the determined minimum vessel berthing time and the maximum cargo handling amount of the vessel corresponding to the minimum vessel berthing time.
Illustratively, at port berth A 2 In a plurality of berthing vessels, berthing time t 2 Is the minimum ship berthing time, and the minimum ship berthing time t 2 Corresponding ship B 2 The actual cargo handling amount is the maximum cargo handling amount D 2 The maximum cargo handling amount D 2 With the minimum ship berthing time t 2 Ratio v of (v) 2 As the port berth A 2 Is provided.
It should be noted that, the method for mining the loading and unloading efficiency of the port berth provided in the steps 203 to 206 is a method provided for one port berth, and the method for mining the loading and unloading efficiency of other port berths can refer to the steps 203 to 206, which is not repeated in the embodiment of the present application.
Step 207, establishing a corresponding relation between the geographical range of the port berth and the loading and unloading efficiency of the port berth.
In this embodiment of the present application, the method for mining the loading and unloading efficiency of the port berths provided in the steps 203 to 206 may be used to obtain loading and unloading efficiencies of a plurality of port berths, and further, a one-to-one correspondence between the port berths, the geographic ranges and the loading and unloading efficiencies may be established by combining the geographic range data of the plurality of port berths, so as to improve the informatization level of the port berths.
By way of example, a port berth loading efficiency table may be established as shown in Table 2, with Table 2 including port berth numbers, geographic range data, and loading efficiency, where the loading efficiency may be in tons per hour (t/h).
TABLE 2
And step 208, updating the loading and unloading efficiency of the port berth after a preset time, wherein the preset time is determined based on actual requirements.
In order to ensure the accuracy of the obtained loading and unloading efficiency of the port berth, the basic data of the ship berthed at the port berth may be updated after a preset time, and the loading and unloading efficiency of the port berth may be further updated based on the mining method of the loading and unloading efficiency of the port berth provided in the above steps 203 to 207. The preset time may be determined based on actual needs. For example, the preset time is half a month.
In summary, the method for mining port berth loading and unloading efficiency provided in the embodiments of the present application may determine a geographical range of port berths based on the acquired global ship track data and port range data, further determine a maximum cargo loading and unloading amount of a ship berthed at the port berth based on at least one ship draft data in the basic information of at least one ship and a pre-constructed cargo estimation model, acquire a minimum ship berthing time corresponding to the maximum cargo loading and unloading amount, calculate loading and unloading efficiency of the port berth based on the maximum cargo loading and unloading amount and the minimum ship berthing time, implement mining of port berth loading and unloading efficiency information, establish a correspondence between port berth, geographical range and loading and unloading efficiency, and improve the informatization level of port berth. And the port berth loading and unloading efficiency can be updated after the preset time, so that the accuracy of the acquired port berth loading and unloading efficiency is improved.
The embodiment of the application provides a port berth loading and unloading efficiency excavating device, as shown in fig. 3, the device 30 includes:
a first obtaining module 301 configured to obtain at least one ship basic information, each ship basic information including historical ship draft data, a ship model, maximum draft data and maximum load data corresponding to the ship model;
a first determination module 302 configured to input at least one historical ship draft data to a pre-constructed cargo estimation model to determine a maximum cargo handling capacity of the berthing ship at the port berth, the cargo handling capacity being an amount of cargo loaded or unloaded by the berthing ship at the port berth;
a second acquisition module 303 configured to acquire a minimum ship berthing time corresponding to the maximum cargo handling amount;
a calculation module 304 configured to calculate a loading and unloading efficiency of the port berth based on the maximum cargo loading and the minimum vessel berthing time.
Optionally, the first determining module 302 is configured to:
inputting each historical ship draft data into a pre-constructed cargo estimation model respectively to determine the actual cargo handling capacity of the ship corresponding to the historical ship draft data;
Based on the actual cargo handling capacity of the vessel and the maximum load data of the vessel, a maximum cargo handling capacity for berthing the vessel at the port berth is determined.
Optionally, the first determining module 302 is configured to:
acquiring historical ship draft data when a ship enters a port berth from each historical ship draft data, inputting the historical ship draft data into a pre-constructed cargo estimation model, and determining a first cargo carrying capacity of the ship;
acquiring historical ship draft data when the ship leaves the port berth from each historical ship draft data, inputting the historical ship draft data into a pre-constructed cargo estimation model, and determining a second cargo carrying capacity of the ship;
and determining the actual cargo loading and unloading amount of the ship based on the first cargo carrying amount and the second cargo carrying amount, wherein the actual cargo loading and unloading amount is the difference value between the first cargo carrying amount and the second cargo carrying amount.
Optionally, the first determining module 302 is configured to:
judging whether the actual cargo handling capacity of each ship is larger than or equal to a cargo handling capacity threshold of the ship, wherein the cargo handling capacity threshold of the ship is 0.9 times of the maximum load data of the ship;
if there is at least one vessel having an actual cargo handling capacity greater than or equal to the cargo handling capacity threshold of the vessel, determining the actual cargo handling capacity of the at least one vessel as a maximum cargo handling capacity for berthing the vessel at the port berth;
And if the actual cargo handling capacity of the vessel is less than the cargo handling capacity threshold of the vessel, filtering the actual cargo handling capacity of the vessel.
Optionally, the first determining module 302 is configured to:
training and learning according to a machine learning algorithm or a deep learning algorithm by utilizing a sample set formed by the maximum load data, the maximum draft data and the draft data of the ship in an empty state so as to establish a mapping relation between the draft data of the ship and the load data of the ship, wherein the mapping relation is used as a load estimation model.
Optionally, the first determining module 302 is configured to:
calculating first no-load draft data based on the vessel model and the vessel maximum draft data;
and determining the draft data of the ship in the empty state based on the historical ship draft data of the ship model and the first empty draft data.
Optionally, as shown in fig. 4, the apparatus 30 further includes:
an identification module 305 configured to identify a berthing behaviour of the vessel at the port based on the acquired global vessel trajectory data and port range data;
a second determination module 306 configured to determine a port berthing geographical range from berthing behavior and vessel attribute information;
the establishing module 307 is configured to establish a corresponding relationship between the geographical range of the port berth and the loading and unloading efficiency of the port berth.
In summary, in the port berth loading and unloading efficiency mining device provided by the embodiment of the application, the identification module may identify the berthing behavior of the ship at the port based on the acquired global ship track data and port range data, the second determination module may determine the berthing geographic range of the port berth based on the berthing behavior and the ship attribute information, the first acquisition module may acquire at least one ship draft data based on the basic file of the at least one ship, the first determination module may determine the maximum cargo loading and unloading amount of the ship based on the at least one ship draft data and a pre-constructed cargo estimation model, the second acquisition module may acquire the minimum ship berthing time of the ship corresponding to the maximum cargo loading and unloading amount, the calculation module may calculate the loading and unloading efficiency of the port berthing based on the maximum cargo loading and unloading amount and the minimum ship berthing time, so as to realize the mining of the port berthing efficiency information, and further, the establishment module may establish the corresponding relationship between the berthing position, geographic range and loading and unloading efficiency, and improve the informatization level of the port berthing. And the port berth loading and unloading efficiency can be updated after the preset time, so that the accuracy of the acquired port berth loading and unloading efficiency is improved.
Fig. 5 is a computer system including a Central Processing Unit (CPU) 401, which may 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, according to an exemplary embodiment. In the RAM403, various programs and data required for the system operation are also stored. The CPU401, ROM402, and RAM403 are connected to each other by 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 Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk or 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. The drives 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 installed on the drive 410 as needed, so that a computer program read therefrom is installed into the storage section 408 as needed.
In particular, the processes described above in fig. 1-2 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 shown in the flowcharts. In such embodiments, the computer program may be downloaded and installed from a network via a communication portion, and/or installed from a removable medium. The above-described functions defined in the system of the present application are performed 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. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any 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 context of this document, 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 the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. 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 flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods, apparatuses, 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 involved in the embodiments of the present application may be implemented by means of software, or may be implemented by means of hardware, and the described units may also be provided in a processor. Wherein the names of the units do not constitute a limitation of the units themselves in some cases. The described units or modules may also be provided in a processor, for example, as: a processor includes a first acquisition module, a first determination module, a second acquisition module, and a calculation module. The names of these units or modules do not in any way limit the units or modules themselves, for example, the first acquisition module may also be described as "first acquisition module for acquiring ship profile information".
As another aspect, the present application also provides a computer-readable medium that may be contained in the electronic device described in the above embodiment; or may exist alone without being incorporated 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 port berth handling efficiency mining method as described in the above embodiments.
The foregoing description is only of the preferred embodiments of the present application and is presented as a description of the principles of the technology being utilized. It will be appreciated by persons skilled in the art that the scope of the invention referred to in this application is not limited to the specific combinations of features described above, but it is intended to cover other embodiments in which any combination of features described above or equivalents thereof is possible without departing from the spirit of the invention. Such as the above-described features and technical features having similar functions (but not limited to) disclosed in the present application are replaced with each other.
Claims (12)
1. A method for efficiently excavating port berths, the method comprising:
Acquiring at least one ship basic information, each ship basic information comprising a history ship
The method comprises the steps of ship draft data, ship model, maximum draft data and maximum load data corresponding to the ship model;
inputting at least one of said historical ship draft data into a pre-constructed cargo estimation model to determine a maximum cargo handling capacity of a vessel berthed at a port berth, the cargo handling capacity being the amount of cargo loaded or unloaded by the vessel berthed at said port berth;
acquiring a minimum ship berthing time corresponding to the maximum cargo handling capacity;
determining the ratio of the maximum cargo handling capacity to the minimum ship berthing time as the loading and unloading efficiency of the port berth;
wherein said inputting at least one of said historical ship draft data into a pre-constructed cargo estimation model to determine a maximum cargo handling capacity for berthing a ship at a port berth comprises:
inputting each of the historical ship draft data into a pre-constructed cargo estimation model to determine an actual cargo handling capacity of the ship corresponding to the historical ship draft data;
determining a maximum cargo handling capacity for berthing the vessel at the port berth based on the actual cargo handling capacity of the vessel and the maximum load data of the vessel;
Wherein the determining the maximum cargo handling amount for berthing the vessel at the port berth based on the actual cargo handling amount of the vessel and the maximum load data of the vessel comprises:
determining whether an actual cargo handling capacity of each of the vessels is greater than or equal to the vessel
A cargo handling capacity threshold of 0.9 times the maximum load data of the vessel;
determining the actual cargo handling capacity of at least one of the vessels as a maximum cargo handling capacity for berthing the vessel at the port berth if the actual cargo handling capacity of the at least one of the vessels is greater than or equal to the cargo handling capacity threshold of the vessel;
and if the actual cargo handling capacity of the ship is smaller than the cargo handling capacity threshold of the ship, filtering the actual cargo handling capacity of the ship.
2. The method of claim 1, wherein said inputting each of said historical ship draft data into a pre-constructed cargo estimation model to determine an actual cargo handling capacity of said ship corresponding to said historical ship draft data, respectively, comprises:
acquiring historical ship draft data when the ship enters the port berth from each historical ship draft data, inputting the historical ship draft data into a pre-constructed cargo estimation model, and determining a first cargo carrying capacity of the ship;
Acquiring historical ship draft data when the ship leaves the port berth from each historical ship draft data, inputting the historical ship draft data into a pre-constructed cargo estimation model, and determining a second cargo carrying capacity of the ship;
determining an actual cargo handling capacity of the vessel based on the first cargo capacity and the second cargo capacity, the actual cargo handling capacity being a difference between the first cargo capacity and the second cargo capacity.
3. The method of claim 1, wherein the pre-constructed cargo evaluation model comprises:
training and learning according to a machine learning algorithm or a deep learning algorithm by using a sample set formed by the maximum load data, the maximum draft data and the draft data of the ship in an empty state so as to establish a mapping relation between the draft data of the ship and the load data of the ship, wherein the mapping relation is used as a cargo estimation model.
4. A method according to claim 3, wherein the draft data of the vessel in an empty state comprises:
calculating first no-load draft data based on the vessel model and the vessel maximum draft data;
and determining the draft data of the ship in the no-load state based on the historical ship draft data of the ship model and the first no-load draft data.
5. The method according to claim 1, wherein the method further comprises:
identifying the berthing behavior of the ship at the port according to the acquired global ship track data and port range data;
determining a port berthing geographic range according to the berthing behavior and the ship attribute information;
and establishing a corresponding relation between the geographical range of the port berth and the loading and unloading efficiency of the port berth.
6. A port berth loading and unloading efficiency mining apparatus, the apparatus comprising:
a first acquisition module configured to acquire at least one ship basic information, each of the ship basic information including historical ship draft data, a ship model, maximum draft data and maximum load data corresponding to the ship model;
a first determination module configured to input at least one of the historical ship draft data to a pre-constructed cargo estimation model to determine a maximum cargo handling capacity of a ship berthed at a port berth, the cargo handling capacity being an amount of cargo loaded or unloaded by the ship berthed at the port berth;
a second acquisition module configured to acquire a minimum ship berthing time corresponding to the maximum cargo handling amount;
A calculation module configured to calculate a loading and unloading efficiency of the port berth based on the maximum cargo loading and unloading amount and the minimum vessel berthing time;
the first determining module is specifically configured to input each historical ship draft data into a pre-constructed cargo estimation model respectively so as to determine the actual cargo loading and unloading amount of the ship corresponding to the historical ship draft data;
determining a maximum cargo handling capacity for berthing the vessel at the port berth based on the actual cargo handling capacity of the vessel and the maximum load data of the vessel;
the first determination module is specifically further configured to determine whether an actual cargo handling capacity of each of the vessels is greater than or equal to the vessel
A cargo handling capacity threshold of 0.9 times the maximum load data of the vessel;
determining the actual cargo handling capacity of at least one of the vessels as a maximum cargo handling capacity for berthing the vessel at the port berth if the actual cargo handling capacity of the at least one of the vessels is greater than or equal to the cargo handling capacity threshold of the vessel;
and if the actual cargo handling capacity of the ship is smaller than the cargo handling capacity threshold of the ship, filtering the actual cargo handling capacity of the ship.
7. The apparatus of claim 6, wherein the first determination module is configured to: acquiring historical ship draft data when the ship enters the port berth from each historical ship draft data, inputting the historical ship draft data into a pre-constructed cargo estimation model, and determining a first cargo carrying capacity of the ship;
acquiring historical ship draft data when the ship leaves the port berth from each historical ship draft data, inputting the historical ship draft data into a pre-constructed cargo estimation model, and determining a second cargo carrying capacity of the ship;
determining an actual cargo handling capacity of the vessel based on the first cargo capacity and the second cargo capacity, the actual cargo handling capacity being a difference between the first cargo capacity and the second cargo capacity.
8. The apparatus of claim 6, wherein the first determination module is configured to:
training and learning according to a machine learning algorithm or a deep learning algorithm by using a sample set formed by the maximum load data, the maximum draft data and the draft data of the ship in an empty state so as to establish a mapping relation between the draft data of the ship and the load data of the ship, wherein the mapping relation is used as a cargo estimation model.
9. The apparatus of claim 8, wherein the first determination module is configured to: calculating first no-load draft data based on the vessel model and the vessel maximum draft data;
and determining the draft data of the ship in the no-load state based on the historical ship draft data of the ship model and the first no-load draft data.
10. The apparatus of claim 6, wherein the apparatus further comprises:
an identification module configured to identify a berthing behavior of the vessel at the port based on the acquired global vessel trajectory data and port range data;
a second determination module configured to determine a port berthing geographical range from the berthing behavior and vessel attribute information;
the establishing module is configured to establish a corresponding relation between the geographical range of the port berth and the loading and unloading efficiency of the port berth.
11. A computer device, the computer device comprising:
a processor;
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the port berth handling efficiency mining method of any of claims 1 to 5.
12. A computer readable storage medium having instructions stored therein which, when run on a processing component, cause the processing component to perform the port berth handling efficiency mining method of any of claims 1 to 5.
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