Intelligent assessment method for congestion of air route node port

Abstract

The invention discloses a route node port congestion intelligent assessment method, which comprises the following steps of S1, selecting a supervision area, and obtaining route and corresponding port information contained in the supervision area; s2, obtaining the operation state of each operation item included in the port according to the port information; s3, acquiring AIS data of each ship in the supervision area, analyzing the ship in-out quantity and the cargo quantity of the port, and performing grouping through time periods; s4, constructing a congestion evaluation model, calculating the current operation state of the port, estimating the operation state of each subsequent time period, setting different weights for each operation item of the port, and counting the final score value of the port according to the load rate; and S5, according to the data in the steps S1 to S4, generating a statistical report according to the report template, and generating a visual chart. According to the method, quantitative calculation and evaluation of the congestion degree of the ship route node port are realized, and accurate data support is provided for ship route planning, ship scheduling and logistics optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of port management, and particularly relates to an intelligent evaluation method for port congestion at route nodes. Background Art

[0002] With the increase in global trade volume, shipping remains a large-capacity and economical mode of cargo transportation. However, with the growth of maritime shipping volume, the existing port system has been difficult to meet the needs of ship entry and exit. Moreover, the scale of ports cannot be upgraded during a short period of suspension, which will further increase the pressure on other ports. Therefore, under the existing port system, based on existing data, it is necessary to fully understand the working efficiency of ports, obtain data of ships at sea, generate data that enables managers to intuitively understand the existing operation status according to the system and ship data, give scientific suggestions based on the data, help managers scientifically plan the ships entering and leaving the ports, and avoid port congestion. Therefore, it is necessary to design an intelligent evaluation method for port congestion at route nodes to solve the above problems. Summary of the Invention

[0003] The purpose of the present invention is to provide an intelligent evaluation method for port congestion at route nodes.

[0004] To achieve the above purpose, the present invention adopts the following technical solutions:

[0005] An intelligent evaluation method for port congestion at route nodes includes the following steps:

[0006] S1. Select a regulatory area, obtain the routes included in the regulatory area, and obtain the port information of the nodes according to the routes;

[0007] S2. According to the port information, obtain the operation data of each port through multi-source data terminals, and calculate the operation status of each operation item included in the port according to the data;

[0008] S3. Obtain the AIS data of each ship in the regulatory area, analyze and obtain the ship information of each ship, analyze the ship entry and exit volume and cargo volume of the port according to the ship information, and group them by time period;

[0009] S4. Build a congestion evaluation model, calculate the current operation status and predict the operation status of each subsequent time period according to the ship information of each ship and the operation data of the corresponding port, set different weights for each operation item of the port, and count the final score value of the port according to the load rate of each operation item in each time period;

[0010] S5. Generate a statistical report according to the data in steps S1 - S4 according to the report template, and generate a visual chart.

[0011] Further, the supervised area is a closed area drawn by a drawing tool on a sea area map. The closed area contains sea areas and ports. Obtain the shipping lanes included in this supervised area, obtain the relationships between the shipping lanes and each port, and obtain the port information corresponding to the shipping lane nodes.

[0012] Further, the specific steps of step S2 are as follows:

[0013] S21. According to the port information, obtain the operation data of each port through multi-source data terminals, including operation items, real-time operation data, and historical operation data;

[0014] S22. Calculate the real-time operation status of each operation item according to the real-time operation data to obtain the load of each operation item; calculate the historical operation status of each operation item according to the historical operation data to obtain the average load and full load of each operation item under different ship types and different cargo types during a specified time period.

[0015] S23. Calculate the load rate of each operation item according to the current operation status, load, and full load of the operation item, and calculate its corresponding load rate through a specified time period.

[0016] Further, the operation items include the number of ship berths, the number of equipment, the stacking volume of each cargo type, and the throughput.

[0017] Further, the specific steps of step S3 are as follows:

[0018] S31. Obtain the AIS data of each ship in the supervised area, analyze and obtain the ship information of each ship. The ship information includes ship type, position, starting point, ending point, speed, course, cargo type, cargo volume, ship width, MMSI, IMO, call sign, and track;

[0019] S32. Match the corresponding shipping lane and port according to the ending point and track in the ship information, and calculate the cargo volume of each ship and this shipping lane, as well as the predicted arrival time, berthing time, and cargo handling time at the port according to the speed, cargo type, and cargo volume of each ship;

[0020] S33. Set 4 hours as a time period, divide the operation time of the port into 6 time groups, and assign each ship to the corresponding time group according to the arrival time.

[0021] Further, the ports include sub-ports, wharves, anchorages, berths, and supervised areas.

[0022] Further, the specific steps of step S4 are as follows:

[0023] S41. Build a congestion assessment model. Based on the location, destination port, and speed in the vessel information of each vessel, match the corresponding port and predict the time of its arrival at the destination port.

[0024] S42. Obtain the operation data of the destination port, calculate the current operation status of each operation item, and based on the ongoing operations, berth waiting, and cargo volume in the vessel information, as well as the cargo accumulation volume and throughput corresponding to the operation item, estimate the operation status of each subsequent time period.

[0025] S43. Set different weights for each operation item of the port respectively. According to the load rate of each operation item in each time period, the higher the load rate, the higher the corresponding weight score, and count the final score value of the port.

[0026] Further, step S4 also includes selecting a shipping route on the sea area map, obtaining all the vessel information on this route, analyzing the port tendency of the vessels at the route nodes, obtaining the number of vessels and the cargo weight, and estimating the change in throughput of the ports at the route nodes.

[0027] Further, the congestion assessment model is equipped with regression and clustering algorithms, and according to the weights assigned to each operation item, quantitatively calculate the index value of the current congestion level of the port.

[0028] After adopting the above technical solution, compared with the background technology, the present invention has the following advantages:

[0029] The present invention analyzes the corresponding port information on the shipping route, obtains the operation data of the port, analyzes the operation status and the maximum tolerable load of each operation item of the port, and through the analysis of historical operation data, helps the management side fully understand the real-time status of the port operation items, obtains the vessel information of the vessels on the shipping route, estimates the number of vessels and the cargo volume in the future time period, estimates the change in throughput of the corresponding port, and calculates the port congestion situation through the congestion assessment model, realizing the quantitative calculation and assessment of the congestion degree of the ports at the route nodes by integrating, processing, and analyzing the real-time and historical operation data from multiple ports, providing accurate data support for route planning, vessel scheduling, and logistics optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0031] In order to make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0032] It should be noted that in the present invention, terms such as "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are all based on the orientation or positional relationship shown in the drawings. They are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the devices or elements of the present invention must have a specific orientation. Therefore, they should not be construed as a limitation to the present invention.

[0033] Embodiment

[0034] Reference Figure 1 As shown, the present invention discloses an intelligent evaluation method for port congestion at route nodes, including the following steps:

[0035] S1. Select a regulatory area, obtain the routes included in the regulatory area, and obtain the port information of the nodes according to the routes;

[0036] S2. According to the port information, obtain the operation data of each port through multi-source data terminals, and calculate the operation status of each operation item included in the port according to the data;

[0037] S3. Obtain the AIS data of each ship in the regulatory area, analyze and obtain the ship information of each ship, analyze the ship in and out volume and cargo volume of the port according to the ship information, and group them by time period;

[0038] S4. Build a congestion evaluation model, calculate the current operation status and predict the operation status of each subsequent time period according to the ship information of each ship and the operation data of the corresponding port, set different weights for each operation item of the port respectively, and count the final score value of the port according to the load rate of each operation item in each time period;

[0039] S5. Generate a statistical report according to the data in steps S1 - S4 according to the report template, and generate a visualization chart.

[0040] The regulatory area is a closed area drawn on the sea area map by a drawing tool. The closed area contains sea areas and ports. Obtain the routes included in the regulatory area, obtain the relationship between the routes and each port, and obtain the port information corresponding to the route nodes.

[0041] The operation items are to extract feature items directly related to port congestion from a large amount of data by using data mining technology, such as ship docking frequency, cargo handling efficiency, etc. These feature items have important reference value for evaluating the congestion degree of the port.

[0042] The specific steps of step S2 are as follows:

[0043] S21. According to the port information, obtain the operation data of each port through multi-source data terminals, including operation items, real-time operation data and historical operation data;

[0044] S22. Calculate the real-time operation status of each operation item based on the real-time operation data to obtain the load of each operation item; calculate the historical operation status of each operation item based on the historical operation data to obtain the average load and full load of each operation item under different ship types and different cargo types during a specified time period.

[0045] S23. Calculate the load rate of the operation item based on the current operation status, load, and full load of the operation item, and calculate the corresponding load rate over a specified time period.

[0046] The operation items include the number of ship berths, the number of equipment, the stacking volume of each cargo type, and the throughput.

[0047] The specific steps of step S3 are as follows:

[0048] S31. Obtain the AIS data of each ship in the regulatory area, analyze and obtain the ship information of each ship. The ship information includes ship type, location, starting point, ending point, speed, course, cargo type, cargo volume, ship width, MMSI, IMO, call sign, and trajectory.

[0049] The ship types in this embodiment include cargo ships, fishing boats, high-speed ships, passenger ships, oil tankers, container ships, tugboats, or service ships.

[0050] Each port can count indicators such as the number of ships in port, the number of operating ships, the number of waiting ships, the duration in port, the operating duration, and the waiting duration on a daily basis. The operation volume of each type of ship can be counted by ship type to understand the distribution of the operation volume of each ship type.

[0051] S32. Match the corresponding route and port according to the ending point and trajectory in the ship information, calculate the cargo volume of each ship and the route based on the speed, cargo type, and cargo volume of each ship, as well as predict the arrival time at the port, the berthing time, and the cargo handling time.

[0052] S33. Set 4 hours as a time period, divide the port operation time into 6 time groups, and allocate each ship to the corresponding time group according to the arrival time.

[0053] Conduct comparative statistics on the ship in-and-out volume and port handling capacity for different time periods.

[0054] Divide the time into a time period of 4 hours, and respectively count the number of incoming and outgoing ships, the number of ships in port, the number of waiting ships, the number of operating ships, etc. in each time period of each port every day. Evaluate the port handling capacity based on the number of incoming and outgoing ships, the number of operating ships, and the number of waiting ships in each time period.

[0055] The ports include sub-ports, wharves, anchorages, berths, and regulatory areas.

[0056] The specific steps of step S4 are as follows:

[0057] S41. Construct a congestion assessment model. According to the position, destination port, and speed in the vessel information of each vessel, match the corresponding port and predict the time of arrival at the destination port;

[0058] S42. Obtain the operation data of the destination port, calculate the current operation status of each operation item. According to the vessel information of being under operation, berth waiting, and cargo volume, the cargo accumulation volume and throughput corresponding to the operation item, estimate the operation status of each subsequent time period;

[0059] S43. Set different weights for each operation item of the port respectively. According to the load rate of each time period of the operation item, when the load rate is higher, the corresponding weight score is higher, and count the final score value of the port.

[0060] The higher the score value, the higher the congestion degree of the port. By setting thresholds at different stages, it is divided into non-congested, moderately congested, and severely congested.

[0061] Step S4 further includes selecting a route on the sea area map, obtaining all vessel information on this route, analyzing the port tendency of the vessels at the route nodes, obtaining the number of vessels and the cargo weight, and estimating the throughput change of the ports at the route nodes.

[0062] The congestion assessment model is provided with regression and clustering algorithms. According to the weights assigned to each operation item, quantitatively calculate the index value of the current congestion level of the port. Using mathematical models and methods, comprehensively analyze and process the various characteristics of each port node. This index value can objectively and accurately reflect the congestion status of the port.

[0063] In step S5 of this embodiment, for a specified vessel, analyze the voyages within a period of time, and based on the voyages, analyze the departure port and destination port of the vessel. According to the number of voyages, the main flow direction and flow volume of the vessels can be analyzed. For a specified vessel, analyze the voyages within a period of time, and based on the voyages, analyze the departure port, destination port, departure berth, destination berth, departure anchorage, and destination anchorage of the vessel. According to the grouping statistics of the number of voyages, the ports, terminals, and anchorages with high vessel flow heat can be analyzed.

[0064] For vessels with relatively stable routes such as liners and passenger ships, the flow of the route can be analyzed. A route can include multiple calling ports. By establishing the corresponding relationship between the route and the calling sequence. Combining the voyage data, the number of voyages of the route within a period of time, that is, the flow volume, can be analyzed.

[0065] Statistically analyze and predict the number of ships arriving at ports in the past, present, and future. The indicators include the number of ships in the port per day, the number of ships under operation per day, the number of ships waiting at anchor per day, etc.

[0066] In this embodiment, the operation data from each port is updated in real time, and the congestion index is recalculated based on the new data. This dynamic update mechanism ensures that the system can promptly reflect the congestion changes at the port and provide users with the most accurate data support.

[0067] In this embodiment, by analyzing the port information corresponding to the shipping route, the operation data of the port is obtained, the operation status and maximum load capacity of each operation item at the port are analyzed, and through the analysis of historical operation data, it helps the management side fully understand the real-time status of the port operation items, obtain the ship information of the ships on the shipping route, estimate the number of ships and the volume of goods in the future time period, estimate the change in the throughput of the corresponding port, and calculate the port congestion situation through the congestion assessment model. It realizes the quantitative calculation and evaluation of the congestion degree of the port nodes on the shipping route by integrating, processing, and analyzing the real-time and historical operation data from multiple ports, and provides accurate data support for shipping route planning, ship scheduling, and logistics optimization.

[0068] As mentioned above, it is only the preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. An intelligent assessment method for port congestion at a route node, characterized in that: The following steps are involved: S1. Select a regulatory area, obtain the routes included in the regulatory area, and obtain the port information of the node according to the routes; S2. Based on the port information, the operation data of each port is obtained through the multi-source data terminal, and the operation status of each operation project included in the port is calculated based on the data; S3. Obtain AIS data of each ship in the supervision area, analyze and obtain the ship information of each ship, analyze the ship in and out volume and cargo volume of the port based on the ship information, and group them by time period; S4. Construct a congestion assessment model, calculate the current operation status and estimate the operation status in subsequent time periods based on the ship information of each ship and the operation data of the corresponding port, set different weights for each operation item of the port, and calculate the final score of the port based on the load rate of the operation item in each time period; S5. Generate a statistical report based on the data in steps S1-S4 according to the report template and generate a visual chart.

2. The method for intelligently assessing port congestion at a route node according to claim 1, characterized in that: The regulatory area is a closed area drawn on a sea map by a drawing tool, and the closed area includes sea areas and ports. The routes included in the regulatory area are obtained, the relationship between the routes and the ports is obtained, and the port information corresponding to the route nodes is obtained.

3. The method for intelligently assessing port congestion at a route node according to claim 2, characterized in that: The specific steps of step S2 are as follows: S21. Based on the port information, obtain the operation data of each port through multi-source data terminals, including operation items, real-time operation data and historical operation data; S22. Calculate the real-time operation status of each operation item according to the real-time operation data to obtain the load of each operation item; calculate the historical operation status of each operation item according to the historical operation data to obtain the average load and full load of each operation item for different ship types and different cargo types in a specified time period; S23. Calculate the load rate of the operation project according to the current operation status, load and full load of the operation project, and calculate the corresponding load rate through a specified time period.

4. The method for intelligently assessing port congestion at a route node according to claim 3, characterized in that: The operation items include the number of ship berths, the amount of equipment, the accumulation of various types of cargo and the throughput.

5. The method for intelligently assessing port congestion at a route node according to claim 4, characterized in that: The specific steps of step S3 are as follows: S31. Obtain AIS data of each ship in the supervision area, analyze and obtain ship information of each ship, including ship type, position, starting point, end point, speed, course, cargo type, cargo volume, ship width, MMSI, IMO, call sign and track; S32, matching the corresponding route and port according to the destination and track in the ship information, calculating the cargo volume of each ship and the route according to the speed, cargo type and cargo volume of each ship, and predicting the arrival time, berthing time and cargo throughput time of the port; S33. Set 4 hours as a time period, divide the port's operating time into 6 time groups, and assign each ship to a corresponding time group according to its arrival time.

6. The method for intelligently assessing port congestion at a route node according to claim 5, characterized in that: The port includes sub-ports, docks, anchorages, berths and supervision areas.

7. The method for intelligently assessing port congestion at a route node according to claim 1, characterized in that: The specific steps of step S4 are as follows: S41, constructing a congestion assessment model, matching the corresponding port according to the location, destination port and speed in the ship information of each ship, and predicting the time of arrival at the destination port; S42, obtaining the operation data of the destination port, calculating the current operation status of each operation project, and estimating the operation status of each subsequent time period based on the information of the ship in operation, waiting at berth and cargo volume, the cargo accumulation volume and throughput corresponding to the operation project; S43. Different weights are set for each operation item of the port. According to the load rate of the operation item in each time period, the higher the load rate, the higher the corresponding weight score. The final score value of the port is calculated.

8. The method for intelligently assessing port congestion at a route node according to claim 7, characterized in that: The step S4 also includes selecting a route on the sea map, obtaining all ship information on the route, analyzing the port trends of the route node ships, obtaining the number of ships and cargo weight, and estimating the throughput changes of the route node ports.

9. The method for intelligently assessing port congestion at a route node according to claim 8, characterized in that: The congestion assessment model is provided with regression and clustering algorithms, and quantitatively calculates the index value of the current congestion level of the port according to the weights assigned to each operation item.

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