Ship gate waiting time length prediction method and device, electronic equipment, readable storage medium and chip

Abstract

The embodiment of the invention provides a ship waiting time length prediction method and device, electronic equipment, a readable storage medium and a chip, and the method comprises the steps: determining navigation data in a ship automatic recognition system; determining a first area corresponding to a ship lock passing lock chamber according to the selected ship lock; determining a historical lockage record; determining a sample ship passing through the first area according to the historical lockage record; the sample ships are classified, and at least one ship type is determined; according to the selected ship lock, determining a second area corresponding to anchoring and waiting for the lock; determining the gate waiting duration of the plurality of sample ships corresponding to the second area; determining a gate waiting parameter corresponding to each ship type according to the gate waiting duration; determining a target ship and trajectory parameters corresponding to the target ship; and according to the trajectory parameters and the gate waiting parameters, determining the predicted gate waiting duration and the predicted gate passing time of the target ship. Through the scheme of the invention, the ship waiting time length prediction precision and the ship lock scheduling efficiency are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of inland river shipping intelligent scheduling, in particular to a ship waiting time prediction method and device, an electronic device, a readable storage medium and a chip. BACKGROUND

[0002] At present, the prediction of ship lock passing and waiting time mainly relies on artificial experience, and some methods based on rules and experience are used to predict the ship lock passing time, which cannot dynamically respond to the change of ship flow, has great subjectivity, low accuracy and low scheduling efficiency, leading to serious congestion of inland river ship locks and affecting the efficiency of inland river shipping. SUMMARY

[0003] The purpose of the embodiments of the present application is to provide a ship waiting time prediction method and device, an electronic device, a readable storage medium and a chip, which can solve the problem of predicting ship lock passing and waiting time, relying on artificial experience, low accuracy and low scheduling efficiency.

[0004] Therefore, the embodiments of the first aspect of the present application provide a ship waiting time prediction method.

[0005] The embodiments of the second aspect of the present application provide a ship waiting time prediction device.

[0006] The embodiments of the third aspect of the present application provide an electronic device.

[0007] The embodiments of the fourth aspect of the present application provide a readable storage medium.

[0008] The embodiments of the fifth aspect of the present application provide a chip.

[0009] In order to achieve the above-mentioned purpose, the embodiments of the first aspect of the present application provide a ship waiting time prediction method, comprising: determining the navigation data in the ship automatic identification system; determining the first area corresponding to the ship lock passing lock chamber according to the selected ship lock; determining the historical lock passing record according to the navigation data; determining the sample ship passing the first area according to the historical lock passing record; classifying the sample ship to determine at least one ship type; determining the second area corresponding to the anchor waiting lock according to the selected ship lock; determining the waiting time of the plurality of sample ships corresponding to the second area; determining the waiting parameters corresponding to each ship type according to the waiting time; determining the target ship and the trajectory parameters corresponding to the target ship; determining the predicted waiting time and the predicted lock passing time of the target ship according to the trajectory parameters and the waiting parameters.

[0010] In the present application, the ship position, speed, heading and other dynamic data of multiple ships are combined with the ship name, call sign, length, width and other static data of the ships by the automatic identification system (AIS) and the global positioning system, and are played to nearby water area ships and shore base stations. The navigation data also includes the structural parameters, latitude and longitude coordinates of multiple ship locks, and the corresponding lock chamber working conditions. According to the selected ship lock, the first area corresponding to the actual geographical coordinates of the ship lock passing lock chamber is determined, the historical lock records of multiple ships are fused with the spatial data, at least one ship whose AIS trajectory crosses the first area within the recording time is determined, and the at least one ship whose AIS trajectory crosses the first area is determined as a sample ship. The ship type of each sample ship is determined, and the scheduling priority and lock operation complexity of each ship type are different when passing through the lock. The sample ships are grouped by type to analyze the lock waiting rules of each type of ship. According to the selected ship lock, the second area corresponding to the ship anchoring lock is determined, and the lock waiting time of the sample ship corresponding to the second area is determined according to the time when the sample ship enters the second area and the time when the sample ship leaves the second area. The lock waiting time of multiple different ship types within the recording time is determined. For the ship type, the lock waiting parameters of each ship type are determined, which are used to represent the lock waiting situation of each ship type in the second area. The lock waiting parameters are representative values such as median or average. The expected lock waiting time of the target ship is determined according to the AIS trajectory parameters of the target ship and the lock waiting parameters corresponding to the ship type of the target ship, so as to determine the expected lock passing time of the target ship.

[0011] In some technical solutions, the expected lock waiting time of the target ship is determined according to the trajectory parameters and the lock waiting parameters, including: determining the static data of the target ship according to the automatic identification system; determining the target ship type of the target ship according to the static data; determining the lock waiting parameters corresponding to the target ship type; determining the expected lock waiting time and the expected lock passing time of the target ship according to the trajectory parameters and the lock waiting parameters corresponding to the target ship type.

[0012] In the present application, the static data of the target ship is parsed from the AIS data sent by the ship to determine the target ship type corresponding to the target ship. According to the determined target ship type, the lock waiting parameters corresponding to the target ship type are retrieved and called from the previously calculated database or sample model. The prediction result corresponding to the target ship, i.e. the expected lock waiting time, is determined according to the trajectory parameters of the target ship and the lock waiting parameters corresponding to the target ship type.

[0013] In some embodiments, the expected waiting time of the target ship is determined according to the trajectory parameters and the waiting parameter corresponding to the target ship type, including: determining real-time position data and historical position data of the target ship according to the trajectory parameters; determining the region boundary of the second region; determining the entering time of the target ship according to the real-time position data, the historical position data and the region boundary; and determining the expected waiting time and the expected passing time of the target ship according to the entering time and the waiting parameter.

[0014] In the present embodiment, the system continuously receives and parses AIS messages sent by multiple ships near the ship lock, and extracts the most core dynamic information, i.e. latitude and longitude coordinates (real-time position data) and corresponding time stamps. The real-time position data of the ships that have not entered the second region is monitored through the AIS message, the real-time position coordinates of the ship are determined from the real-time position data, and the historical position data of the ship is obtained. When the real-time position coordinates pass through the region boundary of the second region, i.e. the ship crosses the region boundary and moves from the channel to the anchorage waiting area, it indicates that the ship has officially entered the waiting sequence and is marked as a target ship, the entering time is recorded, and the timing starts. The expected waiting time of the target ship is determined according to the entering time and the waiting parameter, and the passing time of the target ship is determined.

[0015] In some embodiments, the waiting parameter corresponding to each ship type is determined according to the waiting time, including: determining historical trajectory data of a plurality of sample ships; determining a first time when the sample ship enters the second region according to the historical trajectory data and the region boundary; determining a second time when the sample ship leaves the second region according to the historical trajectory data and the region boundary; determining the waiting time corresponding to the ship type according to the first time and the second time; determining the number of ships of each ship type; and determining the waiting parameter of each ship type according to the waiting time and the number of ships.

[0016] In the present embodiment, the complete AIS trajectory sequence of all sample ships identified in a specific historical period (such as T-1 day, i.e. yesterday, T being the current date) is retrieved from the database. These trajectory data are a series of latitude and longitude points sorted by time, each point having a time stamp, which completely records the movement of the ship at each moment. By tracing back the historical trajectory of each sample ship, the time point when the trajectory point first crosses the region boundary and enters the second region is determined, i.e. the first time. Similarly, by tracing back the same trajectory, the time point when the trajectory point of the sample ship last crosses the region boundary and leaves the second region is found, i.e. the second time. The system counts the number of all sample ships belonging to the same ship type in the same historical period according to the pre-completed ship classification. The waiting time of all sample ships under the same ship type is taken as a data set, and a waiting parameter representing the general waiting level of ships of the type is calculated using statistical methods.

[0017] In some embodiments, the second region comprises a second uplink region and a second downlink region.

[0018] In some embodiments, the second uplink region is a special waiting-to-lock anchorage electronic fence for uplink ships on an electronic map according to actual geographical coordinates, and the second downlink region is a special waiting-to-lock anchorage electronic fence for downlink ships on an electronic map according to actual geographical coordinates.

[0019] The system will backtrack the historical trajectory data of the sample ships corresponding to the second uplink region and the sample ships corresponding to the second downlink region, respectively. The trajectory of an uplink ship will only be compared with the boundary of the second uplink region to calculate the entering and leaving time, so as to obtain the uplink waiting-to-lock time. These data will be used to independently calculate the uplink waiting-to-lock parameters.

[0020] Similarly, the data of the sample ships corresponding to the second downlink region are used to calculate the downlink waiting-to-lock parameters.

[0021] In some embodiments, the ship waiting-to-lock time prediction method further comprises: determining at least one feature parameter, the feature parameter comprising a ship feature, an environment feature, and a congestion feature; and determining the predicted waiting-to-lock time of the target ship according to the trajectory parameter, the waiting-to-lock parameter, and the feature parameter.

[0022] In some embodiments, in the process of calculating the waiting-to-lock time for prediction, not only the single waiting-to-lock parameter is relied on, but also the dynamically changing features, i.e. the feature parameters, are calculated or obtained in real time. The feature parameters comprise a ship feature, an environment feature, and a congestion feature. The ship feature refers to the real-time state of the target ship, including but not limited to: real-time speed, navigation state (such as sailing, anchoring, and waiting-to-lock), ship draft, ship load condition, etc. For example, a ship with extremely slow speed may indicate that it is sailing cautiously or preparing to anchor and wait-to-lock. The environment feature refers to external natural conditions. It is obtained by accessing meteorological and hydrological data interfaces, including but not limited to: real-time wind speed, wind direction, visibility, water flow speed, water level, etc. For example, foggy weather with low visibility may cause the dispatch to slow down and prolong the waiting-to-lock time. The congestion feature refers to the traffic flow condition. It is based on real-time analysis of the current AIS data, including but not limited to: the total number of ships in the same direction in the second region, the number of queued ships in front of the target ship, the distribution proportion of different ship types in the region, etc.

[0023] A second aspect of the present invention provides a vessel waiting time prediction device, comprising: a data acquisition module for determining navigation data in an automatic identification system (AIS); a sample area module for determining a first area corresponding to the lock passage chamber based on a selected lock; a historical analysis module for determining historical lock passage records based on the navigation data; a sample determination module for determining sample vessels passing through the first area based on the historical lock passage records; a vessel classification module for classifying the sample vessels and determining at least one vessel type; a waiting area module for determining a second area corresponding to anchoring waiting time based on the selected lock; a duration determination module for determining the waiting time for multiple sample vessels corresponding to the second area; a parameter determination module for determining waiting parameters corresponding to each vessel type based on the waiting time; a target vessel module for determining the target vessel and the trajectory parameters corresponding to the target vessel; and a duration prediction module for determining the expected waiting time and expected passage time of the target vessel based on the trajectory parameters and the waiting parameters.

[0024] An embodiment of the third aspect of this application provides an electronic device, including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method for predicting the lock duration of ships, etc., as described in the first aspect.

[0025] An embodiment of the fourth aspect of this application provides a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the ship lock duration prediction method in the first aspect.

[0026] An embodiment of the fifth aspect of this application provides a chip including a processor and a communication interface, the communication interface and the processor being coupled together, the processor being used to run a program or instructions to implement the steps of the ship lock duration prediction method as described in the first aspect.

[0027] Additional aspects and advantages of the technical solutions of the present invention will become apparent in the following description or may be learned by practice of the invention. Attached Figure Description

[0028] Figure 1 One of the flowcharts of the method for predicting the waiting time of ships in locks according to this application is shown; Figure 2 A second flowchart illustrating the method for predicting lockage time according to this application is shown. Figure 3 The third flowchart of the method for predicting the waiting time of ships in locks according to this application is shown; Figure 4 The fourth flowchart illustrates the method for predicting the waiting time for ships at locks according to this application. Figure 5 Fifth of the flowcharts for the method of predicting the waiting time of ships in locks according to this application is shown; Figure 6 A schematic block diagram of the ship lock waiting time prediction device according to this application is shown; Figure 7 A schematic block diagram of the structure of an electronic device according to this application is shown; Figure 8 A schematic block diagram of the ship lock waiting time prediction system according to this application is shown; Figure 9 A schematic diagram of a ship passing through a lock according to this application is shown.

[0029] in, Figures 6 to 9 The correspondence between the reference numerals and component names in the attached drawings is as follows: 900: Vessel waiting time prediction device; 902: Data acquisition module; 904: Sample area module; 906: Historical analysis module; 908: Sample determination module; 910: Vessel classification module; 912: Waiting area module; 914: Duration determination module; 916: Parameter determination module; 918: Target vessel module; 920: Duration prediction module; 1000: Electronic equipment; 1109: Memory; 1110: Processor; 800: Vessel waiting time prediction system; 802: Data acquisition and storage module; 804: Feature analysis and statistical calculation module; 806: Prediction calculation module; 808: Result output module; 100: Selected lock; 102: Lock chamber passage area; 104: Downward waiting area; 106: Upward waiting area; 1022: Sample vessel. Detailed Implementation

[0030] To better understand the above-described objectives, features, and advantages of the embodiments of the present invention, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0031] Many specific details are set forth in the following description in order to provide a full understanding of this application. However, embodiments of the invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of this application is not limited to the specific embodiments disclosed below.

[0032] In the context of inland waterway shipping, the average waiting time for ships to pass through locks accounts for 30%-50% of the total sailing time, resulting in fuel waste and increased carbon emissions. Furthermore, traditional scheduling relies on manual experience and cannot dynamically respond to changes in ship traffic (such as concentrated passage through locks during holidays), leading to low scheduling efficiency.

[0033] Existing AIS data is not being fully utilized. The AIS system is short for Automatic Identification System, which consists of shore-based (base station) signal receiving facilities and shipboard equipment. The Automatic Identification System (AIS) works with the Global Positioning System (GPS or Beidou) to broadcast dynamic information such as ship position, speed and course, combined with static information such as ship name, call sign, length and beam, via VHF channels to nearby ships and shore-based base stations. This allows nearby ships and shore stations to keep abreast of the dynamic and static information of all nearby ships, enabling them to communicate and coordinate with each other and take necessary avoidance actions. It also greatly assists port and shipping traffic management departments in scheduling ships.

[0034] By using relevant data processing technologies, the messages sent by AIS devices are parsed and integrated with geographic information technology, allowing the parsed points to be displayed on a map. Each data point constitutes a ship's navigation trajectory. The identified trajectory points typically include fields such as: Maritime Mobile Identifier (MMSI), ship name, call sign, length, beam, ship type, longitude, latitude, ground speed, navigation status, heading, and reception time. AIS data is a true reflection of the ship's situation regarding the geographical, water, hydrological, meteorological, and route safety conditions at that time.

[0035] The following detailed description of the ship lock waiting time prediction method, device, electronic device, readable storage medium, and chip provided in this application embodiment, through specific embodiments and application scenarios, provides a detailed explanation.

[0036] like Figure 1 As shown, this embodiment provides a method for predicting the waiting time for ships in locks, including: Step S100: Determine the navigation data in the Automatic Identification System (AIS); Step S102: Determine the first area corresponding to the lock passage chamber based on the selected lock; Step S104: Determine historical lock passage records based on the navigation data; Step S106: Determine the sample vessels that passed through the first area based on historical lock passage records; Step S108: Classify the sample ships and determine at least one ship type; Step S110: Determine the second area corresponding to the anchorage lock, etc., based on the selected lock; Step S112: Determine the lock waiting time for multiple sample vessels corresponding to the second area; Step S114: Determine the lock waiting parameters corresponding to each vessel type based on the lock waiting time; Step S116: Determine the target vessel and the corresponding trajectory parameters; Step S118: Determine the expected waiting time and expected passage time of the target vessel based on the trajectory parameters and lock waiting parameters.

[0037] In this invention, the Automatic Identification System (AIS) is used in conjunction with the Global Positioning System (GPS) to transmit dynamic data of multiple vessels, such as their position, speed, and heading, along with static data such as their name, call sign, length, and beam, to nearby vessels and shore-based base stations. The navigation data also includes structural parameters, latitude and longitude coordinates, and corresponding lock chamber operating conditions for multiple locks. A first region corresponding to the actual geographical coordinates of the lock chamber is determined based on the selected lock. Historical lock passage records of multiple vessels are fused with spatial data to identify at least one vessel whose AIS trajectory traverses the first region within the recorded time. This vessel is then identified as a sample vessel. The vessel type of each sample vessel is determined, as each vessel type has different scheduling priorities and lock passage operation complexities. Grouping sample vessels by type facilitates subsequent analysis of lock waiting patterns for each type of vessel. A second region corresponding to vessel anchoring and waiting is determined based on the selected lock. The waiting time for the sample vessel in the second region is determined based on the time it enters and leaves the second region. The waiting time for multiple vessel types within the recorded time period is determined. For each vessel type, a waiting parameter is determined, representing the waiting status of each vessel type within the second area. The waiting parameter is a representative value such as the median or mean. Based on the target vessel's AIS trajectory parameters and the corresponding waiting parameter for its vessel type, the expected waiting time for the target vessel is determined, thus determining the expected passage time.

[0038] Understandably, by classifying vessels into different types and making predictions for each type separately, the accuracy of waiting time forecasts can be improved. Accurate waiting time forecasts provide lock dispatchers with a scientific basis for decision-making. The dispatch center can then more rationally arrange lock cycles and schedule vessel berths within the lock chambers, reducing downtime and allowing more vessels to pass safely within a given time frame, thereby improving overall traffic efficiency and alleviating congestion.

[0039] The first area is the lock chamber electronic fence Z1, and the second area is the anchorage and waiting lock electronic fence Z2. Lock chamber electronic fence Z1 is a virtual boundary area defined on an electronic map based on the actual geographical coordinates of the lock passage chamber. Whether a vessel can pass through the lock is determined by whether its AIS track point enters the virtual boundary area corresponding to the lock passage chamber. Anchorage and waiting lock electronic fence Z2 is a virtual area defined in the anchorages upstream and downstream of the lock, or in the waiting waters. When a vessel enters the virtual area corresponding to the anchorage and waiting lock electronic fence, it means the vessel has begun queuing to pass through the lock.

[0040] In some embodiments, navigation data may be determined through multidimensional data acquisition, including vessel data, meteorological data, maritime map data, and historical lock passage records.

[0041] For example, dynamic data includes: ship AIS trajectory (real-time location, historical trajectory, etc.), meteorological data (wind speed, visibility), and lock chamber operating conditions (working status, etc.).

[0042] For example, static data includes: basic ship parameters (ship type, length, beam, etc.), lock structure parameters (lock chamber size, sill depth), and historical lock passage records (ship type priority rules).

[0043] For example, historical lock passage records are used to provide archive information on past lock passages. Historical lock passage records include information such as the vessel's maritime mobility identification code, lock passage time, lock passage direction, and vessel type.

[0044] In some embodiments, the historical record duration can be optionally determined based on the average vessel flow corresponding to the current lock, and historical lock passage records within the historical record duration can be identified. The average vessel flow and the historical record duration are negatively correlated; the higher the average vessel flow, the shorter the historical record duration. This aims to identify various vessel types within the current watershed environment. For example, if the average vessel flow is greater than the average value, the historical record duration is 1 day, and historical lock passage records within the past day (T-1 day, where T is the current date) can be identified.

[0045] In some embodiments, optionally, the recording duration corresponding to the first region is the same as the recording duration of the second region. That is, multiple sample vessels corresponding to the first region are determined by the historical lock passage records of the past day, and the lock passage duration of sample vessels of different vessel types corresponding to the second region in the past day is obtained.

[0046] In some embodiments, the recording duration corresponding to the second area can be dynamically adjusted based on the number of vessels in the second area. The number of vessels in the second area is positively correlated with the recording duration corresponding to the second area. For example, if the number of vessels waiting to pass through the locks in the second area is greater than the average as determined by the AIS system combined with the GPS system, the recording duration is reduced from 1 day to 0.5 days. That is, the waiting time of sample vessels of different vessel types in the second area corresponding to the past 0.5 days is obtained to improve the accuracy and robustness of the waiting parameters in the event of channel congestion.

[0047] For example, vessel types include: container ships, dry bulk carriers, cruise ships, passenger ships, and new energy vessels. Each vessel type has a different lock passage priority, and historical lock passage records include the lock passage priority corresponding to each vessel type.

[0048] In some embodiments, the predicted waiting time can be output to the system interface or a relevant scheduling system to assist in operational decision-making.

[0049] In one embodiment, the lock parameters of sample vessels of multiple different vessel types can be stored in a database or sample model.

[0050] In some embodiments, optionally, the median, mode, or a specific quantile (such as the 75th percentile) of the historical lock waiting time for the vessel type is calculated as a lock waiting parameter.

[0051] In some embodiments, optionally, such as Figure 2 As shown, step S118: Determine the estimated waiting time for the target vessel based on the trajectory parameters and lock waiting parameters, including: Step S1180: Determine the static data of the target vessel based on the Automatic Identification System (AIS); Step S1182: Determine the target vessel type based on static data; Step S1184: Determine the lock parameters corresponding to the target vessel type; Step S1186: Determine the expected waiting time and expected passage time of the target vessel based on the trajectory parameters and the lock waiting parameters corresponding to the target vessel type.

[0052] In this embodiment, the static data of the target vessel is parsed from the AIS data transmitted by the vessel to determine the target vessel type. Based on the determined target vessel type, the gate waiting parameters corresponding to the target vessel type are retrieved and called from the previously calculated database or sample model. Based on the trajectory parameters of the target vessel and the gate waiting parameters corresponding to the target vessel type, the prediction result corresponding to the target vessel, i.e., the estimated gate waiting time, is determined.

[0053] Understandably, traditional methods use the same average waiting time for all vessels, while this invention treats different vessel types, such as container ships and bulk carriers, differently. This is because different vessel types may have different priorities, lock passage complexity, and scheduling policies, resulting in variations in their waiting time distribution. By conducting targeted analysis for different vessel types, the accuracy and reliability of vessel lock passage time prediction in inland waterway shipping environments are improved.

[0054] In some embodiments, optionally, determining the target vessel type based on static data further includes: further classifying the vessel type into size classes (e.g., large container ships, medium and small container ships) within the same vessel type based on length and beam data. This is because the scheduling and lock passage operations of ultra-large vessels may be more time-consuming, and their lock waiting patterns differ from those of smaller vessels.

[0055] In some embodiments, optionally, determining the target vessel type based on static data further includes: identifying the vessel's draft and comparing it in real time with the sill depth published by the lock. Vessels with drafts close to or exceeding the sill depth may need to wait for a specific tide level or special scheduling, and their lock waiting parameters should be calculated separately.

[0056] In some embodiments, optionally, a visual label is created for the target vessel on the electronic navigation chart or scheduling system interface, dynamically displaying its expected lock passage time.

[0057] In some embodiments, optionally, text information about the expected lock passage time can be automatically sent to the target vessel via high-frequency voice broadcasting or a communication system.

[0058] In some embodiments, optionally, such as Figure 3 As shown, step S1186: Determine the estimated waiting time for the target vessel based on the trajectory parameters and the lock waiting parameters corresponding to the target vessel type, including: Step S11860: Determine the real-time and historical position data of the target vessel based on the trajectory parameters; Step S11862: Determine the boundary of the second region; Step S11864: Determine the entry time of the target vessel based on the real-time location data, the historical location data, and the area boundary; Step S11866: Determine the expected waiting time and expected passage time of the target vessel based on the entry time and waiting parameters.

[0059] In this embodiment, the system continuously receives and parses AIS messages sent by multiple vessels near the lock, extracting the most crucial dynamic information: latitude and longitude coordinates (real-time location data) and the corresponding timestamp. The system monitors the real-time location data of vessels not yet entering the second area using AIS messages, determining their real-time coordinates and acquiring their historical location data. When the real-time coordinates cross the boundary of the second area (i.e., the vessel crosses the boundary and heads from the channel towards the anchorage and lock waiting area), it indicates that the vessel has officially entered the waiting sequence, is marked as the target vessel, its entry time is recorded, and timing begins. Based on the entry time and lock waiting parameters, the estimated waiting time for the target vessel is determined, thus determining the target vessel's lock passage time.

[0060] Specifically, the system performs spatial geographic calculations (such as determining the position of points and polygons) by comparing the real-time latitude and longitude coordinates of the target vessel with the pre-defined regional boundaries on the electronic map. When the system detects that the vessel's trajectory point first enters the interior of this boundary from the outside and enters the second region, it records this precise time point.

[0061] Understandably, defining the start of the waiting period by delineating precise geographical boundaries, i.e., area boundaries, avoids timing errors caused by ambiguity in the anchorage area. For example, a ship might linger in the waters near the anchorage quite early, but only truly begin queuing after entering the designated waiting area. This scheme ensures consistency in the timing starting point, thus making the prediction results more reliable.

[0062] In some embodiments, the second region may be divided into multiple levels based on the actual number of vessels waiting to pass through the lock and the waiting area. Based on the specific location of the target vessel when it enters the second region (such as whether it is in the first-level waiting area or the second-level waiting area), the waiting sub-parameters corresponding to the sub-region are called to make a weighted correction on the expected waiting time, so as to reduce the prediction error caused by the different positions of the target vessel in the region.

[0063] For example, the area boundary includes the outer entrance boundary, the inner core boundary, or the pre-lock preparation boundary of the second area. Vessels enter the second area through the outer entrance boundary, pass through multiple levels of different sub-areas through the inner core boundary, or leave the second area through the pre-lock preparation boundary.

[0064] In some embodiments, the area boundary can be dynamically adjusted based on the real-time number and density of vessels waiting to enter the gate. For example, when there are too many vessels in the queue, the geographical scope of the entrance boundary can be automatically expanded to include vessels further away in the queue counting and prediction system, providing early warnings of congestion.

[0065] In some embodiments, the electronic fence may not necessarily be rectangular or circular; it may be a complex polygonal boundary defined according to the actual waterway, anchorage terrain, no-berthing zone, etc.

[0066] In some embodiments, the trajectory of the target vessel may be continuously monitored. If the vessel does not proceed to the gate after the recorded entry time but instead crosses the area boundary and leaves the anchorage, the waiting period is terminated, the prediction is canceled, and the queue position it occupies is released.

[0067] In some embodiments, optionally, to prevent false triggering of brief entry into the boundary and then leaving, after the target vessel's position first enters the area boundary, the duration of its stay within the boundary is continuously monitored, and only when the continuous stay exceeds a time threshold T (e.g., 5 minutes) is the moment of the first entry retrospectively confirmed as the entry time.

[0068] In some embodiments, optionally, such as Figure 4 As shown, step S114: Determine the lock waiting parameters corresponding to each vessel type based on the lock waiting time, including: Step S1140: Determine the historical trajectory data of multiple sample vessels; Step S1142: Determine the first moment when the sample vessel enters the second area based on historical trajectory data and area boundaries; Step S1144: Determine the second moment when the sample vessel leaves the second region based on historical trajectory data and regional boundaries; Step S1146: Determine the lock waiting time corresponding to the vessel type based on the first and second time points; Step S1148: Determine the number of ships for each ship type; Step S1150: Determine the lock waiting parameters for each vessel type based on the lock waiting time and the number of vessels.

[0069] In this embodiment, the system retrieves complete AIS trajectory sequences of all identified sample vessels within a specific historical period (e.g., yesterday) from the database. These trajectory data are a series of latitude and longitude points sorted by time, each with a timestamp, fully recording the movement of the vessels at every moment. By tracing back the historical trajectory of each sample vessel, spatial geographic calculations are used to determine the first moment when the trajectory point first crosses the regional boundary and enters the second region. Similarly, tracing back the same trajectory, the system finds the last time the trajectory point of that sample vessel crosses the regional boundary and leaves the second region, which is the second moment. Based on pre-completed vessel classification, the system counts the number of all sample vessels belonging to the same vessel type within the same historical period. The waiting times of all sample vessels of the same vessel type are collected as a dataset, and statistical methods are used to calculate a waiting parameter that represents the general waiting level of that type of vessel.

[0070] Understandably, precise geofence boundary triggering defines the start and end of the waiting period, rather than relying on rough manual recording or simple location, fundamentally ensuring the accuracy and consistency of the original data. Furthermore, the entire process, from trajectory backtracking and boundary judgment to time calculation and statistical evaluation, is completed automatically by the system, reducing subjective errors that may arise from manual data collection and entry, thereby improving the robustness of gate waiting time prediction.

[0071] For example, the median lock waiting time of sample ships of different ship types is obtained as the lock waiting parameter.

[0072] In some embodiments, the lock waiting parameters may optionally include, but are not limited to, the median lock waiting time of sample vessels of different vessel types or the average lock waiting time of sample vessels of different vessel types.

[0073] In some embodiments, the historical trajectory data may be cleaned, missing trajectory points may be repaired using time-series-based interpolation algorithms (e.g., linear interpolation and Kalman filtering), and the trajectory may be smoothed using a moving average algorithm to eliminate positioning errors.

[0074] In some embodiments, optionally, not all vessels entering the second area are waiting to pass through the lock; they may be temporarily anchored, operating, or passing through. Based on trajectory pattern recognition of vessel behavior, if a vessel is identified as being anchored and waiting for the lock in the second area for more than a threshold duration, and its final destination is the current lock, it is determined to be a valid sample; if it quickly crosses the area or sails in another direction, it is determined to be an invalid sample and removed from the current statistics.

[0075] In some embodiments, the sample vessel may not be stationary during the waiting period and may move briefly at times. Calculating its total dwell time throughout the area is more accurate than simply calculating the difference between entry and exit time. Determining the lock waiting time based on the first and second times also includes calculating the cumulative value of all dwell times of the sample vessel within the area boundary as its lock waiting time.

[0076] In some embodiments, the second region may be divided into multiple sub-regions and each sub-region may be assigned a weight coefficient; the waiting time is the sum of the products of the vessel’s stay time in each sub-region and the corresponding region weight.

[0077] In some embodiments, the second region may optionally include a second uplink region and a second downlink region.

[0078] In this embodiment, the second upbound area is a dedicated waiting anchorage electronic fence for vessels in the upbound direction, defined on the electronic map according to actual geographical coordinates; the second downbound area is a dedicated waiting anchorage electronic fence for vessels in the downbound direction, defined on the electronic map according to actual geographical coordinates.

[0079] The system will backtrack the historical trajectory data of sample vessels corresponding to the second uphill zone and the second downhill zone, respectively. The trajectory of an uphill vessel will only be compared with the boundary of the second uphill zone to calculate its entry and exit times, thereby obtaining its waiting time at the lock. This data will be used to independently calculate the uphill waiting parameters.

[0080] Similarly, the data of the sample vessels corresponding to the second downstream region are used to calculate the downstream lock parameters.

[0081] Understandably, by establishing independent statistical models for the uplink and downlink directions, errors caused by conflating data from two different patterns are avoided. This ensures that the lock parameters matched to the target vessel are highly consistent with its actual operational scenario, thereby greatly improving the accuracy of the prediction results.

[0082] The boundaries of the second uplink region and the second downlink region are different.

[0083] In some embodiments, the lock scheduling center can optionally view the predicted queue length and estimated waiting time for both upstream and downstream directions, thereby developing a more scientific scheduling plan. For example, it can dynamically adjust the allocation ratio of upstream and downstream lock sessions based on the uneven congestion in the two directions, thereby maximizing the overall throughput efficiency of the lock.

[0084] In some embodiments, the range of the uplink and downlink zones is optionally not fixed and can be dynamically adjusted according to demand. The geographical boundaries of the second uplink zone and the second downlink zone are dynamically adjusted based on the real-time ratio of the number of vessels waiting to pass in the uplink and downlink directions. For example, when there are far more downlink vessels than uplink vessels, the electronic fence range of the downlink zone can be temporarily expanded to include more downstream waters in the queuing system, achieving flexible and efficient utilization of anchorage resources.

[0085] In some embodiments, optionally, completely independent prediction models are established for the uplink and downlink directions. The median waiting time or other waiting parameters are calculated independently for each vessel type in the uplink and downlink directions. That is, there are uplink container ship parameters and downlink container ship parameters, and their values ​​may differ.

[0086] In some embodiments, optionally, the data and prediction results for the uplink and downlink areas are displayed in separate screens or blocks on the scheduling system interface. The electronic navigation maps of the second uplink area and the second downlink area are displayed in separate screens, and the real-time position, ship type, and predicted lock passage time of the ships in each area are displayed independently on their respective maps.

[0087] In some embodiments, optionally, such asFigure 5 As shown, the method for predicting the waiting time for ships at locks also includes: Step S120: Determine at least one characteristic parameter, including ship characteristics, environmental characteristics, and congestion characteristics; Step S122: Determine the expected waiting time for the target vessel based on the trajectory parameters, lock waiting parameters, and characteristic parameters.

[0088] In this embodiment, when predicting the waiting time at the lock, it not only relies on a single waiting parameter but also calculates or acquires dynamically changing features in real time, i.e., feature parameters. Feature parameters include vessel characteristics, environmental characteristics, and congestion characteristics. Vessel characteristics refer to the real-time state of the target vessel, including but not limited to: real-time speed, navigation status (e.g., underway, anchored waiting at the lock), draft, and load. For example, a vessel with extremely slow speed may indicate that it is navigating cautiously or preparing to anchor while waiting at the lock. Environmental characteristics refer to external natural conditions. These are obtained through access to meteorological and hydrological data interfaces, including but not limited to: real-time wind speed, wind direction, visibility, current speed, and water level. For example, heavy fog and low visibility may cause deceleration and prolong waiting time. Congestion characteristics refer to traffic flow conditions. These are based on real-time analysis of current AIS data, including but not limited to: the total number of vessels currently traveling in the same direction within the second area, the number of vessels queuing ahead of the target vessel, and the distribution ratio of different vessel types within the area.

[0089] By combining at least one characteristic parameter as a correction factor with the lock waiting parameters and trajectory parameters, the expected lock waiting time for the target vessel is determined.

[0090] Understandably, combining static historical patterns with dynamic real-time scenarios enables prediction results to respond to instantaneous changes in conditions such as traffic flow and weather, thereby overcoming the lag and bias that may result from relying solely on historical data, and further improving the robustness and accuracy of gate waiting time prediction.

[0091] In some embodiments, the system periodically (e.g., every 5 minutes) reacquires multiple feature parameters, such as congestion characteristics, environmental characteristics, and vessel characteristics, and recalculates the prediction duration. For example, a vessel's estimated lock passage time may be dynamically updated and gradually reduced as vessels ahead of it depart or the weather improves, providing users with the most real-time and realistic prediction.

[0092] In one specific embodiment, optionally, the core steps of the method for predicting the waiting time for ships at locks include: Step 1: Multi-source data acquisition: Dynamic data: Ship AIS trajectory (real-time location, historical trajectory, etc.), meteorological data (wind speed, visibility), lock chamber operating conditions (working status, etc.); Static data: basic ship parameters (ship type, length, beam, etc.), lock structural parameters (lock chamber size, sill depth), and historical lock passage records (ship type priority rules).

[0093] Step 2: Feature Engineering Construction: Spatiotemporal characteristics: real-time distance, speed, and course direction between the ship and the lock; Ship characteristics: hull type, dimensions, load capacity; Environmental characteristics: meteorological parameters, hydrological conditions; Congestion characteristics: historical waiting queue length, and distribution of waiting vessels of the same type.

[0094] Step 3, Prediction Process: (1) Sample ship determination: An electronic fence Z1 is demarcated over the lock chamber area; Vessels that entered the electronic fence Z1 of the lock chamber yesterday (i.e., T-1 day, where T is the current date) are identified as sample vessels.

[0095] (2) Statistical analysis of sample ships: The sample ships are classified according to their ship type; ship types may include: container ships, dry bulk carriers, oil tankers, passenger ships, and new energy ships, etc. Quantity statistics and classification analysis of various types of ships were conducted.

[0096] (3) Determination of the median waiting time for sample vessels at the lock: Electronic fences for anchorage areas Z2 (upward) and Z2 (downward) are demarcated for the upstream and downstream directions of the lock, respectively. The Z2 fence can be divided into multiple zones based on the actual number of vessels waiting to pass through the lock and the waiting area. By querying the time t1 when sample vessel 1 enters anchorage area Z2 and the time t2 when it leaves Z2, the lock waiting time of sample vessel 1 can be obtained as: Δt1 = t2 - t1; Similarly, obtain the waiting time for each of the number n of all vessels passing through the lock for different vessel types yesterday (i.e., day T-1, where T is the current date); Find the median lock time for different ship types, Δt = (Δt1 + Δt2 + ... + Δtn) / n.

[0097] (4) Determination of the target vessel's estimated lock passage time: When the target vessel enters the electronic fence Z2 of the anchorage area, the entry time t' is recorded; Based on the target vessel's type, retrieve the median lock waiting time Δt for the corresponding vessel type from step three; Calculate the estimated lock passage time of the target vessel: Testimated = t' + Δt; The forecast results are output to the system interface or relevant scheduling system to assist in operational decision-making.

[0098] like Figure 9 As shown, the selected lock 100 includes an upstream waiting area 106, a downstream waiting area 104, and a lock chamber passage area 102. Multiple sample vessels 1022 are moored or waiting in the upstream and downstream waiting areas 106 and 104, respectively. Multiple sample vessels 1022 are currently passing through the lock chamber passage area 102. These sample vessels 1022 are categorized by vessel type, and their AIS data can be displayed on the terminal page.

[0099] In one embodiment, alternatively, such as Figure 8 As shown, to implement the above-mentioned method for predicting ship lock waiting time, a corresponding ship lock waiting time prediction system 800 is provided, comprising: Data acquisition and storage module 802: used to receive AIS information, collect meteorological and hydrological data, and collect the status of the gate chamber; Feature Analysis and Statistical Calculation Module 804: Used for ship classification (ship type / direction), constructing spatiotemporal features, and calculating lock median duration in history; Prediction Calculation Module 806: Used to calculate the estimated lock passage time by calling the median duration of the same type of ship when the target ship enters the electronic fence Z2 of the anchorage area; Output module 808: Provides visualization and API interfaces to enable shipping companies, ports, and cargo owners to optimize scheduling.

[0100] Understandably, the ship waiting time prediction system 800 provided by this invention realizes the ship waiting time prediction method. Based on the scalable prediction model of electronic fence and historical statistics, it gets rid of the dependence on a single rule, realizes personalized prediction by ship type and direction, improves matching degree and accuracy, improves the efficiency of ship lock scheduling, and alleviates transportation congestion in inland waterway environment.

[0101] like Figure 6As shown in the embodiment of this application, a vessel waiting time prediction device 900 is also provided. The vessel waiting time prediction device 900 includes: a data acquisition module 902, used to determine navigation data in the Automatic Identification System (AIS); a sample area module 904, used to determine a first area corresponding to the lock passage chamber based on the selected lock; a historical analysis module 906, used to determine historical lock passage records based on navigation data; a sample determination module 908, used to determine sample vessels passing through the first area based on historical lock passage records; a vessel classification module 910, used to classify the sample vessels and determine at least one vessel type; a waiting area module 912, used to determine a second area corresponding to anchoring waiting time based on the selected lock; a time determination module 914, used to determine the waiting time of multiple sample vessels corresponding to the second area; a parameter determination module 916, used to determine the waiting parameters corresponding to each vessel type based on the waiting time; a target vessel module 918, used to determine the target vessel and the trajectory parameters corresponding to the target vessel; and a time prediction module 920, used to determine the expected waiting time and expected passage time of the target vessel based on the trajectory parameters and the waiting parameters.

[0102] like Figure 7 As shown, this application embodiment also provides an electronic device 1000, including a processor 1110, a memory 1109, and a program or instructions stored in the memory 1109 and executable on the processor 1110. When the program or instructions are executed by the processor 1110, they implement the various processes of the above-described ship lock waiting time prediction method and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0103] Optionally, the processor 1110 is used to determine navigation data in the Automatic Identification System (AIS); Optionally, the processor 1110 is also configured to determine a first area corresponding to the lock passage chamber based on the selected lock. Optionally, the processor 1110 is also used to determine historical lock passage records based on navigation data; Optionally, the processor 1110 is also used to determine the sample vessels passing through the first area based on historical lock passage records; Optionally, the processor 1110 is also used to classify the sample ships and determine at least one ship type; Optionally, the processor 1110 is also configured to determine a second area corresponding to the lock such as the anchorage lock based on the selected lock; Optionally, the processor 1110 is also used to determine the lock waiting time for multiple sample vessels corresponding to the second area; Optionally, the processor 1110 is also configured to determine the lock waiting parameters corresponding to each vessel type based on the lock waiting time; Optionally, the processor 1110 is also used to determine the target vessel and the trajectory parameters corresponding to the target vessel; Optionally, the processor 1110 is also used to determine the expected waiting time and expected passage time of the target vessel based on the trajectory parameters and the lock-keeping parameters.

[0104] The memory 1109 can be used to store software programs and various data. The memory 1109 may primarily include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area may store the operating system, application programs or instructions required for at least one function (such as sound playback, image playback, etc.). Furthermore, the memory 1109 may include volatile memory or non-volatile memory, or both. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus RAM (DRRAM). The memory 1109 in this embodiment includes, but is not limited to, these and any other suitable types of memory.

[0105] This application also provides a readable storage medium storing a program or instructions. When executed by a processor, this program or instructions implement the various processes of the above-described ship lock waiting time prediction method embodiments and achieve the same technical effects. To avoid repetition, these will not be described again here. Furthermore, the readable storage medium improves the data storage capacity and data processing speed of the ship lock waiting time prediction method in this application.

[0106] A computer-readable storage medium can be a tangible device that holds and stores instructions for use by an instruction execution device. A computer-readable storage medium can be an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing, but is not limited thereto. A non-exhaustive list of more specific examples of computer-readable storage media includes: portable computer floppy disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable optical disc read-only memory (CD-ROM), digital universal disk (DVD), memory cards, floppy disks, encoding mechanical devices (e.g., punched cards or grooves with raised structures for recording instructions), and any suitable combination of the foregoing. The computer-readable storage medium used herein should not be construed as the transmission of signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media, or electrical signals transmitted through wires.

[0107] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0108] This application also provides a chip, which includes a processor and a communication interface. The communication interface and the processor are coupled. The processor is used to run programs or instructions to implement the various processes of the above-described ship lock waiting time prediction method embodiments, and can achieve the same technical effect. To avoid repetition, it will not be described again here. In addition, the chip improves the data processing speed of the ship lock waiting time prediction method in this application.

[0109] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0110] In this invention, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance; the term "multiple" refers to two or more unless otherwise explicitly defined. The terms "install," "connect," "link," and "fix" should be interpreted broadly. For example, "connect" can be a fixed connection, a detachable connection, or an integral connection; "link" can be a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0111] In the description of this invention, it should be understood that the terms "upper," "lower," "left," "right," "front," "rear," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or unit referred to must have a specific orientation or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0112] In the description of this specification, the terms "one embodiment," "some embodiments," "specific embodiment," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0113] The above are merely preferred embodiments of the present invention and are not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for predicting the waiting time for ships at locks, characterized in that, include: Determine navigation data from the Automatic Identification System (AIS); Determine the first area corresponding to the lock passage chamber based on the selected lock; Historical lock passage records are determined based on the navigation data; The sample vessels that passed through the first area were determined based on the historical lock passage records. The sample ships are classified to determine at least one ship type; Determine the second area corresponding to the anchorage lock based on the selected lock; Determine the lock waiting time for multiple sample vessels corresponding to the second region; Determine the lock-waiting parameters corresponding to each of the vessel types based on the lock-waiting duration; Determine the target vessel and the corresponding trajectory parameters; The expected waiting time and expected passage time of the target vessel are determined based on the trajectory parameters and the lock-keeping parameters.

2. The method for predicting ship lock waiting time according to claim 1, characterized in that, The step of determining the estimated waiting time for the target vessel based on the trajectory parameters and the lock waiting parameters includes: The static data of the target vessel are determined based on the Automatic Identification System (AIS). The target vessel type is determined based on the static data. Determine the lock parameters corresponding to the target vessel type; The expected waiting time and expected passage time of the target vessel are determined based on the trajectory parameters and the lock-keeping parameters corresponding to the target vessel type.

3. The method for predicting ship lock waiting time according to claim 2, characterized in that, The step of determining the estimated waiting time for the target vessel based on the trajectory parameters and the lock-waiting parameters corresponding to the target vessel type includes: The real-time and historical position data of the target vessel are determined based on the trajectory parameters. Determine the boundary of the second region; The entry time of the target vessel is determined based on the real-time location data, the historical location data, and the area boundary. The expected waiting time and expected passage time of the target vessel are determined based on the entry time and the lock-waiting parameters.

4. The method for predicting ship lock waiting time according to claim 3, characterized in that, The step of determining the lock-waiting parameters corresponding to each vessel type based on the lock-waiting duration includes: Determine the historical trajectory data of multiple sample vessels; The first moment when the sample vessel enters the second region is determined based on the historical trajectory data and the regional boundary. The second moment when the sample vessel leaves the second region is determined based on the historical trajectory data and the region boundary. The lock waiting time corresponding to the vessel type is determined based on the first time and the second time. Determine the number of ships for each of the aforementioned ship types; The lock-waiting parameters for each vessel type are determined based on the lock-waiting time and the number of vessels.

5. The method for predicting ship lock waiting time according to claim 1, characterized in that, The second region includes a second uplink region and a second downlink region.

6. The method for predicting ship lock waiting time according to any one of claims 1 to 5, characterized in that, The method for predicting the waiting time for ships at locks also includes: Determine at least one characteristic parameter, which includes ship characteristics, environmental characteristics, and congestion characteristics; The estimated waiting time for the target vessel is determined based on the trajectory parameters, the lock waiting parameters, and the characteristic parameters.

7. A device for predicting the waiting time of ships at locks, characterized in that, include: The data acquisition module is used to determine navigation data in the Automatic Identification System (AIS). The sample area module is used to determine the first area corresponding to the lock passage chamber based on the selected lock. The historical analysis module is used to determine historical lock passage records based on the navigation data; A sample determination module is used to determine sample vessels passing through the first area based on the historical lock passage records; The ship classification module is used to classify the sample ships and determine at least one ship type; The lock waiting area module is used to determine the second area corresponding to the anchoring lock waiting area based on the selected lock. The duration determination module is used to determine the lock waiting time for multiple sample vessels in the second area; The parameter determination module is used to determine the lock waiting parameters corresponding to each of the vessel types based on the lock waiting duration; The target vessel module is used to determine the target vessel and the trajectory parameters corresponding to the target vessel; The duration prediction module is used to determine the expected waiting time and expected passage time of the target vessel based on the trajectory parameters and the lock waiting parameters.

8. An electronic device, characterized in that, It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the ship lock waiting time prediction method as described in any one of claims 1 to 6.

9. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the ship lock waiting time prediction method as described in any one of claims 1 to 6.

10. A chip, characterized in that, The chip includes a processor and a communication interface, the communication interface and the processor being coupled together. The processor is used to run programs or instructions to implement the steps of the ship lock waiting time prediction method as described in any one of claims 1 to 6.

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