A dispatching method and system based on a ship traffic flow prediction model

Abstract

The application discloses a scheduling method and system based on a ship traffic flow prediction model, belongs to the technical field of multi-stage double-lane ship locks, and is used for solving the technical problem that the utilization efficiency of a lock chamber is low and the overall operation efficiency and the synergy of a ship fleet are difficult to improve due to unreasonable ship arrangement plans in the operation process of an existing multi-stage double-lane ship lock. The method comprises the following steps: performing traffic analysis on a ship queue waiting to pass through a water area under the time factor and the length factor, and determining a ship queue state; performing dynamic and static data statistics on all ships in the water area waiting to pass through, and obtaining dynamic attributes and static attributes of each ship; performing interval division on ship flow of the ship queue in the waiting-to-pass area, and obtaining an initial ship interval; performing target optimization scheduling processing on the ship flow in the ship interval, and obtaining final interval ship flow; and generating a ship flow scheduling planning scheme based on the multi-stage double-lane ship lock.

Description

Technical Field

[0001] The present application relates to the field of multi-level dual-route ships, and in particular to a scheduling method and system based on a ship traffic flow prediction model. Background Art

[0002] Inland waterway shipping has the advantages of large transport volume, low energy consumption and less pollution, and occupies an important position in ship transportation.

[0003] In order to improve the current traffic capacity of inland waterways, according to the current waterway hydrological conditions, ship traffic flow, etc., the lock types are divided into single-step single-line locks, single-step multi-line locks, multi-step single-line locks and multi-step multi-line locks. Different types of locks correspond to different ship entry and exit processes, so the corresponding lock scheduling rules are also different.

[0004] A multi-step double-line ship lock refers to a ship lock in the waterway where multiple steps are formed due to the different height differences in the inland river basin. Each step ship lock has two lock chambers, which is a multi-step double-line ship lock. The existing multi-step double-line ship lock usually adopts a dam scheduling method, that is, the two locks do not interfere with each other. However, due to the differences in the lock chamber space and operation processes of different locks, the operation time of ships entering and exiting the two step ship locks has a large difference, which makes it easy for ships to be backlogged and congested in the waterway. Summary of the Invention

[0005] The embodiments of the present application provide a scheduling method and system based on a ship traffic flow prediction model, which is used to solve the following technical problems: during the operation of existing multi-step double-line ship locks, due to unreasonable ship arrangement plans, the utilization efficiency of the lock chamber is low, making it difficult to improve the overall operational efficiency and coordination of the fleet passage.

[0006] The embodiments of this application adopt the following technical solutions:

[0007] In one aspect, the embodiment of the present application provides a scheduling method based on a ship traffic flow prediction model, comprising: according to a ship scheduling time threshold, performing traffic analysis on a ship queue in a waiting water area under time factor and queue length factor to determine a ship queue state; if the ship queue state is a ship congestion state, performing dynamic and static data statistics on all ships in the waiting water area to obtain dynamic attributes and static attributes of each ship; according to a lock chamber space parameter and based on the dynamic attributes and the static attributes, performing interval division on ship flow of the ship queue in the waiting water area to obtain an initial ship interval; through a preset ship traffic flow prediction model, performing target optimization scheduling processing on ship flow in the initial ship interval to obtain a final interval ship flow; and based on the final interval ship flow, generating a ship flow scheduling planning scheme based on a multi-stage double-line ship lock.

[0008] The embodiment of the present application can more effectively predict ship traffic conditions by analyzing the time factor and the queue length factor of the ship queue, thereby reducing the waiting time of the ship and improving the efficiency of water area traffic. Through the statistics of dynamic and static data, the characteristics of each ship can be more accurately understood, which helps to take targeted scheduling strategies and relieve congestion. Based on the lock chamber space parameter and the ship attributes, the interval division of the ship flow is helpful to more reasonably allocate the lock chamber resources and improve the use efficiency of the lock chamber. The use of the preset ship traffic flow prediction model can target the optimization scheduling of the ship flow, thereby improving the accuracy of the scheduling plan. It can better adapt to the traffic demand of different ships and reduce the uncertainty in the scheduling process. Through the optimization of ship scheduling, the energy consumption of the ship in the waiting and low-speed navigation can be reduced. Reasonable ship scheduling can reduce the collision risk between ships and improve the navigation safety.

[0009] In a feasible implementation manner, based on the ship scheduling time threshold, a passage analysis is performed on the ship queue waiting for passage under time factors and length factors to determine the state of the ship queue, specifically including: determining the ship scheduling time threshold between the waiting waters and the first lock based on the scale factor of the port to be passed; wherein the ship scheduling time threshold is the longest waiting time interval of the first lock; through a preset waterway sensing system and based on the ship scheduling time threshold, a preliminary scan is performed on the ships waiting for passage to determine the previous ship flow information at the previous time node; based on the current time node, the current ship flow information in the waiting waters is scanned and collected; the previous ship flow information is numerically compared with the current ship flow information to determine a flow comparison value; if the flow comparison value is greater than or equal to the first preset threshold, the ship queue state at the current time node is defined as the ship congestion state; if the flow comparison value is less than the first preset threshold, the ship queue state at the current time node is determined as the ship uncrowded state.

[0010] In a feasible implementation manner, if the ship queue state is a crowded ship state, dynamic and static data statistics are performed on all ships in the waiting passage waters to obtain dynamic and static attributes of each ship, specifically including: if the ship queue state is a crowded ship state, the image acquisition route planning and processing of the drone is performed on the waiting passage area to obtain a drone aerial photography route; wherein, the drone aerial photography route is a full ship image acquisition route with the shortest time consumption; through the drone and based on the drone aerial photography route, image acquisition and processing are performed on all ships in the waiting passage area to obtain a ship static information image; through a preset search box, all ship pixels contained in the ship static information image are rectangularly framed and marked, and based on the foreground and background layers of the ship static information image, the image connected to the ship bottom mapping is mapped. The water area and the ship area itself are both pixel segmented to obtain the draft area image and the ship itself image; the ship itself image and the corresponding draft area image are spliced ​​to obtain the image of the area actually occupied by the ship; the key feature information of the image in the actual ship occupied area image is matched with the key information features in the pre-stored ship declaration information to determine the static attributes of all ships in the waiting waters; wherein, the static attributes include: ship type, ship size, ship dimensions, draft depth, type of cargo carried and special ship marks; dynamic information analysis of all ships in the waiting area for passage is performed on the navigation conditions and waiting time, to determine the dynamic attributes of all ships; the dynamic attributes and static attributes are mounted on each ship corresponding to the image of the actual ship occupied area.

[0011] In a feasible implementation, dynamic information analysis of the navigation conditions and waiting time of all ships in the waiting area is performed to determine the dynamic attributes of all ships, specifically including: obtaining the current flow information of the ships in the waiting area through the drone; performing navigation capacity analysis on the ship type and the draft in the static attributes of each ship to obtain the navigation capacity of each ship; wherein, the smaller the ship type and the shallower the draft, the stronger the navigation capacity corresponding to the ship; performing numerical comparison and judgment on the current flow information and the navigation capacity of each ship to obtain the navigation capacity of each ship. The actual navigation intensity of the ship; wherein, the greater the ratio of the value of the navigation capacity to the data of the current flow information, the higher the actual navigation intensity of the ship; within the ship scheduling time threshold, the waiting time of each ship in the waiting area is counted, and the special ship mark in the static attribute is combined with the waiting time for data processing to determine the waiting time of each ship; wherein, the waiting time is the time result information after the special ship mark and the waiting time are weighted; the actual navigation intensity and the waiting time of each ship are combined to obtain the dynamic attribute.

[0012] In a feasible embodiment, according to the lock chamber space parameters and based on the dynamic attributes and the static attributes, the ship queue in the waiting passage area is divided into intervals of ship flow to obtain an initial ship interval, which specifically includes: extracting the lock chamber space parameters; wherein the lock chamber space parameters at least include: water level difference parameters, space plane length and width parameters, gate number parameters and gate opening number parameters; based on the lock chamber space parameters and the current flow information of the ship, the ship queue in the waiting passage area is divided into diversion zones to determine the initial queue diversion line; several ships located in the left and right edge areas of the initial queue diversion line are determined as pending ships; the dynamic attributes and static attributes of the pending ships are obtained; the waiting time and actual navigation intensity of the dynamic attributes of each pending ship are scored and processed through preset expert scoring rules to obtain a dynamic score; the static attributes of each pending ship are scored and processed to obtain a dynamic score. The weight ratios of the various attribute items are divided to determine the static weight ratios; if the static attributes include special ship marks, the special ship marks will be re-weighted based on the current traffic information in the waiting area to obtain special weight ratios; the static attributes of each of the pending ships are scored using the static weight ratios and the special weight ratios to obtain a static score; the static scores and the dynamic scores are integrated and sorted to obtain the attribute priority of each of the pending ships; according to the priority order of the attribute priorities, the ship size of the pending ship is matched with the space parameters of the lock chamber, and the pending ship after the spatial parameter matching is passed is determined as a passable ship; based on the position information of the passable ship, the initial queue diversion line is corrected to generate the initial ship interval containing all passable ships.

[0013] In a feasible embodiment, before the ship traffic flow in the initial ship interval is subjected to target optimization scheduling processing through a preset ship traffic flow prediction model to obtain the final interval ship traffic flow, the method further includes: obtaining historical initial queue diversion line data, historical attribute priority data, historical passable ship data and historical initial ship interval data; using the historical initial queue diversion line data, the historical attribute priority data and the historical passable ship data as inputs of a neural network model; and using the historical initial ship interval data as outputs of the neural network model; performing model training processing on the neural network model based on the training set and validation set of the inputs and based on the output to obtain a ship interval prediction model; generating a ship traffic algorithm based on the number of ships identified by the drone in the historical initial ship interval and the ship scheduling time threshold; combining the ship traffic algorithm with the ship interval prediction model to obtain the ship traffic flow prediction model.

[0014] In a feasible implementation, the ship traffic in the initial ship interval is subjected to target optimization scheduling processing through a preset ship traffic flow prediction model to obtain the final interval ship traffic, specifically including: obtaining the initial ship interval at the current time node; inputting the initial queue diversion line data, attribute priority data and passable ship data corresponding to the initial ship interval into the ship traffic flow prediction model to obtain the final ship interval; wherein, the final ship interval is the interval after the data of the initial ship interval is secondary corrected; based on the ship traffic flow algorithm of the ship traffic flow prediction model, the flow of the final ship interval is calculated to obtain the final interval ship traffic.

[0015] In a feasible implementation, based on the ship flow in the final interval, a ship flow scheduling planning scheme based on a multi-step double-line ship lock is generated, specifically including: identifying all schedulable ships in the ship flow in the final interval; according to the ship arrangement corresponding to the lock chamber space parameters and based on the actual traffic intensity of the static attributes in the schedulable ships, the scheduling information is sent to the schedulable ships respectively to generate the ship flow scheduling planning scheme.

[0016] In a feasible implementation manner, the dispatch information is sent in the form of text messages, radio broadcasts, and radar signal identification.

[0017] On the other hand, an embodiment of the present application also provides a scheduling system based on a ship traffic flow prediction model, including: a queue acquisition module, which is used to perform a traffic analysis on the ship queue waiting for passage under time factors and length factors according to the ship scheduling time threshold, and determine the ship queue status; a ship data analysis module, which is used to perform dynamic and static data statistics on all ships in the waiting waters if the ship queue status is a ship congestion state, and obtain the dynamic attributes and static attributes of each ship; according to the lock chamber space parameters, and based on the dynamic attributes and the static attributes, the ship queue in the waiting passage area is divided into intervals of ship flow to obtain an initial ship interval; a ship flow calculation module, which is used to perform target optimization scheduling processing on the ship flow in the initial ship interval through a preset ship traffic flow prediction model to obtain the final interval ship flow; a planning scheme generation module, which is used to generate a ship flow scheduling planning scheme based on a multi-step double-line lock based on the final interval ship flow.

[0018] This application provides a scheduling method and system based on a ship traffic flow prediction model. Compared with the existing technology, the embodiments of this application have the following beneficial technical effects:

[0019] 1. Optimize ship traffic efficiency: By analyzing the time factors and captain factors of ship queues, ship traffic conditions can be predicted more effectively, thereby reducing ship waiting time and improving water traffic efficiency.

[0020] 2. Alleviate ship congestion: When a ship queue is in a congested state, statistics on dynamic and static data can provide a more accurate understanding of the characteristics of each ship, helping to adopt targeted scheduling strategies and alleviate congestion.

[0021] 3. Improve lock chamber utilization: Based on lock chamber space parameters and ship attributes, ship flow is divided into intervals, which helps to more reasonably allocate lock chamber resources and improve lock chamber utilization efficiency.

[0022] 4. Prediction and scheduling accuracy: Using the preset ship traffic flow prediction model, the ship traffic can be targeted and optimized, thereby improving the accuracy of the scheduling plan.

[0023] 5. Generate an optimized scheduling plan: The final generated ship flow scheduling plan based on a multi-stage double-line lock can better adapt to the passage needs of different ships and reduce uncertainty in the scheduling process.

[0024] 6. Reduce energy consumption: By optimizing ship scheduling, the energy consumption of ships during waiting and low-speed sailing can be reduced.

[0025] 7. Improve navigation safety: Reasonable ship scheduling can reduce the risk of collision between ships and improve navigation safety.

[0026] 8. Improve the intelligence level of the management system: The implementation of this method helps to improve the intelligence level of the ship traffic management system and achieve more efficient and intelligent ship scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments described in the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0028] Figure 1 A flow chart of a scheduling method based on a ship traffic flow prediction model provided in an embodiment of the present application;

[0029] Figure 2 A schematic diagram of a multi-step double-line ship lock provided in an embodiment of the present application;

[0030] Figure 3A schematic structural diagram of a scheduling device based on a ship traffic flow prediction model provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the technical solutions in this application, the following will clearly and completely describe the technical solutions in the embodiments of this application in conjunction with the drawings in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0032] The present application embodiment provides a scheduling method based on a ship traffic flow prediction model, such as Figure 1 As shown, the scheduling method based on the ship traffic flow prediction model specifically includes steps S101-S105:

[0033] It should be noted that the multi-tiered lock system is located along numerous ports, and the constant flow of ships entering and leaving ports for loading and unloading operations results in a fluid and dynamic flow of vessels within the waterway. This leads to deviations in the scheduling plans for the upstream and downstream locks. Furthermore, because different locks are affected by water levels, their ability to guide ships through the locks varies. These internal and external factors significantly impact the daily scheduling of these locks. Therefore, it is necessary to utilize sensing systems (big data and the Internet of Things) along the waterway to scan vessels within the waterway, collect, analyze, and process the data, detect the number of vessels in real time, and promptly analyze static information about the vessels (size, draft, etc.) to formulate dynamic, real-time lock scheduling plans.

[0034] Locks generate scheduling plans based on existing waterway vessels. When selecting vessels, many issues often need to be considered, such as long waiting times, crew complaints caused by relatively unreasonable and unfair lock rankings, and local environmental instability caused by an excessive number of waiting vessels. It is now necessary to fully consider the impact of ship selection and ship combination scheduling plans on waiting times and crew satisfaction, and propose scheduling decision-making technologies with comprehensive objective optimization to improve lock operation efficiency and service quality, while ensuring fairness for ships.

[0035] During lock scheduling, the flow of ships in the waterway varies depending on season, cargo flow, crew habits, and scheduling methods. This discrepancy in the division of uniform time periods creates a thorny issue for lock scheduling. This can lead to ships waiting for locks, which in turn can lead to wasted resources while the locks wait for ships. Time-sharing scheduling technology based on time-sharing flow division is now needed. This technology can determine ship scheduling times based on different traffic flows and ship types, and strike a balance between ship waiting times and lock waiting times.

[0036] S101, according to the ship scheduling time threshold, the ship queue waiting for passing through the water area is analyzed in time factor and queue length factor, and the ship queue state is determined.

[0037] Specifically, first, the ship scheduling time threshold between the waiting passing water area and the first ship lock is determined based on the scale factor of the port to be passed. The ship scheduling time threshold is the longest waiting time interval of the first ship lock. Then, the preset channel along the line sensing system is scanned, and the last ship flow information at the last time node is determined based on the ship scheduling time threshold.

[0038] Further, based on the current time node, the current ship flow information in the waiting passing water area is scanned and collected.

[0039] Further, the last ship flow information and the current ship flow information are compared and judged to determine the flow comparison value.

[0040] Further, if the flow comparison value is greater than or equal to the first preset threshold, the ship queue state at the current time node is defined as the ship crowded state. If the flow comparison value is less than the first preset threshold, the ship queue state at the current time node is determined as the ship non-crowded state.

[0041] In one embodiment, Figure 2 A multi-stage double-line ship lock schematic diagram is provided for the embodiment of the application, as shown in Figure 2 The channel design of the unmanned aerial vehicle is used to collect the ship conditions of the waiting passing area of the ship preparing to pass through No. 1 ship lock to No. 1 lock chamber (first ship lock), and the waiting condition of the ship is grasped based on the ship scheduling time threshold, so as to judge the ship queue state. Then, after the numerical comparison and judgment of the preliminary estimated last ship flow information and the current ship flow information, the ship queue state is finally obtained.

[0042] S102, if the ship queue state is the ship crowded state, the dynamic and static data of all ships in the waiting passing water area are counted to obtain the dynamic and static attributes of each ship.

[0043] Specifically, if the ship queue state is the ship crowded state, the image collection route planning of the unmanned aerial vehicle is processed for the waiting passing area to obtain the unmanned aerial vehicle aerial route. The unmanned aerial vehicle aerial route is the ship image full collection route with the shortest time consumption.

[0044] Further, the image collection processing of all ships in the waiting passing area is performed by the unmanned aerial vehicle based on the unmanned aerial vehicle aerial route to obtain the ship static information image.

[0045] Further, all ship pixels contained in the ship static information image need to be processed by the preset search box, and the pixel segmentation processing is performed on the ship bottom mapping connected water area and the ship itself area based on the foreground and background layers of the ship static information image, so as to obtain the water area image and the ship itself image.

[0046] Further, the ship itself image and the corresponding water area image are subjected to image splicing processing to obtain the actual ship occupied area image. The image key feature information in the actual ship occupied area image is matched with the key information feature in the pre-stored ship declaration information to determine the static attributes of all ships in the waiting navigation water area. The static attributes include ship type, ship size, ship size, water depth, cargo type and special ship marker.

[0047] Further, the dynamic information of all ships in the waiting navigation area needs to be analyzed in relation to the navigation condition and the waiting time, so as to determine the dynamic attributes of all ships.

[0048] As a feasible implementation, the current flow information of the ships in the waiting navigation area can be obtained by the unmanned aerial vehicle. Then, the ship type and the water depth in the static attribute of each ship are analyzed for the navigation capacity to obtain the navigation passing capacity of each ship. The smaller the ship type and the shallower the water depth, the stronger the corresponding navigation passing capacity. Then, the current flow information and the navigation passing capacity of each ship are compared and judged to obtain the actual navigation strength of each ship. The greater the ratio of the value of the navigation passing capacity to the data of the current flow information, the higher the actual navigation strength of the ship. Within the ship scheduling time threshold, the waiting time of each ship in the waiting navigation area is counted, and the special ship marker in the static attribute and the waiting time are combined to determine the waiting time of each ship. The waiting time is the time result information obtained by weight allocation of the special ship marker and the waiting time. Finally, the actual navigation strength and the waiting time of each ship are combined to obtain the dynamic attribute.

[0049] Finally, the dynamic attribute and the static attribute are mounted to each ship corresponding to the actual ship occupied area image.

[0050] In one embodiment, the priority of the ship scheduling is the key of the ship ordering. In the priority rule setting, the ship ordering rule is based on the principle of first come first served and priority. The ship is arranged according to the ship number and the water level. The static and dynamic properties of the ship waiting for the lock are also considered. The static property mainly refers to the inherent property of the ship, that is, the property of the ship which does not change after the ship declares to pass through the lock, such as the size of the ship, the type of cargo and the like. The dynamic property is the property which changes with time and external environment, such as the navigation condition (in the case of abundant navigation capacity, the weight of the local company's turning lock tug is greater than that of the general cargo ship. In the case of insufficient navigation capacity, the weight of the general cargo ship should be greater than that of the turning lock tug) and the waiting time for the lock (the longer the waiting time for the lock, the greater the weight, and the higher the level of the ship, the faster the weight increases). By weighing the static and dynamic properties of the ship, a relatively reasonable and fair ship priority order is obtained.

[0051] In S103, according to the lock chamber space parameters and based on the dynamic properties and the static properties, the ship flow interval of the ship queue in the waiting area is divided to obtain the initial ship interval.

[0052] Specifically, the ship lock management system is used to extract the lock chamber space parameters. The lock chamber space parameters at least include the water level difference parameter, the space plane length and width parameter, the number of lock gates parameter and the number of open lock gates parameter. Based on the lock chamber space parameters and the current flow information of the ship, the ship queue of the waiting area is divided to determine the initial queue shunt line.

[0053] As a feasible implementation, the intelligent ship lock scheduling rule is constructed according to the hard condition restrictions such as the size of the lock chamber, the ship passing through the lock time and the heading. Under the premise of trying to ensure the load balance of multiple lock chambers and the reasonableness of the ship delay, the scheduling operation plan of the ship declaring to pass through the lock is arranged to maximize the operation benefit and service level. The lock chamber arrangement, ship ordering and lock chamber arrangement level are improved.

[0054] Further, a plurality of ships located at the left and right edge regions of the initial queue shunt line are determined as the pending ships, and the dynamic properties and the static properties of the pending ships are obtained.

[0055] Further, the waiting time for the lock and the actual navigation intensity of the dynamic properties of each pending ship are scored by the preset expert scoring rule to obtain the dynamic score.

[0056] Furthermore, the static attributes of each pending vessel are weighted and their respective sub-items are assigned a specific weight ratio. If the static attributes include a special vessel flag, the special vessel flag is re-weighted based on current traffic information in the waiting area to determine a specific weight ratio. Finally, the static attributes of each pending vessel are scored using the static weight ratios and the special weight ratios to obtain a static score.

[0057] In one embodiment, some ships that are capable of transporting special cargoes or carrying special missions also need to be given priority, so the static attributes of the ship need to be re-weighted according to the special ship marks. The total static attributes then include the static weight ratios of each item and the static score under the special weight ratios.

[0058] Furthermore, the static and dynamic scores are combined and ranked to determine the attribute priority of each pending vessel. Based on the attribute priority order, the vessel's dimensions are then matched with the lock chamber's spatial parameters. Only those vessels that pass the spatial parameter match are considered passable. Finally, based on the positional information of passable vessels, the initial queue diversion lines are corrected, and an initial ship interval containing all passable vessels is generated.

[0059] In one embodiment, ships with high scores, i.e., ships with higher attribute priorities, are first matched with the space parameters of the lock chamber. If all meet the requirements, these ships are used as the last pending ships to be divided into the left and right edge areas of the initial queue diversion line, thereby ensuring the fairness of ships and the utilization efficiency of the lock chamber to the greatest extent possible, achieving the greatest possible balance, and only after the final passable ships are determined can the initial ship interval containing all passable ships be generated, that is, the diversion line and diversion area are finally divided.

[0060] S104: Using a preset ship traffic flow prediction model, target optimization scheduling is performed on the ship traffic in the initial ship interval to obtain the final interval ship traffic.

[0061] Specifically, first obtain historical initial queue diversion line data, historical attribute priority data, historical passable ship data and historical initial ship interval data.

[0062] Furthermore, historical initial queue diversion line data, historical attribute priority data, and historical passable vessel data are used as inputs to the neural network model. Historical initial vessel interval data is used as the output of the neural network model. The neural network model is then trained based on the training and validation sets of the inputs and the outputs to produce a vessel interval prediction model.

[0063] Furthermore, a ship flow algorithm is generated based on the number of ships identified by the UAV in the historical initial ship interval and the ship scheduling time threshold.

[0064] Furthermore, it is necessary to combine the ship traffic flow algorithm with the ship interval prediction model to obtain a ship traffic flow prediction model.

[0065] Furthermore, the initial vessel interval at the current time node is obtained. The initial queue diversion line data, attribute priority data, and passable vessel data corresponding to this initial vessel interval are input into the vessel traffic flow prediction model to obtain the final vessel interval. The final vessel interval is the interval after the initial vessel interval data has been secondary corrected.

[0066] Furthermore, based on the ship traffic flow algorithm of the ship traffic flow prediction model, the flow of the final ship interval is calculated to obtain the final interval ship flow.

[0067] S105. Based on the final interval ship flow, generate a ship flow scheduling planning scheme based on a multi-stage double-line ship lock.

[0068] Specifically, all dispatchable vessels in the final interval vessel flow are first identified. Then, based on the ship arrangement corresponding to the lock chamber space parameters and the actual traffic intensity of the dispatchable vessels' static attributes, dispatch information is sent to each dispatchable vessel to generate a vessel flow dispatch plan. Dispatching information can be sent via text message, radio broadcast, and radar signal identification.

[0069] In addition, the present application also provides a scheduling system based on a ship traffic flow prediction model, such as Figure 3 As shown, the scheduling system 300 based on the ship traffic flow prediction model specifically includes:

[0070] The queue collection module 310 is used to analyze the passage of the ship queue waiting for passage in the water area under the factors of time and length according to the ship scheduling time threshold, and determine the ship queue status;

[0071] The ship data analysis module 320 is configured to collect dynamic and static data statistics of all ships in the waiting area if the ship queue status is congested, and obtain dynamic and static attributes of each ship; and to divide the ship queue in the waiting area into intervals of ship flow based on the dynamic and static attributes according to the lock chamber space parameters, and obtain initial ship intervals;

[0072] The vessel traffic flow calculation module 330 is used to perform target optimization scheduling processing on the vessel traffic flow in the initial vessel interval using a preset vessel traffic flow prediction model to obtain the vessel traffic flow in the final interval;

[0073] The planning scheme generating module 340 is used to generate a ship flow scheduling planning scheme based on a multi-step double-line ship lock based on the final ship flow.

[0074] The embodiments of the present application can more effectively predict the passage of ships by analyzing the time factors and the length factors of the ship queue, thereby reducing the waiting time of ships and improving the efficiency of passage in waters. Through the statistics of dynamic and static data, the characteristics of each ship can be understood more accurately, which helps to adopt targeted scheduling strategies to alleviate congestion. Based on the lock chamber space parameters and ship attributes, the ship flow is divided into intervals, which helps to more reasonably allocate lock chamber resources and improve the efficiency of lock chamber utilization. Using the preset ship traffic flow prediction model, the ship flow can be targeted and optimized, thereby improving the accuracy of the scheduling plan. It can better adapt to the passage needs of different ships and reduce the uncertainty in the scheduling process. By optimizing ship scheduling, the energy consumption of ships during waiting and low-speed navigation can be reduced. Reasonable ship scheduling can reduce the risk of collision between ships and improve navigation safety.

[0075] The various embodiments in this application are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences from other embodiments. In particular, the system storage medium embodiment is generally similar to the method embodiment, so its description is relatively simple. For relevant parts, refer to the description of the method embodiment.

[0076] The foregoing description describes specific embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0077] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the embodiments of the present application may have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included within the scope of the claims of the present application.

Claims

1. A scheduling method based on a ship traffic flow prediction model, characterized in that: The method comprises: Based on the ship dispatch time threshold, the ship queue waiting for passage is analyzed under the factors of time and length to determine the status of the ship queue; If the ship queue state is a ship congestion state, dynamic and static data statistics are collected for all ships in the waiting waters to obtain dynamic and static attributes of each ship; According to the lock chamber space parameters and based on the dynamic attributes and the static attributes, the ship queue in the waiting water area is divided into intervals of ship flow to obtain an initial ship interval, specifically including: Extracting the lock chamber space parameters; wherein the lock chamber space parameters include at least: water level drop parameters, space plane length and width parameters, gate number parameters, and gate opening number parameters; Based on the lock chamber space parameters and the current flow information of the ship, the ship queue is divided into different groups in the waiting passage waters, and an initial queue diversion line is determined; Determine several ships located at the left and right edge areas of the initial queue diversion line as pending ships; Obtaining dynamic attributes and static attributes of the pending ship; The dynamic attributes of each pending ship, such as waiting time at the lock and actual navigation intensity, are scored using the preset expert scoring rules to obtain a dynamic score. The static attributes of each pending vessel are divided into weighted proportions to determine the weight proportions of each static attribute; if the static attributes include a special vessel mark, the special vessel mark is re-weighted based on the current flow information in the waiting waters to obtain a special weight proportion; Scoring the static attributes of each of the pending ships using the static weight ratios and the special weight ratios to obtain a static score; Integrate and sort the static scores and the dynamic scores to obtain the attribute priority of each of the pending ships; performing spatial parameter matching on the ship size of the pending ship and the space parameters of the lock chamber according to the priority order of the attribute priorities, and determining the pending ship after the spatial parameter matching passes as a passable ship; Based on the position information of the passable ships, the initial queue diversion line is corrected, and the initial ship section including all passable ships is generated; By using a preset ship traffic flow prediction model, the ship traffic flow in the initial ship interval is subjected to target optimization scheduling processing to obtain the final interval ship traffic flow; Based on the final interval ship flow, a ship flow scheduling planning scheme based on a multi-step double-line ship lock is generated.

2. The scheduling method based on the ship traffic flow prediction model according to claim 1 is characterized in that: Based on the ship dispatch time threshold, the ship queue waiting for passage is analyzed under the factors of time and length to determine the status of the ship queue, including: Determine a ship dispatching time threshold between the waiting passage waters and the first ship lock based on the scale of the port to be passed; wherein the ship dispatching time threshold is the longest waiting time interval of the first ship lock; Through a preset sensing system along the waterway and based on the ship scheduling time threshold, a preliminary scan is performed on the ships waiting to pass through the waters to determine the previous ship flow information at the previous time node; Scan and collect current ship traffic information in the waiting waters based on the current time node; Comparing the previous ship flow information with the current ship flow information to determine a flow comparison value; If the flow comparison value is greater than or equal to a first preset threshold, defining the ship queue state at the current time node as the ship congestion state; If the flow comparison value is less than the first preset threshold, the ship queue state at the current time node is determined to be a non-crowded ship state.

3. The scheduling method based on the ship traffic flow prediction model according to claim 1 is characterized in that: If the ship queue state is a crowded state, dynamic and static data statistics are collected for all ships in the waiting waters to obtain dynamic and static attributes of each ship, including: If the ship queue state is a crowded state, a drone image acquisition route planning process is performed on the waiting waters to obtain a drone aerial photography route; wherein the drone aerial photography route is a full ship image acquisition route with the shortest time consumption; Using the drone and based on the drone's aerial photography route, image acquisition and processing are performed on all ships in the waiting waters to obtain static information images of the ships; Through a preset search box, all ship pixels contained in the ship static information image are rectangularly framed and marked, and based on the foreground and background layers of the ship static information image, the draft area connected to the ship bottom mapping and the ship area itself are pixel-segmented to obtain the draft area image and the ship image; Performing image stitching processing on the image of the ship itself and the corresponding image of the draft area to obtain an image of the area actually occupied by the ship; Matching key image feature information in the image of the actual vessel-occupied area with key information features in pre-stored vessel declaration information to determine static attributes of all vessels in the waiting passage waters; wherein the static attributes include: vessel type, vessel size, vessel dimensions, draft, cargo type, and special vessel markings; Analyzing the navigation conditions and dynamic information of all ships in the waiting waters during the waiting time to determine the dynamic attributes of all ships; The dynamic attributes and the static attributes are mounted on each ship corresponding to the image of the area occupied by the actual ship.

4. The scheduling method based on the ship traffic flow prediction model according to claim 3 is characterized in that: Analyze the navigation conditions and dynamic information of all ships in the waiting waters during the waiting time to determine the dynamic attributes of all ships, including: Obtaining, by means of the drone, current flow information of the vessel in the waiting waters; Performing a navigability analysis on the ship type and the draft in each ship's static attributes to obtain the navigability of each ship; wherein the smaller the ship type and the shallower the draft, the greater the navigability corresponding to the ship; Comparing the current flow information with the navigation capacity of each ship to determine the actual navigation intensity of each ship; wherein, the greater the ratio of the navigation capacity to the current flow information, the higher the actual navigation intensity of the ship; Within the vessel scheduling time threshold, the waiting time of each vessel in the waiting passage waters is counted, and the special vessel mark in the static attribute is combined with the waiting time to perform data processing to determine the waiting time of each vessel; wherein the waiting time is the time result information after weighting the special vessel mark and the waiting time; The actual navigation intensity and the waiting time of each ship are combined to obtain the dynamic attribute.

5. The scheduling method based on the ship traffic flow prediction model according to claim 1 is characterized in that: Before performing target optimization scheduling processing on the ship traffic flow in the initial ship interval using a preset ship traffic flow prediction model to obtain the final interval ship traffic flow, the method further includes: Obtain historical initial queue diversion line data, historical attribute priority data, historical passable ship data, and historical initial ship interval data; The historical initial queue diversion line data, the historical attribute priority data, and the historical passable ship data are used as inputs of a neural network model; and the historical initial ship interval data are used as outputs of the neural network model; Performing model training processing on the neural network model according to the training set and the validation set of the input amount and based on the output amount to obtain a ship interval prediction model; generating a ship flow algorithm based on the number of ships identified by the drone in the historical initial ship interval and the ship scheduling time threshold; The ship traffic flow prediction model is obtained by combining the ship traffic flow algorithm with the ship interval prediction model.

6. The scheduling method based on the ship traffic flow prediction model according to claim 1 is characterized in that: Using a preset ship traffic flow prediction model, target optimization scheduling is performed on the ship traffic in the initial ship interval to obtain the final interval ship traffic, specifically including: Obtaining the initial ship interval at the current time node; Inputting the initial queue diversion line data, attribute priority data, and passable ship data corresponding to the initial ship interval into the ship traffic flow prediction model to obtain a final ship interval; wherein the final ship interval is the interval after the data of the initial ship interval is twice corrected; Based on the ship traffic flow algorithm of the ship traffic flow prediction model, flow calculation is performed on the final ship interval to obtain the ship flow of the final interval.

7. The scheduling method based on the ship traffic flow prediction model according to claim 1 is characterized in that: Based on the final interval ship flow, a ship flow scheduling planning scheme based on a multi-step double-line ship lock is generated, specifically including: Identifying all dispatchable vessels in the final interval vessel flow; According to the ship arrangement corresponding to the lock chamber space parameters and based on the actual traffic intensity of the static attributes of the schedulable ships, the scheduling information is sent to the schedulable ships respectively to generate the ship flow scheduling planning scheme.

8. The scheduling method based on the ship traffic flow prediction model according to claim 7 is characterized in that: The dispatch information may be sent in the form of text messages, radio broadcasts, and radar signal identification.

9. A scheduling system based on a ship traffic flow prediction model, characterized in that: The system comprises: The queue collection module is used to analyze the passage of ships waiting to pass through the waters based on the time factor and the length factor according to the ship scheduling time threshold, and determine the ship queue status; The ship data analysis module is used for, if the ship queue state is a ship congestion state, to perform dynamic and static data statistics on all ships in the waiting waters to obtain the dynamic attributes and static attributes of each ship; according to the lock chamber space parameters, and based on the dynamic attributes and the static attributes, to divide the ship queue in the waiting waters into intervals of ship flow to obtain the initial ship interval, specifically including: extracting the lock chamber space parameters; wherein the lock chamber space parameters at least include: water level drop parameters, space plane length and width parameters, gate number parameters and gate opening number parameters; based on the lock chamber space parameters and the current flow information of the ship, to divert the ship queue in the waiting waters to determine the initial queue diversion line; to determine several ships located in the left and right edge areas of the initial queue diversion line as pending ships; to obtain the dynamic attributes and static attributes of the pending ships; and to score the waiting time and actual navigation intensity of the dynamic attributes of each pending ship through the preset expert scoring rules. Perform scoring processing to obtain a dynamic score; divide the various attribute items of the static attribute of each pending ship into weight ratios to determine the static weight ratios; if the static attribute includes a special ship mark, the special ship mark will be re-weighted based on the current flow information in the waiting waters to obtain a special weight ratio; score the static attribute of each pending ship according to the static weight ratios and the special weight ratio to obtain a static score; integrate and sort the static score and the dynamic score to obtain the attribute priority of each pending ship; according to the priority order of the attribute priority, perform spatial parameter matching on the ship size of the pending ship and the space parameter of the lock chamber, and determine the pending ship after the spatial parameter matching passes as a passable ship; based on the position information of the passable ship, perform diversion line correction processing on the initial queue diversion line, and generate the initial ship interval containing all passable ships; a ship traffic flow calculation module, configured to perform target optimization scheduling processing on the ship traffic flow in the initial ship interval using a preset ship traffic flow prediction model to obtain the final interval ship traffic flow; A planning scheme generating module is used to generate a ship flow scheduling planning scheme based on a multi-step double-line ship lock based on the final interval ship flow.

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