An Optimization Method for the Efficiency of Autonomous-Traditional Ship Mixed Navigation in Port Waters under Interactive Risk Constraints

Abstract

This invention discloses a method for optimizing the mixed navigation efficiency of autonomous and conventional vessels in port waters under interactive risk constraints, belonging to the field of vessel traffic organization optimization in port waters. The method includes: collecting static and dynamic information of vessels in the intersection area through AIS base stations to obtain the maximum number of vessels and real-time motion parameters, thus obtaining the basic interaction frequency; simultaneously collecting tidal forecasts, one-way navigation periods, and hydro-meteorological parameters; based on the maximum number of vessels and the basic interaction frequency, obtaining the upper limit of the interaction frequency for autonomous vessels through a safety reduction coefficient; establishing safety distance constraints, vessel peak-shifting constraints, one-way navigation constraints, and tide-following time window limits in conjunction with a vessel domain model; constructing a mixed-integer linear programming model using the upper limit of the interaction frequency and various constraints; solving the model using a solver to obtain the vessel entry and exit timing and speed commands, thereby adjusting the mixed navigation efficiency. This invention balances safety and efficiency under interactive risk constraints, improving navigation performance in mixed navigation scenarios.

Description

Technical Field

[0001] This invention relates to the field of vessel traffic organization optimization in port waters, and more particularly to a method for optimizing the efficiency of autonomous-traditional mixed navigation in port waters under interactive risk constraints. Background Technology

[0002] The expansion of port scale and resource integration have exacerbated the complexity of waterway navigation, forming a comprehensive waterway pattern in which diverse navigation units (such as channels and confluence areas), vessel traffic flow, and port environment are deeply intertwined. At the same time, the commercial prospects of autonomous vessels are becoming increasingly clear, and autonomous intelligent vessels are gradually being put into operation, marking my country's entry into the leading ranks in the field of intelligent shipping. Vessels navigating in complex port waters need to be planned and dispatched in a unified manner. With the continued development of the gradual mixed navigation trend, incorporating multi-mode autonomous vessels into dispatching instructions will form a new mixed navigation organization paradigm. Autonomous and traditional vessels have characteristics such as differences in perception and response, complex communication environment, heterogeneous operation modes, and asymmetric information interaction during mixed navigation. Dense and disorderly mixed navigation in restricted waters can easily induce new navigation risks. High-frequency interactions such as overtaking, head-on encounters, and cross-encounters will exacerbate the complexity of mixed navigation and significantly compress the effective application boundary of autonomous vessels' obstacle avoidance and decision-making capabilities. The navigation process faces high uncertainty and is accompanied by systemic safety hazards.

[0003] The complex evolution of port waters has posed challenges to navigation safety and efficiency, and has also promoted in-depth research both domestically and internationally. Current research still focuses on the passage of traditional vessels, paying attention to the impact of complex factors such as water space structure, rule differences, and system disturbances on navigation. There is no research on improving the efficiency of mixed navigation between autonomous and traditional vessels under safety constraints. The gradual integration of autonomous vessels has brought new challenges to the mixed navigation requirements and navigation system performance in port waters. Current research lacks a systematic analysis of mixed navigation characteristics and has not seen any research on mixed navigation organization design. The safe and efficient allocation of navigation resources is the core of improving mixed navigation efficiency. A scientific allocation mechanism depends on the overall planning of dispatch instructions. At present, in key navigation units such as waterways and confluence waters, the spatiotemporal resource allocation patterns, navigation restrictions and applicable boundaries are not clear, making it difficult to achieve orderly guidance, staggered passage, diversion control and interactive frequency reduction for heterogeneous vessels. Under the superposition of mixed navigation operation characteristics and existing influencing factors in complex waters (such as one-way navigation, tidal navigation, interactive risks, etc.), the existing manual dispatch instructions are difficult to achieve efficient overall planning, which limits the potential for mixed navigation environment regulation. Summary of the Invention

[0004] This invention provides a method for optimizing the efficiency of autonomous-traditional vessel mixed navigation in port waters under interactive risk constraints. This method overcomes the difficulties of existing technologies in achieving orderly guidance, staggered passage, diversion control, and interactive frequency reduction of heterogeneous vessels. It also addresses the problem that under the superposition of influencing factors, manually-led scheduling instructions are difficult to achieve efficient overall planning and limit the potential for regulating mixed navigation environments.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows: A method for optimizing the efficiency of autonomous-traditional vessel mixed navigation in port waters under interactive risk constraints includes: S1. Collect static and dynamic information of ships in the intersection area in real time through AIS base stations, and obtain the maximum number of ships in the intersection area and the real-time motion parameters of each ship based on the static and dynamic information of ships in the intersection area; obtain basic interaction frequency data based on the real-time motion parameters of ships; and collect tide forecasts, one-way navigation periods and hydrological and meteorological environmental parameters at the same time. S2. Based on the maximum number of ships and the basic interaction frequency, the upper limit of the interaction frequency of autonomous ships is obtained through a safety reduction factor. Based on the upper limit of the interaction frequency of autonomous ships, combined with the preset ship domain model, the safety distance constraint, ship peak-shifting constraint, one-way navigation constraint and tide-following time window restriction constraint are obtained. S3. Based on the data collected in step S1, and with constraints such as the upper limit of interaction frequency of autonomous vessels, safety distance constraints, vessel peak-shifting constraints, one-way navigation constraints, and tide-riding time window restrictions, construct a mixed integer linear programming model for the weighted sum of delays of autonomous-traditional vessel mixed navigation tasks in complex port waters. S4. Based on the constraints of the mixed-integer linear programming model, the mixed-integer linear programming model is solved by a solver to obtain the ship entry and exit timing and speed commands, so as to adjust the mixed navigation efficiency of autonomous and conventional ships in the intersection area.

[0006] Furthermore, the expression for the mixed-integer linear programming model is:

[0007] In the formula, The sum of weighted delays for mixed navigation missions of autonomous and conventional vessels in complex port waters; For the first Weighted delay of the mission for each ship; Assign a mission number to the vessel; The set of ship tasks within the scheduling cycle; For the first The weighting coefficient of each ship's mission; For the first The start time of the mission for each ship; For the first The estimated start time of the mission for each ship; For the first The waiting time for the ship's mission has been delayed.

[0008] Furthermore, based on the aforementioned safety distance constraints and ship staggering constraints, start time and traffic diversion constraints in the intersection area are constructed. The expressions for the start time and traffic diversion constraints in the intersection area are as follows:

[0009]

[0010]

[0011]

[0012] In the formula, The time within the scheduling period; For the vessel traffic organization and scheduling cycle; As a decision variable, and Indicates the first The ship's mission Start immediately, otherwise ; As a decision variable, and Indicates the first The ship's mission Arrive at the intersection area at the right time ,otherwise ; Number the intersection area; For the first The intersection area that the ships do not pass through on their mission; For the first The ships have arrived at the rendezvous area. The sailing time; For the first The ships converge at the intersection area they pass through on their mission.

[0013] Furthermore, the interaction frequency control constraints in the intersection area are constructed based on the upper limit of the interaction frequency of autonomous vessels; The expression for controlling the interaction frequency in the intersection area is as follows:

[0014]

[0015]

[0016]

[0017]

[0018]

[0019]

[0020]

[0021]

[0022] In the formula, As a decision variable, and Indicates the first The ship's mission Constantly occupying the intersection area ,otherwise ; For the first The ships are on a mission in the intersection area. The sailing time; As a decision variable, and Indicates the first The mission of the ship and the first Traditional ship missions in Simultaneously occupying the intersection area ,otherwise ; As a decision variable, and Indicates the first Traditional ship missions in Constantly occupying the intersection area ,otherwise ; For autonomous vessel missions; As a decision variable, and Indicates the first The mission of the autonomous vessel and the first Traditional vessels simultaneously occupied the rendezvous area. ,otherwise ; Intersection area The upper limit on the frequency of autonomous ship interactions within the territory; To ensure the maximum number of vessels that can pass through the intersection area; For the penetration rate of domestically built ships; For the dynamic domain correction factor of autonomous vessels; This refers to the frequency of interactions between autonomous vessels and other vessels within the intersection area.

[0023] Furthermore, navigation constraints are constructed based on safety distance constraints and the upper limit of interaction frequency of autonomous vessels; The expression for the navigation constraints of the waterway is as follows:

[0024]

[0025]

[0026] In the formula, As a decision variable, and Indicates the first The ship's mission Arrive at the channel at the right time ,otherwise ; Number the waterway; For the first The shipping lanes that the ships do not pass through on their mission; For waterways connecting to the intersection area; For the first A collection of shipping routes traversed by the vessels on their mission.

[0027] Beneficial Effects: This invention presents a method for optimizing the mixed navigation efficiency of autonomous and conventional vessels in port waters under interactive risk constraints. By considering the interactive risk constraints related to autonomous vessels in each intersection area and combining them with the inherent navigation restrictions in complex port waters, a mixed-integer linear programming model is constructed and solved using the weighted total delay of heterogeneous vessel entry and exit tasks as the objective function. Based on the inherent restrictions in complex port waters, this method considers the interactive risks of autonomous vessels in intersection areas to ensure effective interaction between autonomous and conventional vessels, ensuring the effective application boundary of autonomous vessels' obstacle avoidance and decision-making capabilities. While ensuring the navigation safety of heterogeneous vessels, it improves the navigation efficiency of heterogeneous vessels, providing an efficient and flexible solution for optimizing the mixed navigation scheduling instructions of autonomous and conventional vessels in complex port waters. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a schematic diagram of the process for optimizing the efficiency of autonomous and conventional ship mixed navigation according to the present invention. Figure 2 This is a schematic diagram of the vessel dispatching instructions for the meeting area of ​​vessels 1 to 15 in this embodiment of the invention; Figure 3 This is a schematic diagram of the vessel dispatching instructions for the meeting area of ​​vessels 16 to 30 in an embodiment of the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] This embodiment provides a method for optimizing the efficiency of autonomous and conventional vessel mixed navigation in port waters under interactive risk constraints, such as... Figure 1 As shown, it includes: S1. Collect static and dynamic information of ships in the intersection area in real time through AIS base stations, and obtain the maximum number of ships in the intersection area and the real-time motion parameters of each ship based on the static and dynamic information of ships in the intersection area; obtain basic interaction frequency data based on the real-time motion parameters of ships; and collect tide forecasts, one-way navigation periods and hydrological and meteorological environmental parameters at the same time. The static information refers to information related to the basic attributes of the ship, including data such as ship identification, ship type, and ship size; The dynamic information includes vessel sailing time, transit time in the intersection area, and vessel arrival time sequence. S2. Based on the maximum number of ships and the basic interaction frequency, the upper limit of the interaction frequency of autonomous ships is obtained through a safety reduction factor. Based on the upper limit of the interaction frequency of autonomous ships, combined with the preset ship domain model, the safety distance constraint, ship peak-shifting constraint, one-way navigation constraint and tide-following time window restriction constraint are obtained. Specifically, based on historical AIS data, the maximum number of stable passages in traditional vessel intersection areas per unit time is statistically analyzed; using measured data (AIS and shipborne sensors) from pilot ports for autonomous vessels, a safety reduction factor is derived; and based on the maximum stable number of passages and the safety reduction factor, the upper limit of the interaction frequency of autonomous vessels is calculated. The preset ship domain model can be either a circular ship domain model or an elliptical ship domain model; the calculation of the safety reduction coefficient, the upper limit of the interaction frequency of autonomous ships, and the preset ship domain model are existing technologies and will not be described in detail here.

[0032] S3. Based on the data collected in step S1, and with constraints such as the upper limit of interaction frequency of autonomous vessels, safety distance constraints, vessel peak-shifting constraints, one-way navigation constraints, and tide-riding time window restrictions, construct a mixed integer linear programming model for the weighted sum of delays of autonomous-traditional vessel mixed navigation tasks in complex port waters. Preferably, the expression for the mixed-integer linear programming model is: (1) In the formula, The sum of weighted delays for mixed navigation missions of autonomous and conventional vessels in complex port waters; For the first Weighted delay of the mission for each ship; Assign a mission number to the vessel; The set of ship tasks within the scheduling cycle; For the first The weighting coefficient of each ship's mission; For the first The start time of the mission for each ship; For the first The estimated start time of the mission for each ship; For the first The waiting time for the ship's mission has been delayed.

[0033] Preferably, the start time and traffic diversion constraints in the intersection area are constructed based on the safety distance constraints and the staggered vessel peak constraints; The expressions for the start time and traffic diversion constraints in the intersection area are as follows: (2) (3) (4) (5) In the formula, The time within the scheduling period; For the vessel traffic organization and scheduling cycle; As a decision variable, and Indicates the first The ship's mission Start immediately, otherwise ; As a decision variable, and Indicates the first The ship's mission Arrive at the intersection area at the right time ,otherwise ; Number the intersection area; For the first The intersection area that the ships do not pass through on their mission; For the first The ships have arrived at the rendezvous area. The sailing time; For the first The ships converge at the intersection area they pass through on their mission.

[0034] Equations (2) and (3) are effective inequalities, which reduce the spatial range of decision variables for the time when a ship's mission arrives at the intersection area; Equation (4) represents the correlation between the variable for the time when a ship's mission arrives at the intersection area and the time when the ship's mission begins; Equation (5) expresses that at most one ship can arrive at the same conflict point in the same intersection area at the same time, ensuring the safe distance control of ships traveling in the same direction in the same channel, and avoiding peak-shifting conflicts of ships in the intersection area.

[0035] Specifically, traffic management in the intersection area takes into account two factors: first, the safe distance is calculated based on the shipping sector to ensure that ships maintain the minimum safe distance between them; second, ships stagger their peak times to avoid conflicts, so that the time when each ship passes through the conflict point is staggered, and multiple ships do not enter the intersection area at the same time. The start time and traffic diversion constraints in the intersection area also include constraints on the start time of ship missions, expressed as follows: (6) (7) (8) Equation (6) indicates that the start time of a ship's mission should not be less than the online time of the mission (i.e., the time when the ship arrives at the port area); Equation (7) defines the association between continuous and 0-1 decision variables related to the start time of a ship's mission; Equation (8) indicates that the start time of a ship's mission is unique.

[0036] Preferably, interaction frequency control constraints in the intersection area are constructed based on the upper limit of the interaction frequency of the autonomous vessels; The expression for the interaction frequency control constraint in the intersection area is as follows: (9) (10) (11) (12) (13) (14) (15) (16) (17) In the formula, As a decision variable, and Indicates the first The ship's mission Constantly occupying the intersection area ,otherwise ; For the first The ships are on a mission in the intersection area. The sailing time; As a decision variable, and Indicates the first The mission of the autonomous vessel and the first Traditional ship missions in Simultaneously occupying the intersection area ,otherwise ; As a decision variable, and Indicates the first Traditional ship missions in Constantly occupying the intersection area ,otherwise ; For autonomous vessel missions; As a decision variable, and Indicates the first The mission of the autonomous vessel and the first Traditional vessels simultaneously occupied the rendezvous area. ,otherwise ; Intersection area The upper limit on the frequency of autonomous ship interactions within the territory; To ensure the maximum number of vessels that can pass through the intersection area; For the penetration rate of domestically built ships; For the dynamic domain correction factor of autonomous vessels; The frequency of interactions between autonomous vessels and other vessels within the intersection area; Equation (9) represents the correlation between the variable of a vessel occupying the intersection area and the variable of a vessel entering the intersection area; Equations (10) to (12) represent the variable definitions of autonomous vessels and traditional vessels occupying the intersection area at the same time; Equations (13) and (14) represent the variable definitions of autonomous vessels and traditional vessels interacting within the intersection area; Equation (15) expresses the calculation of the reduction factor based on the existing maximum number of vessels passing (interacting) in the intersection area obtained through AIS data analysis, and obtains the upper limit of the interaction frequency in the intersection area under autonomous-traditional mixed navigation conditions; Equations (16) and (17) express the definition of the interaction frequency of autonomous vessels in the intersection area and the interaction frequency limit to ensure that autonomous vessels have sufficient obstacle avoidance boundaries; Specifically, the risk control constraints in the intersection area are achieved by quantifying the risk of ship collisions into a dynamic limit on the number of ships allowed to pass (interact) within a unit of time in the intersection area; and by analyzing historical AIS trajectories to determine the maximum stable number of ships passing through the intersection area under traditional traffic conditions. Combining shipbuilding theory, the dynamic domain correction coefficient for autonomous ships The value is jointly determined by the autonomous control prediction time domain, target behavior recognition error, and mixed navigation interaction risk level, and can be obtained from publicly available autonomous ship test results or simulation calibration; combined with the penetration rate of autonomous ships. The average safe occupancy level under mixed traffic conditions is calculated, and the upper limit of the interaction frequency in the intersection area is reduced and corrected to obtain the parameters. The values ​​are determined, and an upper limit constraint on the frequency of interaction in the intersection area under autonomous-traditional mixed navigation conditions is formed, thereby transforming the control of complex interaction risks into a control constraint on the number of autonomous ship interaction frequencies.

[0037] Preferably, navigation constraints are constructed based on safety distance constraints and the upper limit of interaction frequency of autonomous vessels; The expression for the navigation constraints of the waterway is as follows: (18) (19) (20) In the formula, As a decision variable, and Indicates the first The ship's mission Arrive at the channel at the right time ,otherwise ; Number the waterway; For the first The shipping lanes that the ships do not pass through on their mission; For waterways connecting to the intersection area; For the first A collection of shipping routes traversed by the vessels on their mission.

[0038] Equations (18) and (19) are valid inequalities, which indicate that the spatial range of the decision variable for the arrival time of the ship's mission in the channel has been reduced; Equation (20) indicates the correlation between the variable for the arrival time of the ship's mission in the intersection area and the variable for the arrival time of the ship's mission in the channel.

[0039] Specifically, the expression for the one-way traffic constraint is as follows: (twenty one) (twenty two) (twenty three) (twenty four) (25) (26) (27) (28) In the formula, As a decision variable, and Indicates the first The ship's mission involves selecting a waterway. Sailing, otherwise ; As a decision variable, and Indicates a one-way channel Inward-facing ship mission The order of events, i.e. The ship is Enter the channel ahead of the ship, otherwise... ; As a decision variable, and Indicates a one-way channel Inward-facing ship mission The order of events, i.e. The ship is Enter the channel ahead of the ship, otherwise... ; For the first The time when the ships arrived at the navigation channel; This is a set of tasks for one-way traffic restricted channels; A set of waterways with one-way traffic rules; As a decision variable, and Indicates the first Traditional ships select routes Sailing, otherwise ; The time a ship spends on its mission within the waterway; For traditional ship missions, the arrival time at the waterway is the time of arrival. For traditional ship missions, the sailing time in the waterway; It is an infinite number; Equation (21) represents the association between the navigation variable of the ship mission and the arrival time variable of the autonomous ship mission; Equation (22) represents the association between the continuous arrival time of the ship mission and the 0-1 decision variable; Equation (23) represents that there can only be one passage order for opposing ships with one-way navigation needs in the channel; Equations (24) to (26) represent the association between the navigation variable of the ship mission and the entry order variable of opposing ships into the channel; Equations (27) and (28) represent the time limit for opposing ships with one-way navigation needs to enter the channel, that is, during one-way navigation, there can only be one direction of navigation in the channel.

[0040] Specifically, based on the physical limitations of ship maneuvering and waterway dimensions, in a one-way waterway, only one direction of ship traffic can flow at a time. This limitation stems from the fact that the effective width of a one-way waterway is insufficient for large ships to pass safely in opposite directions, which is an inherent physical constraint of waterway engineering and ship maneuvering performance.

[0041] Specifically, the expression for the tide-riding time window constraint is as follows: (29) (30) In the formula, A collection of time windows for ships to ride the tide in the waterway; Selecting the time window for ships to ride the tide in the waterway; As a decision variable, and Indicates the first The vessel's mission is to select the tidal window in the shipping channel. Navigation will proceed, otherwise ; A collection of tasks for ships that need to ride the tide; and The initial tide-following time window for vessel missions within the scheduling cycle in the waterway; Equation (29) indicates that a vessel can only choose one tide window for navigation; Equation (30) indicates that a vessel must navigate within the channel time window.

[0042] Specifically, based on the natural laws of tides, the upper and lower limits of the tide-riding window are derived from long-term astronomical tidal observations and tide height predictions at the port, taking into account the periodic rise and fall of seawater caused by celestial movements, and are obtained through natural laws.

[0043] S4. Based on the constraints of the mixed-integer linear programming model, the mixed-integer linear programming model is solved by a solver to obtain the ship entry and exit timing and speed commands, so as to adjust the mixed navigation efficiency of autonomous and conventional ships in the intersection area.

[0044] In this embodiment, the mixed-integer linear programming model is solved using the Gurobi solver. The mixed-ship scheduling instructions are optimized using the solver's built-in heuristic method. This process does not require additional manual design of heuristics or manual parameter tuning. It can directly obtain the optimal or near-optimal scheduling results that meet all constraints, thus obtaining the mixed-ship scheduling instruction scheme. The process of solving the problem using the Gurobi solver is existing technology and will not be described in detail here.

[0045] In this embodiment, a case study analysis of mixed navigation of autonomous and traditional vessels is carried out using the waters of a port in southern China as the object. The complex waters of the port include 11 confluence areas, 21 channel sections, and 6 port areas. Based on the safe speed of vessels in the port, the navigation time in the channel sections is distributed between 2 and 10 seconds, and the navigation time in the confluence areas is distributed between 1 and 4 seconds. The inbound and outbound channels of port areas 1 and 2 are one-way channels. The tide-riding time window is determined based on the actual tidal information of the port area, and the safe interval between adjacent vessels is set to 1 time step. The system plans vessel scheduling instructions with a 6-hour scheduling cycle. The planning period is discretized into a set of time steps, with each step being 5 minutes. The vessel arrival time and channel navigation time are known, the berth plan has been formulated, and pilotage resources are sufficient, so the planning of vessel scheduling instructions is not affected. The navigation process of vessels in the channel and intersection area is represented by a set of nodes, which are regarded as the vessel navigation path composed of the combination of channel segments and intersection areas. Autonomous vessels maintain normal navigation in the port waters and are not affected by the port environment. Arriving ships are generated based on the distribution of ship arrivals at the port, and five ship navigation scenarios are constructed with a total of 30 to 35 ships. Among them, 4 to 5 are autonomous ships. The mixed navigation efficiency optimization model is solved using Gurobi and compared with the First-Come, First-Served (FCFS) method. The comparison results between the autonomous-traditional ship mixed navigation efficiency optimization method and the first-come-first-served method in this embodiment are shown in Table 1. The autonomous-traditional ship mixed navigation efficiency optimization method in this embodiment can reduce ship delays by an average of about 40%. Through multiple calculation experiments, the effectiveness of the autonomous-traditional ship mixed navigation efficiency optimization method in this embodiment is verified. It can take into account the mixed navigation characteristics and the complex elements of port waters, forming a safe and efficient mixed navigation scheduling command method. Table 1 Comparison Results of Optimized Dispatch Instructions for Mixed-Navigation Vessels

[0046] Taking Example 1 in Table 1 as an example for specific analysis, the passage order of mixed-traffic vessels in the intersection area is as follows: Figure 2 and Figure 3 As shown, the autonomous vessels are vessels 5, 7, 14, and 26. The intersection area of ​​vessel 5 is a choke point between the intersecting waterways and the port entry / exit routes, where vessel interactions are dense. This embodiment obtains the upper limit of the interaction frequency of autonomous vessels in this area based on AIS data and the vessel domain, and uses this upper limit as a constraint to dynamically schedule the interaction frequency of autonomous vessels with surrounding vessels, so that the perception, decision-making, and execution resources of autonomous vessels are always within the effective working boundary, ensuring the reliability of the obstacle avoidance function.

[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the efficiency of autonomous-traditional vessel mixed navigation in port waters under interactive risk constraints, characterized in that, include: S1. Collect static and dynamic information of ships in the intersection area in real time through AIS base stations, and obtain the maximum number of ships in the intersection area and the real-time motion parameters of each ship based on the static and dynamic information of ships in the intersection area; obtain basic interaction frequency data based on the real-time motion parameters of ships; and collect tide forecasts, one-way navigation periods and hydrological and meteorological environmental parameters at the same time. S2. Based on the maximum number of ships and the basic interaction frequency, the upper limit of the interaction frequency of autonomous ships is obtained through a safety reduction factor. Based on the upper limit of the interaction frequency of autonomous ships, combined with the preset ship domain model, the safety distance constraint, ship peak-shifting constraint, one-way navigation constraint and tide-following time window restriction constraint are obtained. S3. Based on the data collected in step S1, and with constraints such as the upper limit of interaction frequency of autonomous vessels, safety distance constraints, vessel peak-shifting constraints, one-way navigation constraints, and tide-riding time window restrictions, construct a mixed integer linear programming model for the weighted sum of delays of autonomous-traditional vessel mixed navigation tasks in complex port waters. S4. Based on the constraints of the mixed-integer linear programming model, the mixed-integer linear programming model is solved by a solver to obtain the ship entry and exit timing and speed commands, so as to adjust the mixed navigation efficiency of autonomous and conventional ships in the intersection area.

2. The method for optimizing the efficiency of autonomous-traditional vessel mixed navigation in port waters under interactive risk constraints as described in claim 1, characterized in that, The expression for the mixed-integer linear programming model is: In the formula, The sum of weighted delays for mixed navigation missions of autonomous and conventional vessels in complex port waters; For the first Weighted delay of the mission for each ship; Assign a mission number to the vessel; The set of ship tasks within the scheduling cycle; For the first The weighting coefficient of each ship's mission; For the first The start time of the mission for each ship; For the first The estimated start time of the mission for each ship; For the first The waiting time for the ship's mission has been delayed.

3. The method for optimizing the efficiency of autonomous-traditional vessel mixed navigation in port waters under interactive risk constraints as described in claim 2, characterized in that, Based on the aforementioned safety distance constraints and staggered vessel schedule constraints, a start time and traffic diversion constraints for the intersection area are constructed. The expressions for the start time and traffic diversion constraints in the intersection area are as follows: In the formula, The time within the scheduling period; For the vessel traffic organization and scheduling cycle; As a decision variable, and Indicates the first The ship's mission Start immediately, otherwise ; As a decision variable, and Indicates the first The ship's mission Arrive at the intersection area at the right time ,otherwise ; Number the intersection area; For the first The intersection area that the ships do not pass through on their mission; For the first The ships have arrived at the rendezvous area. The sailing time; For the first The ships converge at the intersection area they pass through on their mission.

4. The method for optimizing the efficiency of autonomous-traditional vessel mixed navigation in port waters under interactive risk constraints as described in claim 3, characterized in that, Based on the upper limit of the interaction frequency of the autonomous vessels, an interaction frequency control constraint is constructed for the intersection area; The expression for the interaction frequency control constraint in the intersection area is as follows: In the formula, As a decision variable, and Indicates the first The ship's mission Constantly occupying the intersection area ,otherwise ; For the first The ships are on a mission in the intersection area. The sailing time; As a decision variable, and Indicates the first The mission of the autonomous vessel and the first Traditional ship missions in Simultaneously occupying the intersection area ,otherwise ; As a decision variable, and Indicates the first Traditional ship missions in Constantly occupying the intersection area ,otherwise ; For autonomous vessel missions; As a decision variable, and Indicates the first The mission of the autonomous vessel and the first Traditional vessels simultaneously occupied the rendezvous area. ,otherwise ; Intersection area The upper limit on the frequency of autonomous ship interactions within the territory; To ensure the maximum number of vessels that can pass through the intersection area; For the penetration rate of domestically built ships; For the dynamic domain correction factor of autonomous vessels; This refers to the frequency of interactions between autonomous vessels and other vessels within the intersection area.

5. The method for optimizing the efficiency of autonomous-traditional vessel mixed navigation in port waters under interactive risk constraints as described in claim 4, characterized in that, Navigation constraints for waterways are constructed based on safety distance constraints and the upper limit of interaction frequency of autonomous vessels; The expression for the navigation constraints of the waterway is as follows: In the formula, As a decision variable, and Indicates the first The ship's mission Arrive at the channel at the right time ,otherwise ; Number the waterway; For the first The shipping lanes that the ships do not pass through on their mission; For waterways connecting to the intersection area; For the first A collection of shipping routes traversed by the vessels on their mission.

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