Intelligent scheduling method and system for ship berthing

Abstract

The present invention relates to the technical field of ship mooring scheduling, and specifically to a method and system for intelligent scheduling of ship mooring. The method comprises the following steps: obtaining basic ship information and terminal facility equipment data; establishing ship berthing rules, and obtaining an initial ship berthing plan using the basic ship information and the ship berthing rules; obtaining an auxiliary equipment allocation plan based on the initial ship berthing plan and the terminal facility equipment data, and obtaining an initial ship berthing scheduling plan by combining the initial ship berthing plan and the auxiliary equipment allocation plan; constructing a ship berthing plan evaluation model, and using the ship berthing plan evaluation model to evaluate the initial ship berthing scheduling plan, adjusting the initial ship berthing scheduling plan according to the evaluation results, and obtaining an optimal ship berthing scheduling plan. The present invention solves the problem of intelligent scheduling of port ship berthing, realizes intelligent scheduling of ship berthing and unberthing, and improves the operational efficiency and safety of the port.

Description

Technical Field

[0001] The present invention relates to the technical field of ship mooring scheduling, and in particular to a ship mooring intelligent scheduling method and system. Background Art

[0002] The production management of ports is mainly based on production scheduling. How to enhance the port service capacity by improving the efficiency of port production operations and resource utilization? Ship berthing scheduling is an extremely complex system engineering, which requires comprehensive consideration of multiple factors such as ship conditions, shore berths, port supporting facilities and equipment, tidal conditions, etc. The efficiency and quality of scheduling directly affect the efficiency of the entire port production operation, and is a hot issue in the research field of port scheduling. At present, in the actual production process, port dispatchers mainly rely on their production experience to select operating equipment and loading and unloading processes. When formulating operation plans, port dispatchers need to use limited port berth resources and production equipment resources to complete the loading and unloading tasks of ships arriving at the port as efficiently as possible, reduce the ship's stay time in the port, and reduce the port's operating costs. However, this method of relying on manual experience is relatively traditional, and there are problems such as complex links, diverse personnel types, waste of resources, and low efficiency. It is not suitable for the formulation of large-scale scheduling plans, which seriously hinders the improvement of port operation efficiency and the development of port service capabilities, and is difficult to adapt to the changing operating environment. How to rationally allocate the limited berth resources and facility and equipment resources of the port, improve the overall efficiency of the port, and promote the development of the port towards intelligence is very important for the development of port enterprises. Summary of the invention

[0003] In view of the shortcomings of existing methods and the needs of practical applications, in order to improve the port operation capacity and solve the problem of intelligent scheduling of port ship berthing, the present invention provides a method for intelligent scheduling of ship berthing, comprising the following steps:

[0004] Obtain basic ship information and terminal facility equipment data; establish ship berthing rules, and use the basic ship information and the ship berthing rules to obtain the initial ship berthing plan; obtain the auxiliary equipment allocation plan based on the initial ship berthing plan and the terminal facility equipment data, and obtain the initial ship berthing scheduling plan by combining the initial ship berthing plan and the auxiliary equipment allocation plan; construct a ship berthing plan evaluation model, use the ship berthing plan evaluation model to evaluate the initial ship berthing scheduling plan, adjust the initial ship berthing scheduling plan according to the evaluation result, and obtain the optimal ship berthing scheduling plan. The present invention obtains the initial ship berthing plan by using the basic ship information and the ship berthing rules, and then iteratively optimizes the initial ship berthing plan to obtain the optimal ship berthing scheduling plan, thereby realizing the intelligent scheduling of ship berthing and unberthing, improving the port throughput and operating efficiency, thereby improving the port's operating efficiency and safety, and effectively solving the problems of inefficiency and waste of resources caused by the difficulty of berthing scheduling in traditional ports.

[0005] Optionally, the basic ship information includes arrival time, cargo information and loading and unloading requirements, ship size, draft and operation restrictions; the terminal facility equipment data includes berth data, tugboat data, ship unloader data and belt conveyor data. Obtaining accurate basic ship information and terminal facility equipment data is conducive to the accurate implementation of the scheduling plan.

[0006] Optionally, the ship berthing rules include: matching ship cargo with berth loading and unloading machinery; matching ship length with berth length; matching ship draft at berth with tidal time. The present invention further facilitates the feasibility of the scheduling plan by setting ship berthing rules.

[0007] Optionally, the ship berthing plan evaluation model satisfies the following formula:

[0008] ,in, It represents the evaluation value of the ship berthing plan. represents the port cost weight coefficient, Indicates the number of ships in the ship berthing plan, Indicates Vessel departure time, Indicates Ship arrival time, Indicates The cost per unit time of the ship in port, Indicates The cargo volume of the vessel, Indicates the cargo volume of all ships in the ship berthing plan, represents the weight coefficient of loading and unloading cost, Indicates Terminal facilities and equipment services Unit cost of the vessel, Indicates Terminal facilities and equipment services The workload of the ship, Indicates the number of terminal facilities and equipment, The present invention evaluates the ship berthing plan in terms of port cost and loading and unloading cost, comprehensively considers the impact of the ship berthing plan on shipping companies and port operations, and is conducive to achieving a win-win situation for ports and shipping companies.

[0009] Optionally, the evaluating the initial ship berthing scheduling plan by using the ship berthing plan evaluation model, adjusting the initial ship berthing scheduling plan according to the evaluation result, and obtaining the optimal ship berthing scheduling plan comprises the following steps:

[0010] The initial ship berthing scheduling plan is matrix-vectorized, and the matrix-vectorized result is adjusted to obtain a plurality of variant vectorized matrices; the ship berthing scheduling plan represented by the variant vectorized matrix is ​​evaluated using the ship berthing plan evaluation model; according to the evaluation result, the variant vectorized matrix is ​​adjusted for the first time and the second time, and iterative evaluation and adjustment are performed based on the adjustment result to obtain the optimal ship berthing scheduling plan. The present invention uses the evaluation result to adjust the initial ship berthing scheduling plan, which is conducive to quickly and accurately obtaining the optimal ship berthing scheduling plan.

[0011] Optionally, the adjustment of the matrix vectorization result includes cross adjustment and variation adjustment. The present invention obtains multiple variation vectorization matrices through multiple adjustments, which further facilitates the subsequent steps to quickly and accurately obtain the optimal scheduling plan for ship berthing.

[0012] Optionally, the first adjustment of the variation quantization matrix satisfies the following formula:

[0013] ,in, Indicates At the next iteration, The mutation vectorization matrix is ​​the mutation vectorization matrix after the first adjustment, represents the maximum number of iterations, Indicates the current iteration number, Indicates The first iteration mutation vectorization matrix, represents the number of cluster centers, Indicates The iteration The mutation vectorization matrix is ​​added to the The probability of learning cluster centers belongs to A random number that satisfies , Indicates The iteration Cluster centers, represents the adaptive learning factor, Indicates The present invention introduces clustering learning and adaptive learning to make a first adjustment to the mutation quantization matrix, which is beneficial to balance the global optimization accuracy and local optimization speed, and further improves the scheduling efficiency of the present invention.

[0014] Optionally, a second adjustment is performed on the mutation quantization matrix to satisfy the following formula:

[0015] ,in, Indicates At the next iteration, The mutation vectorization matrix is ​​the mutation vectorization matrix after the second adjustment, Indicates The worst mutation vector quantization matrix evaluation value in iterations, Indicates The optimal mutation vectorization matrix evaluation value in the iteration, Indicates The first iteration mutation vectorization matrix, represents the adaptive learning factor, Represents the optimal mutation vectorization matrix of the historical iteration process, express A random number, Indicates The iteration The present invention introduces the inverse solution and adaptive learning to perform a second adjustment on the variant quantization matrix, which is further beneficial to improving the quality of search and the scheduling efficiency of the present invention.

[0016] Optionally, the adaptive learning factor satisfies the following formula:

[0017]

[0018] in, represents the adaptive learning factor, Indicates the current iteration number, The present invention adjusts the adaptive learning factor by the number of iterations, which is further conducive to balancing the optimization direction in the early and late stages of iteration.

[0019] In the second aspect, in order to efficiently execute the ship berthing intelligent scheduling method provided by the present invention, the present invention also provides a ship berthing intelligent scheduling system, including a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the ship berthing intelligent scheduling method as described in the first aspect of the present invention. The ship berthing intelligent scheduling system of the present invention has a compact structure and stable performance, and can stably execute the ship berthing intelligent scheduling method provided by the present invention, further improving the overall applicability and practical application ability of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A flow chart of a method for intelligent scheduling of ship berthing provided by an embodiment of the present invention;

[0021] Figure 2 A framework diagram of a ship berthing intelligent scheduling system provided by an embodiment of the present invention;

[0022] Figure 3 A schematic diagram of the structure of an intelligent ship berthing scheduling device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are only for illustration and are not intended to limit the present invention. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details do not need to be adopted to implement the present invention. In other examples, in order to avoid confusing the present invention, known circuits, software or methods are not specifically described.

[0024] Throughout the specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment of the present invention. Therefore, the phrases "in one embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily all refer to the same embodiment or example. In addition, particular features, structures, or characteristics may be combined in one or more embodiments or examples in any suitable combination and / or subcombination. In addition, it should be understood by those of ordinary skill in the art that the figures provided herein are for illustrative purposes and that the figures are not necessarily drawn to scale.

[0025] See also Figure 1In order to improve the port operation capacity and solve the problem of intelligent scheduling of port ship berthing, the present invention provides a method for intelligent scheduling of ship berthing, such as Figure 1 As shown, in one embodiment, the method includes the following steps:

[0026] S1. Obtain basic ship information and terminal facilities and equipment data.

[0027] In an embodiment, the basic information of the ship includes arrival time, cargo information and loading and unloading requirements, ship size, draft depth and operation restrictions.

[0028] The arrival time is determined by the ship's own position, sailing speed, weather conditions and other conditions. The ship will send arrival application information to the port dispatch center two days, one day and 12 hours before the planned arrival at the port. The port dispatch center will formulate long-term or short-term dispatch plans based on the arrival application information.

[0029] Cargo information, including cargo name, type, specifications and dimensions, quantity and weight, packaging method and hazard level, etc.

[0030] Loading and unloading needs require choosing a matching loading and unloading method based on the importance or particularity of the goods. For example, matching loading and unloading equipment needs to be selected for flammable, explosive, toxic or corrosive goods.

[0031] The size of a ship refers to the horizontal distance from the front end to the rear end of the ship, including the bow, stern and all protruding parts.

[0032] Draft depth refers to the vertical distance from the bottom of the ship to the point where the hull connects to the water surface, that is, the deepest length of the underwater part of the ship in the water.

[0033] Operational restrictions include the matching of ship size, channel width and channel water depth, as well as restrictions imposed by ship owners; the length, width, draft and other dimensions of the ship must be adapted to the conditions of the channel and port. Ships that are too wide, too long or have too deep a draft may not be able to pass through certain channels or enter specific ports smoothly; the width of the channel determines whether the ship can pass safely. When a ship is sailing in the channel, it needs to maintain sufficient lateral spacing to prevent collisions with other ships or channel facilities.

[0034] The terminal facilities and equipment data include berth data, tugboat data, ship unloader data, and conveyor belt data.

[0035] Berth data refers to the berths available for ships to berth in the port, as well as the corresponding mooring piles, fixed mechanical equipment, berth length, water depth information, etc.

[0036] Tug machine data, ship unloader data, belt conveyor data, including production technical data such as power, quantity, location, etc. of each equipment.

[0037] In the embodiment, basic information of the ship and data of terminal facilities and equipment may be obtained through the port data management center, or through corresponding sensors and satellite radars of each device.

[0038] S2. Establish ship berthing rules, and obtain an initial ship berthing plan using the basic ship information and the ship berthing rules.

[0039] In an embodiment, ship berthing rules include: matching ship cargo with berth loading and unloading machinery; matching ship length with berth length; matching ship draft at berth with tidal time.

[0040] Specifically, ship cargo comes in bulk and container forms, which require different cargo loading and unloading equipment, such as quay cranes or ship loading and unloading machines. The berth can be allocated to the ship when the ship cargo is matched with the berth loading and unloading machinery; the berth length varies according to the port terminal, and the ship length is also different. Therefore, when matching the ship to berth, the ship length and berth length must match. If the ship length is longer than the berth length, the ship cannot berth, and if the ship length is much smaller than the berth length, berth resources are wasted; the depth of the berth and the waterway is closely related to the tide. Matching the draft of the ship at the berth with the tidal time can avoid the ship from running aground.

[0041] Furthermore, according to the principle of first-come, first-served, the basic information of the ship and the ship berthing rules are used to arrange the ship to the corresponding berth to obtain an initial plan for the ship berthing.

[0042] In some other embodiments, the ship berthing rules may also be subject to other constraints and limitations based on actual production conditions.

[0043] S3. Obtain an auxiliary equipment allocation plan based on the initial ship berthing plan and the terminal facility equipment data, and obtain an initial ship berthing scheduling plan by combining the initial ship berthing plan and the auxiliary equipment allocation plan.

[0044] Specifically, according to the needs of each ship in the initial ship berthing plan, all the allocatable terminal facilities and equipment are initially allocated to obtain an auxiliary equipment allocation plan.

[0045] Combined with the initial ship berthing plan and the auxiliary equipment allocation plan, an initial ship berthing scheduling plan is obtained, including ship model shifts, berthing berths, berthing time, allocation of tugboats, allocation of ship unloaders and allocation of belt conveyors, as well as other relevant information.

[0046] S4. Construct a ship mooring plan evaluation model, use the ship mooring plan evaluation model to evaluate the initial ship mooring scheduling plan, adjust the initial ship mooring scheduling plan according to the evaluation result, and obtain the optimal ship mooring scheduling plan.

[0047] In an embodiment, the ship berthing plan evaluation model constructed in step S4 satisfies the following formula:

[0048] ,in, It represents the evaluation value of the ship berthing plan. represents the port cost weight coefficient, Indicates the number of ships in the ship berthing plan, Indicates Vessel departure time, Indicates Ship arrival time, Indicates The cost per unit time of the ship in port, Indicates The cargo volume of the vessel, Indicates the cargo volume of all ships in the ship berthing plan, represents the weight coefficient of loading and unloading cost, Indicates Terminal facilities and equipment services Unit cost of the vessel, Indicates Terminal facilities and equipment services The workload of the ship, Indicates the number of terminal facilities and equipment, Indicates the number of ships.

[0049] It should be understood that the port costs represent the interests of the shipping company, and the loading and unloading costs represent the interests of the port operator. The weight coefficient between the two needs to be determined based on actual conditions.

[0050] The ship's costs in port include fuel consumption costs, port usage fees, crew wages and benefits, etc.

[0051] While the ship is in port, although the main engine may stop running, the auxiliary engine and boiler still need to consume a certain amount of fuel to maintain the basic operation of the ship, such as power supply and living facilities. The fuel consumption is related to factors such as ship type, tonnage, equipment efficiency, etc.

[0052] Port usage fees are fees that ships must pay while in port, including tugboat fees, pilotage fees, wharf fees, port charges, agency fees, etc. These fees are usually calculated based on the tonnage of the ship, the time in port and the specific regulations of the port.

[0053] Crew members are still required to stay at their posts while the ship is at anchor, so crew wages and benefits are also part of the ship's cost in port, which includes basic wages, navigation allowances, bonuses, as well as additional items such as food expenses, labor insurance benefits, etc.

[0054] Further, the evaluating the initial ship berthing scheduling plan by using the ship berthing plan evaluation model, adjusting the initial ship berthing scheduling plan according to the evaluation result, and obtaining the optimal ship berthing scheduling plan includes the following steps:

[0055] S41, matrix-vectorizing the initial ship berthing scheduling plan, and adjusting the matrix-vectorization result to obtain a plurality of variant vectorization matrices.

[0056] Specifically, matrix vectorization is performed according to the dimensions of the initial scheduling plan for ship berthing, and then the matrix vectorization results are adjusted to obtain multiple variant vectorization matrices.

[0057] Furthermore, the matrix vectorization result is adjusted, including crossover adjustment and mutation adjustment.

[0058] Crossover adjustment includes single-point crossover, multi-point crossover, uniform crossover and other crossover methods. Single-point crossover refers to randomly selecting a crossover point on the mutation vectorization matrix, cutting the two mutation vectorization matrices at the crossover point, and then exchanging the cut segments to form two new mutation vectorization matrices.

[0059] Multi-point crossover refers to randomly selecting multiple crossover points on the mutation vectorization matrix and cutting and exchanging two mutation vectorization matrices at these crossover points.

[0060] Uniform crossover means that each crossover point of two mutation vectorization matrices is exchanged with the same crossover probability to form two new mutation vectorization matrices.

[0061] Other crossover methods include partial matching crossover, sequential crossover, improved sequential crossover, etc. These crossover methods are usually designed for specific encoding methods and optimization problems to better meet the constraints and optimization goals of the problem.

[0062] Mutation adjustment refers to the process of randomly changing certain values ​​on the mutation vectorization matrix with a certain probability to generate a new mutation vectorization matrix, including basic bit mutation, uniform mutation, non-uniform mutation, polynomial mutation and adaptive mutation.

[0063] S42. Utilize the ship mooring plan evaluation model to evaluate the ship mooring scheduling plan represented by the variation vector quantization matrix.

[0064] Specifically, the ship mooring scheduling plan represented by the variation vector quantization matrix is ​​evaluated by using the ship mooring scheduling evaluation model to obtain the corresponding evaluation value of the ship mooring scheduling plan, and the best variation vector quantization matrix and the worst variation vector quantization matrix in this evaluation are obtained according to the ranking. Cluster analysis can also be performed based on the evaluation results using a cluster analysis method to obtain multiple cluster centers.

[0065] It should be understood that a higher evaluation value represents a lower cost of the ship berthing plan, and therefore, a higher evaluation value represents a lower ranking of the ship berthing plan.

[0066] S43. According to the evaluation results, the variation vector quantization matrix is ​​adjusted first and secondly, and iterative evaluation and adjustment are performed based on the adjustment results to obtain the optimal scheduling plan for the ship berthing.

[0067] Specifically, the first adjustment is performed on the variation quantization matrix to satisfy the following formula:

[0068] ,in, Indicates At the next iteration, The mutation vectorization matrix is ​​the mutation vectorization matrix after the first adjustment, represents the maximum number of iterations, Indicates the current iteration number, Indicates The first iteration mutation vectorization matrix, represents the number of cluster centers, Indicates The iteration The mutation vectorization matrix is ​​added to the The probability of learning cluster centers belongs to A random number that satisfies , Indicates The iteration Cluster centers, represents the adaptive learning factor, Indicates The optimal mutation vectorization matrix at iteration .

[0069] Furthermore, the mutation quantization matrix is ​​adjusted for the second time to satisfy the following formula:

[0070] ,in, Indicates At the next iteration, The mutation vectorization matrix is ​​the mutation vectorization matrix after the second adjustment, Indicates The worst mutation vector quantization matrix evaluation value in iterations, Indicates The optimal mutation vectorization matrix evaluation value in the iteration, Indicates The first iteration mutation vectorization matrix, represents the adaptive learning factor, Represents the optimal mutation vectorization matrix of the historical iteration process, express A random number, Indicates The iteration Introducing the inverse solution of the mutation vectorization matrix is ​​helpful to escape from the local optimal situation and obtain the optimal mutation vectorization matrix.

[0071] Furthermore, the adaptive learning factor satisfies the following formula:

[0072]

[0073] in, represents the adaptive learning factor, Indicates the current iteration number, Indicates the maximum number of iterations.

[0074] See also Figure 2 In an embodiment, in order to efficiently execute the ship berthing intelligent scheduling method provided by the present invention, the present invention also provides a ship berthing intelligent scheduling system, including: an input device, an output device, a processor, and a memory, wherein the input device, the output device, the processor, and the memory are interconnected, and the memory contains program instructions, and the program instructions are used for the steps of the ship berthing intelligent scheduling method. The ship berthing intelligent scheduling system of the present invention has a compact structure and stable performance, and can stably execute the ship berthing intelligent scheduling method of the present invention, further improving the overall applicability and practical application ability of the present invention.

[0075] In an embodiment, the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. An input device may be used to obtain data information. An output device may be used to output the results obtained by storing program instructions contained in a computer program in a memory provided by the present invention. The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory.

[0076] In yet another alternative embodiment, see Figure 3 In order to efficiently execute the ship berthing intelligent scheduling method provided by the present invention, this embodiment also provides a ship berthing intelligent scheduling device, such as Figure 3 As shown, including:

[0077] The memory 10 is used to store computer programs; the processor 20 is used to execute computer programs to implement the above-mentioned ship berthing intelligent scheduling method. The memory 10, the processor 20, the communication interface 31 and the communication bus 32. The memory 10, the processor 20, and the communication interface 31 all communicate with each other through the communication bus 32.

[0078] In an embodiment, the memory 10 is used to store one or more program instructions. The memory 10 may store program instructions for implementing the following functions:

[0079] Obtain basic ship information and terminal facility and equipment data; establish ship berthing rules, and use the basic ship information and the ship berthing rules to obtain an initial ship berthing plan; obtain an auxiliary equipment allocation plan based on the initial ship berthing plan and the terminal facility and equipment data, and obtain an initial ship berthing scheduling plan by combining the initial ship berthing plan and the auxiliary equipment allocation plan; construct a ship berthing plan evaluation model, use the ship berthing plan evaluation model to evaluate the initial ship berthing scheduling plan, adjust the initial ship berthing scheduling plan according to the evaluation results, and obtain the optimal ship berthing scheduling plan.

[0080] In one possible implementation, the memory 10 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, and at least one application required for a function, etc.; the data storage area may store data created during use. In addition, the memory 10 may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include NVRAM. The memory stores an operating system and operating instructions, executable modules or data structures, or a subset thereof, or an extended set thereof, wherein the operating instructions may include various operating instructions for implementing various operations. The operating system may include various system programs for implementing various basic tasks and processing hardware-based tasks.

[0081] The processor 20 may be a central processing unit (CPU), an application specific integrated circuit, a digital signal processor, a field programmable gate array or other programmable logic device, and the processor 20 may be a microprocessor or any conventional processor, etc. The processor 20 may call a program stored in the memory 10. The communication interface 31 may be an interface of a communication module, and is used to connect with other devices or systems.

[0082] Of course, it should be noted that Figure 3 The structure shown does not constitute a limitation on the intelligent dispatching device for ship mooring in this embodiment. In actual applications, the intelligent dispatching device for ship mooring may include Figure 3 More or fewer components than shown, or combinations of certain components.

[0083] An embodiment also provides a storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned ship berthing intelligent scheduling method are implemented.

[0084] The storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0085] In summary, the present invention obtains the initial plan for ship berthing by utilizing the basic information of the ship and the ship berthing rules, and then iteratively optimizes the initial plan for ship berthing to obtain the optimal scheduling plan for ship berthing, thereby realizing the intelligent scheduling of ship berthing and leaving, improving the port throughput and operating efficiency, thereby improving the operating efficiency and safety of the port, and effectively solving the problems of inefficiency and waste of resources caused by the difficulties in berthing scheduling in traditional ports.

[0086] Therefore, the present invention effectively overcomes various shortcomings in the prior art and has high industrial utilization value.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope recorded in the present invention.

Claims

1. A method for intelligent scheduling of ship berthing, characterized in that: The ship berthing intelligent scheduling method comprises the following steps: Obtain basic ship information and dock facility equipment data; Establishing ship berthing rules, and obtaining an initial ship berthing plan using the basic ship information and the ship berthing rules; According to the initial ship berthing plan and the terminal facilities and equipment data, an auxiliary equipment allocation plan is obtained, and in combination with the initial ship berthing plan and the auxiliary equipment allocation plan, an initial ship berthing scheduling plan is obtained; Constructing a ship berthing plan evaluation model, using the ship berthing plan evaluation model to evaluate the initial ship berthing scheduling plan, adjusting the initial ship berthing scheduling plan according to the evaluation result, and obtaining an optimal ship berthing scheduling plan; The ship berthing plan evaluation model satisfies the following formula: in, It represents the evaluation value of the ship berthing plan. represents the port cost weight coefficient, Indicates the number of ships in the ship berthing plan, Indicates Vessel departure time, Indicates Ship arrival time, Indicates The cost per unit time of the ship in port, Indicates The cargo volume of the vessel, Indicates the cargo volume of all ships in the ship berthing plan, represents the weight coefficient of loading and unloading cost, Indicates Terminal facilities and equipment services Unit cost of the vessel, Indicates Terminal facilities and equipment services The workload of the ship, Indicates the number of terminal facilities and equipment, Indicates the number of ships; The method of evaluating the initial ship berthing scheduling plan by using the ship berthing plan evaluation model, adjusting the initial ship berthing scheduling plan according to the evaluation result, and obtaining the optimal ship berthing scheduling plan includes the following steps: Matrix-vectorizing the initial ship berthing scheduling plan, and adjusting the matrix-vectorized result to obtain a plurality of variant vectorized matrices; Using the ship berthing plan evaluation model to evaluate the ship berthing scheduling plan represented by the variation vector quantization matrix; According to the evaluation results, a first adjustment and a second adjustment are performed on the variation vector quantization matrix, and based on the adjustment results, iterative evaluation and adjustment are performed to obtain the optimal scheduling plan for ship berthing; The first adjustment of the mutation vectorization matrix satisfies the following formula: in, Indicates At the next iteration, The mutation vectorization matrix is ​​the mutation vectorization matrix after the first adjustment, represents the maximum number of iterations, Indicates the current iteration number, Indicates The first iteration mutation vectorization matrix, represents the number of cluster centers, Indicates The first iteration The mutation vectorization matrix is ​​added to the The probability of learning cluster centers belongs to A random number that satisfies , Indicates The first iteration Cluster centers, represents the adaptive learning factor, Indicates The optimal mutation vectorization matrix at the iteration; The second adjustment is made to the mutation vectorization matrix to satisfy the following formula: in, Indicates At the next iteration, The mutation vectorization matrix is ​​the mutation vectorization matrix after the second adjustment, Indicates The worst mutation vector quantization matrix evaluation value in iterations, Indicates The optimal mutation vectorization matrix evaluation value in the iteration, Indicates The first iteration mutation vectorization matrix, represents the adaptive learning factor, Represents the optimal mutation vectorization matrix of the historical iteration process, express A random number, Indicates The first iteration The inverse solution of the mutation vectorization matrix.

2. The intelligent ship berthing scheduling method according to claim 1 is characterized in that: The basic information of the vessel includes arrival time, cargo information and loading and unloading requirements, vessel size, draft and operating restrictions; The terminal facility equipment data includes berth data, tugboat data, ship unloader data, and conveyor belt data.

3. The intelligent ship berthing scheduling method according to claim 1 is characterized in that: The ship berthing rules include: Matching of ship cargo and berth loading and unloading machinery; The length of the vessel matches the length of the berth; The draft of the ship at the berth matches the tide time.

4. The intelligent ship berthing scheduling method according to claim 1 is characterized in that: The adjusting of the matrix vectorization result includes crossover adjustment and mutation adjustment.

5. The intelligent ship berthing scheduling method according to claim 1 is characterized in that: The adaptive learning factor satisfies the following formula: in, represents the adaptive learning factor, Indicates the current iteration number, Indicates the maximum number of iterations.

6. A ship berthing intelligent scheduling system, characterized in that: The ship berthing intelligent scheduling system includes: an input device, an output device, a processor, and a memory. The input device, output device, processor, and memory are interconnected. The memory includes program instructions, and the program instructions are used to execute the ship berthing intelligent scheduling method according to any one of claims 1-5.

Images