Method for acquiring target production scheduling plan of wharf and electronic device

Abstract

The invention discloses a target production scheduling plan acquisition method of a wharf and an electronic device, and relates to the technical field of production. The target production scheduling plan obtaining method comprises the steps that a berthing and departure planning model is constructed through production scheduling information, the berthing and departure planning model is solved for each port arrival sequence, the optimal port arrival sequence is obtained, and berthing information of each ship is determined through the optimal port arrival sequence; constructing a transport task mathematical model according to the production scheduling information, the optimal arrival sequence and the docking information of each ship, judging the berth state and the ship information through the transport task mathematical model, and dynamically arranging berthing, direct loading and entry and exit tasks at each discrete moment so as to obtain a transport task at each time period; determining storage yard information corresponding to the conveying task in each time period through a storage yard decision model; and according to the docking information of each ship, the conveying task of each time period and the corresponding storage yard information, a target production scheduling plan is generated and executed, and the wharf production scheduling execution efficiency is improved.

Description

[0001] This application is a divisional application of an invention patent application with an application date of November 14, 2024, application number 2024116205306, and name “A method and electronic device for obtaining a target production scheduling plan for a terminal”. Technical Field

[0002] The present invention relates to the field of production technology, and in particular to a method and an electronic device for acquiring a target production scheduling plan for a dock. Background Art

[0003] With the development of economy, the production level of the terminal is gradually improving, and the complexity of production scheduling is also increasing. At present, there is no auxiliary decision-making tool for the preparation of terminal production scheduling, which completely depends on the scheduling experience of the central control dispatcher. This method often leads to low execution efficiency.

[0004] Currently, no effective solution has been proposed for the problem of low efficiency in dock production scheduling execution in related technologies. Summary of the invention

[0005] In order to solve the problem of low execution efficiency of terminal production scheduling in the related art and improve the execution efficiency of terminal production scheduling, in a first aspect, the present invention proposes a method for obtaining a target production scheduling plan for a terminal, comprising: Obtain production scheduling information; The objective function and constraint function are set through the production scheduling information, and the berthing and unberthing planning model is constructed through the objective function and constraint function to obtain the ships that need to dock and the various arrival orders of each ship. The berthing and unberthing planning model is solved for each arrival order to obtain the optimal arrival order, and the berthing information of each ship is determined through the optimal arrival order. A mathematical model of transportation tasks is constructed based on production scheduling information, the optimal port arrival sequence, and the berthing information of each ship. The mathematical model of transportation tasks is used to determine the berth status and ship information, and dynamically arrange the berthing, direct loading, and entry and exit tasks at each discrete moment to obtain the transportation tasks for each time period. A yard decision model is built based on the production scheduling information and the transportation tasks in each time period. The yard information corresponding to the transportation tasks in each time period is determined through the yard decision model. The yard information includes the stacking allocation of inbound and outbound tasks, the use of stackers and reclaimers, and the update of the yard status. According to the docking information of each ship, the transportation task in each time period and the corresponding yard information, a target production schedule is generated and executed.

[0006] Furthermore, the production scheduling information includes: production scheduling demand task information, objective environment information, terminal static information and terminal dynamic information; the docking information includes: docking location and docking time.

[0007] Furthermore, the construction of the berthing and unberthing planning model is specifically as follows: Acquire a first data set, a first parameter group, and a first decision variable group corresponding to a berthing and unberthing planning model based on the production scheduling information; The objective function and the constraint condition function are set by using the first data set, the first parameter group and the first decision variable group, and the berthing and unberthing planning model is obtained by limiting the objective function by using the constraint condition function; Obtain the ships that need to dock and the various port arrival sequences of each ship; The berthing and unberthing planning model is solved for each arrival sequence to obtain the optimal arrival sequence, and the berthing information of each ship is determined by the optimal arrival sequence.

[0008] Furthermore, the objective function is expressed as: ; In the formula, represents the decision time frame, express Elements in Indicates time period Whether it meets the direct installation conditions, It means satisfaction; Indicates the latest time when all ships complete berthing operations; Parameter indicating the direct installation coefficient reduction.

[0009] Furthermore, the constraint function includes: The first constraint function is used to limit the latest time for all ships to complete berthing operations to be later than the departure time of all ships; The second constraint function is used to limit each ship to only the berth it can dock at; The third constraint function is used to limit the berthing time of each ship to be at least longer than the sum of the loading and unloading time and other process time; the other process time includes: Estimated operation time, per vessel The process time before loading and unloading is related to the ship time of berthing; The fourth constraint function is used to limit that the ships at the same berth must not overlap in time and leave time for berth preparation; The fifth constraint function is used to limit the ships at different berths to dock successively. If there is a conflict due to overlapping docking times, the ships must not overlap in time. The sixth constraint function is used to limit the berthing safety interval time for ships that berth at different berths one after another.

[0010] Furthermore, the construction of the mathematical model of the transport task is specifically as follows: The second data set, the second parameter set and the second decision variable set are set according to the production scheduling information, the optimal port arrival sequence and the berthing information of each ship; A transportation task mathematical model with decision variable calculation logic is constructed through the second data set, the second parameter group and the second decision variable set. By executing the decision variable calculation logic in the transportation task mathematical model, the berth status and ship information are judged, and the berthing, direct loading and entry and exit tasks at each discrete moment are dynamically arranged to obtain the transportation task for each time period.

[0011] Furthermore, the decision variable calculation logic includes: Get the current berth status at each discrete time; When the berth is idle, decide whether to arrange the ship to berth in the current period according to the berth preparation time, berthing time range, ship type conflict and safety interval time, and modify the current berth status based on the result of the decision; When the berth is busy, the remaining quantity and the quantity to be loaded of the ship are judged to decide whether to make direct loading task decisions and entry and exit task decisions.

[0012] Furthermore, the direct installation task decision is specifically as follows: The paired ships capable of direct loading are identified through direct loading conditions, and direct loading tasks are arranged for the paired ships; the paired ships refer to the pairing between the first-leg ship and the second-leg ship, ensuring that the cargo can be directly transferred from the first-leg ship to the second-leg ship; the first-leg ship refers to the ship that arrives at the terminal from the outside to unload; the second-leg ship refers to the ship that leaves the terminal after loading.

[0013] Furthermore, the direct installation conditions are specifically as follows: Determine whether there is a matching ship that can be directly loaded among the docked ships; Determine whether the ore type to be loaded and unloaded by the paired ship at this stage is the paired ore type corresponding to the paired ship; Determine whether the unloading stage is assembling the ore of the ship's second-leg ship owner; It is determined whether the remaining quantity of the directly loaded ore unloaded from the paired ship is greater than the quantity to be loaded.

[0014] Furthermore, the entry and exit task decision is specifically: Arrange the task of arranging the entry of ships into the yard and the exit of the yard to the ships.

[0015] Furthermore, the construction of the storage yard decision model is specifically as follows: A third data set, a third parameter group and a third decision variable set are set according to the production scheduling information and the delivery task in each time period; Constructing a storage yard decision model with an allocation model and a decision logic through a third data set, a third parameter group and a third decision variable set; The yard information corresponding to the transportation task in each time period is determined by executing the decision logic and allocation model in the yard decision model.

[0016] Furthermore, the decision logic comprises the steps of: Step 1: Arrange the transportation tasks into entry and exit task sets; Step 2, determine the stacking set that can be used by each task in the entry and exit task sets; Step 3, filter out bucket wheel machines that can perform tasks and their stacking sets that can work; Calculate the evaluation function influence value for each bucket wheel machine and the task it can work on, and select the optimal stack for each task based on the evaluation function influence value; Step 4: Obtain an allocation model by constructing a target allocation function and allocation constraints, and allocate each task to a feasible bucket wheel machine by solving the allocation model; Step 5: After the task allocation is completed, at the beginning of each time period, determine whether the current yard status and the task allocation given in the previous allocation model simultaneously meet multiple preset feasibility conditions. If so, proceed to the next step; if not, return to step 2; Step 6: Execute all entry and exit tasks in this time period according to the allocation results of the allocation model; Step 7: Update the status of the yard based on the execution status of all entry and exit tasks during this time period.

[0017] Furthermore, the allocation constraints include: In each discrete time period, each bucket wheel machine can only work on at most one workable task; Each task can only be assigned to one bucket wheel machine; Different bucket wheel machines cannot work on the same stack while doing their respective tasks in each discrete time period.

[0018] Furthermore, the target allocation function is used to maximize the sum of the impact values ​​of the evaluation functions corresponding to each task.

[0019] Furthermore, the feasibility conditions include: Whether all tasks in the current time period have been allocated and completed in the previous allocation model; Whether the stack arranged for the incoming task can still accommodate the amount of ore for the incoming task; Whether the stack arranged for the exit task still exists at this moment or whether it can meet the output of the ore.

[0020] In a second aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the target production scheduling plan acquisition method for the terminal described above.

[0021] In a third aspect, an embodiment of the present invention provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the intelligent production scheduling method for a terminal as described in the first aspect above.

[0022] In a fourth aspect, an embodiment of the present invention further provides a device for obtaining a target production scheduling plan of a terminal, comprising: Acquisition module, used to obtain production scheduling information; The berthing and unberthing module is used to set the objective function and constraint function through the production scheduling information, and to build the berthing and unberthing planning model through the objective function and constraint function, to obtain the ships that need to dock and the various arrival orders of the ships, to solve the berthing and unberthing planning model for each arrival order, to obtain the optimal arrival order, and to determine the berthing information of each ship through the optimal arrival order; The transportation task module is used to build a transportation task mathematical model based on production scheduling information, the optimal port arrival sequence, and the berthing information of each ship. Through the transportation task mathematical model, the berth status and ship information are judged, and the berthing, direct loading, and entry and exit tasks at each discrete moment are dynamically arranged to obtain the transportation task for each time period; The yard decision module is used to build a yard decision model based on the production scheduling information and the transportation tasks in each time period. The yard information corresponding to the transportation tasks in each time period is determined through the yard decision model. The yard information includes the stacking allocation of entry and exit tasks, the use of stackers and reclaimers, and the update of yard status; A generation module is used to generate and execute a target production schedule based on the docking information of each ship, the transportation task in each time period and the corresponding yard information.

[0023] Compared with the related art, the embodiment of the present application provides a method and an electronic device for obtaining a target production schedule for a terminal. A berthing and unberthing planning model is constructed through production scheduling information, and the berthing and unberthing planning model is solved for each port arrival sequence to obtain the optimal port arrival sequence, and the berthing information of each ship is determined through the optimal port arrival sequence; a transportation task mathematical model is constructed according to the production scheduling information, the optimal port arrival sequence and the berthing information of each ship, and the berthing task mathematical model is used to judge the berthing status and ship information, and dynamically arrange the berthing, direct loading and entry and exit tasks at each discrete moment to obtain the transportation tasks for each time period; the yard information corresponding to the transportation tasks for each time period is determined through the yard decision model; a target production schedule is generated and executed according to the berthing information of each ship, the transportation tasks for each time period and the corresponding yard information, thereby improving the efficiency of terminal production scheduling execution.

[0024] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0026] Figure 1 It is a hardware structure block diagram of a terminal of a method for acquiring a target production scheduling plan of a terminal according to an embodiment of the present invention;

[0027] Figure 2 is a flowchart of a method for obtaining a target production scheduling plan for a terminal according to an embodiment of the present application;

[0028] Figure 3 It is a structural block diagram of a device for acquiring a target production scheduling plan for a terminal according to an embodiment of the present application. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application. In addition, it can also be understood that although the efforts made in this development process may be complex and lengthy, for ordinary technicians in the field related to the contents disclosed in the present application, some changes such as design, manufacturing or production based on the technical contents disclosed in the present application are only conventional technical means, and should not be understood as insufficient contents disclosed in the present application.

[0030] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0031] Unless otherwise defined, the technical terms or scientific terms involved in this application should be understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the" and the like involved in this application do not indicate a quantity limitation, and may indicate the singular or plural. The terms "include", "comprise", "have" and any of their variations involved in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in this application means greater than or equal to two. "And / or" describes the association relationship of associated objects, indicating that there can be three relationships. For example, "A and / or B" can mean: A exists alone, A and B exist at the same time, and B exists alone. The terms "first", "second", "third" and the like involved in the present application are merely used to distinguish similar objects and do not represent a specific ordering of the objects.

[0032] The method embodiment provided in this embodiment can be executed in a terminal, a computer or a similar computing device. Taking running on a terminal as an example, Figure 1 1 is a hardware structure block diagram of a terminal of a method for acquiring a target production scheduling plan for a terminal according to an embodiment of the present invention. Figure 1 As shown, the terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Optionally, the terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is for illustration only and does not limit the structure of the above terminal. Figure 1More or fewer components as shown, or with Figure 1 Different configurations shown.

[0033] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the target production scheduling plan acquisition method of the terminal in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0034] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0035] This embodiment provides a method for obtaining a target production schedule for a terminal. Figure 2 is a flow chart of a method for obtaining a target production scheduling plan for a terminal according to an embodiment of the present application. Figure 2 As shown, the process includes the following steps:

[0036] Step S201, obtaining production scheduling information.

[0037] In this step, the production scheduling information includes: production scheduling demand task information, objective environment information, terminal static information and terminal dynamic information.

[0038] Among them, the production scheduling demand task information, objective environment information, terminal static information, and terminal dynamic information specifically include the following information: The production scheduling demand task information mainly includes the first-leg ship task, the second-leg ship task and the mixed ore task.

[0039] A ship mission mainly includes the following fields:

[0040] The second-pass ship task mainly includes the following field information:

[0041] The mixed ore task mainly includes the following field information:

[0042] Objective environmental information mainly includes day and night visibility, tides, and weather, which are key factors affecting production scheduling.

[0043] 1. Sunset and sunrise:

[0044] 2. Tidal data:

[0045] 3. Weather information:

[0046] The terminal static information mainly includes the static properties of the resources and equipment within the terminal, as follows:

[0047] 1. Ship loaders, ship unloaders, bucket wheel excavators, stackers, and reclaimers:

[0048] 2. Belt conveyor and process:

[0049] 3. Berth:

[0050] 4. Yard:

[0051] The terminal dynamic information mainly includes the internal resources, equipment and attributes of the docked ships at the start of the scheduling algorithm, as follows:

[0052] 1. Overall production target

[0053] 2. Ship loaders, ship unloaders, bucket wheel excavators, stackers, and reclaimers:

[0054] 3. Belt conveyor and process:

[0055] 4. Berths and berthed ships:

[0056] 5. Stacking in the yard:

[0057] Step S202, setting the objective function and constraint function through the production scheduling information, and constructing the berthing and unberthing planning model through the objective function and constraint function, obtaining the ships that need to dock and the multiple port arrival sequences of the ships, solving the berthing and unberthing planning model for each port arrival sequence, obtaining the optimal port arrival sequence, and determining the berthing information of each ship through the optimal port arrival sequence; The construction of the berthing and unberthing planning model is specifically as follows: Acquire a first data set, a first parameter group, and a first decision variable group corresponding to a berthing and unberthing planning model based on the production scheduling information; The objective function and the constraint condition function are set by using the first data set, the first parameter group and the first decision variable group, and the berthing and unberthing planning model is obtained by limiting the objective function by using the constraint condition function; Obtain the ships that need to dock and the various port arrival sequences of each ship; The berthing and unberthing planning model is solved for each arrival sequence to obtain the optimal arrival sequence, and the berthing information of each ship is determined by the optimal arrival sequence.

[0058] In this step, the docking information of each ship can be determined based on the order of arrival of loading and unloading ships, ship types, operation time, non-operation process time, procedure time, available berths, pairing of ships that can be directly loaded, tides, weather, sunrise and sunset, berth preparation time, safety combination rules and other information in the production scheduling information in step S201.

[0059] In this embodiment, the docking information may include a docking location and a docking time.

[0060] Specifically, step S202 can be executed in a pre-set berthing and unberthing module. The pre-set berthing and unberthing module needs to give the ships that need to dock and their arrival order in advance. For ships with priority, their arrival order can be advanced, and multiple arrival orders to be processed are obtained. For each feasible arrival order, the following berthing and unberthing planning model is solved to obtain the order with the best effect as the execution order. The calculation result of the berthing and unberthing planning model will give the berthing berth of each ship and the berthing order of ships at each berth. The important sets, parameters, and decision variables required to construct the berthing and unberthing planning model are listed below.

[0061] (I) The first data set (capital letters are set names, followed by lowercase letters are set elements, the same below) 1. Decision time frame , ; 2. One-way boat , ; 3. Two-way boat , ; 4. One-way boat type collection , ; 5. Collection of two-way ship types , ; 6. Each ship Permissible range of berthing time , ; 7. Each ship Permissible range of departure time , ; 8. Each ship A collection of available berths , ; 9. Berth and A combination of ship types that will cause conflicts if they dock one after another and some of their docking times overlap , ; 10. Theoretically, a pairing of one-way and two-way vessels that can be directly loaded , .

[0062] (II) The first parameter group 1. Number of ships per trip ; 2. Number of second-leg ships ; 3. Boat Ship Type ; 4. Each ship Estimated duration of the job ; 5. Each ship Process time before loading and unloading ; 6. Berth Preparation time between ship calls ; 7. Berth and Two types of ships docked successively and Required safe berthing interval time ; 8. Parameters for direct installation coefficient reduction .

[0063] 3. The first decision variable group 1. Boat Berth time ; 2. Boat Departure time ; 3. The latest time for all ships to complete berthing operations ; 4. Chengchuan Whether to rely on berth ; 5. Time period Is it later than the ship? Start time of loading and unloading , satisfy; 6. Time period Is it earlier than the ship? End of loading and unloading time , satisfy; 7. Time period Is it on a boat? During loading and unloading time , in; 8. Time period Is it on a boat? and During loading and unloading time , in; 9. Time period Whether it meets the direct installation conditions , satisfy.

[0064] The berthing and unberthing module can directly solve the value of each decision variable in the first decision variable group by constructing a berthing and unberthing planning model. The objective function and some key constraint condition functions of the berthing and unberthing planning model are described below.

[0065] 1. Objective function The objective function attempts to strike a balance between "completing a given task in the shortest possible time" and "creating as many direct installation opportunities as possible". The objective function formula is: .

[0066] (II) Constraint function 1. The first constraint function is used to limit the latest time for all ships to complete berthing operations to be later than the departure time of all ships: ; 2. The second constraint function is used to limit each ship to the berth it can dock at: ; 3. The third constraint function is used to limit the berthing time of each ship to be at least longer than the sum of the loading and unloading time and other process time; the other process time includes: Estimated operation time, per vessel The process time before loading and unloading is related to the ship Berthing time; the formula expression of the third constraint function is: ; 4. The fourth constraint function is used to limit the ships at the same berth to not overlap in time and to allow for berth preparation time: ; 5. The fifth constraint function is used to limit the ships at different berths to dock successively. If there is a conflict in the docking time, the ships must not overlap in time: ; 6. The sixth constraint function is used to limit the berthing safety interval time for ships to berth successively at different berths: ; 7. The seventh constraint function is used to limit The value of the variable: ; 8. The eighth constraint function is used to limit The value of the variable: ; 9. The ninth constraint function is used to limit The value of the variable: ; 10. The tenth constraint function is used to limit The value of the variable: ; 11. The eleventh constraint function is used to limit The value of the variable: ; 12. The twelfth constraint function is used to limit the range of variable values: the berthing and unberthing time must meet the requirements: ; 13. The thirteenth constraint function is used to limit the variable value range: 0-1 Variable requirements: 。

[0067] Step S203, constructing a mathematical model of the transportation task based on the production scheduling information, the optimal port arrival sequence, and the berthing information of each ship, and judging the berth status and ship information through the mathematical model of the transportation task, dynamically arranging the berthing, direct loading, and entry and exit tasks at each discrete moment, so as to obtain the transportation task for each time period; It should be noted that "judging ship information" mainly refers to evaluating and judging the current status and related information of the ship to ensure that the transportation task can be arranged reasonably. Specifically, judging ship information includes the following aspects:

[0068] 1. Vessel berthing information Berth location: the berth where the ship is currently docked; Dockage time: the vessel’s arrival time and estimated departure time; Vessel type: The type of vessel, which affects its docking and loading and unloading requirements.

[0069] 2. Vessel loading and unloading task information Mineral types and quantities: the mineral types and quantities that the ship needs to load and unload; Shipowner information: the amount of each shipper's ore, the type of single mixed pile, the allowable range of elements when mixing ore, etc.; Remaining quantity / to-be-loaded quantity: The current remaining quantity of ore or the quantity of ore to be loaded on the ship.

[0070] 3. Vessel’s operating status Operation time: the time it takes for a ship to complete the loading and unloading process; Process progress: currently completed processes and remaining processes; Equipment usage: the status of the ship unloaders, belts and other equipment currently used by the ship.

[0071] 4. Dynamic properties of ships Current status: whether the vessel is currently loading or unloading, or in a waiting state; Status at next moment: The expected status of the vessel at next moment, including whether it needs to leave the berth.

[0072] In other words, "judging ship information" mainly involves evaluating and judging the ship's berthing information, loading and unloading task information, operation status and dynamic attributes to ensure that the transportation tasks can be arranged reasonably. With this information, it can be ensured that the ship's berthing, direct loading and entry and exit tasks at each discrete moment can be carried out efficiently and orderly.

[0073] The construction of the mathematical model of the transport task is specifically as follows: The second data set, the second parameter set and the second decision variable set are set according to the production scheduling information, the optimal port arrival sequence and the berthing information of each ship; A transportation task mathematical model with decision variable calculation logic is constructed through the second data set, the second parameter group and the second decision variable set. By executing the decision variable calculation logic in the transportation task mathematical model, the berth status and ship information are judged, and the berthing, direct loading and entry and exit tasks at each discrete moment are dynamically arranged to obtain the transportation task for each time period.

[0074] In this step, the transportation task can be determined based on the optimal arrival order obtained in step S202, the ship's berthing information, and the upper limit of the unloaders used by the first-leg ship in the production scheduling information in step S201, the upper limit of the unloaders when directly loading on the second-leg ship, the elemental attributes of each type of ore on the first-leg ship, the single mixed stack types of different cargo owners, the elemental attribute requirements when mixing, the working efficiency of each major machine, and other information.

[0075] The delivery tasks may include but are not limited to the following: 1. How many processes are used for a certain mineral by each cargo owner on a ship, and how much weight is transferred to the yard by a single process; 2. How much weight is transferred directly from a first-leg ship to a second-leg ship berthed at the same time; 3. How much weight of a certain mineral of a certain owner is transferred from the yard to a certain second-leg ship; 4. How many ship unloaders are used for each entry and direct loading process; 5. Which approach belt is used for each approach and direct loading process; 6. Which specific process is used for each direct installation process; 7. Which ship loader is used for each exit process; 8. Berth status at each discrete moment.

[0076] Specifically, step S203 can be executed in a pre-set transport task module. After the berthing information of each ship is determined, the transport task module will then determine the transport task within each discrete time period. This part of the decision does not involve the pick-up and drop-off position of the yard, but only determines the time at which the ship is to be picked up. In the calculation, how much weight of a certain mineral on the corresponding ship is transferred to the yard, or how much weight is directly transferred to a second-pass ship berthed at the same time; at the same time, how much weight of a certain mineral is transferred from the yard to a second-pass ship. Of course, the number of unloaders to be scheduled and the belts to be used will also be determined here. The following lists the important sets, parameters, and decision variables of the mathematical model of the transportation task in the transportation task module. The content already given in the berthing and unberthing modules will not be repeated here.

[0077] 1. Second Data Set 1. Mineral collection for each ship , .

[0078] (II) Second parameter group 1. One boat trip The maximum number of unloaders allowed to be used simultaneously ; 2. Two-way boat Maximum number of unloaders allowed to be used simultaneously for direct loading ; 3. Each ship Loading and unloading Mineral species , Mineral volume , element attributes ; 4. Each ship Loading and unloading The first Name of the consignor , mineral quantity , Single mixed stack type , 0 single pile, 1 non-mixed ore, 2 mixed ore; 5. Each ship Loading and unloading The first The allowable range of elements when mixing ore for each consignor ; 6. Theoretically, direct loading can be paired with one or two vessels Paired mineral species ; 7. Theoretically, direct loading can be paired with one or two vessels Name of the second-leg shipper ; 8.Total number of ship unloaders ; 9. Material reclaiming efficiency of a single ship unloader per unit time period ; 10. Bucket wheel excavator single machine reclaiming efficiency per unit time period .

[0079] (III) Second decision variable set 1. Time Ship To the ship Direct shipment ; 2. Time Ship Goods entering the yard ; 3. Time Ship Number of ship unloaders used in the yard ; 4. Time yard to ship Goods shipped .

[0080] The decision variables in the second decision variable set are calculated by executing the decision variable calculation logic in the transport task mathematical model; the decision variable calculation logic includes: Get the current berth status at each discrete time; When the berth is idle, decide whether to arrange the ship to berth in the current period according to the berth preparation time, berthing time range, ship type conflict and safety interval time, and modify the current berth status based on the result of the decision; When the berth is busy, the remaining quantity and the quantity to be loaded of the ship are judged to decide whether to make direct loading task decisions and entry and exit task decisions.

[0081] It should be noted that:

[0082] 1. The remaining volume refers to the volume of ore that still needs to be completed after the ship has completed part of the loading and unloading tasks. Specifically: For unloading berths: the amount of ore not yet unloaded from the vessel; For loading berths: the amount of ore on the yard that has not yet been loaded onto the vessel.

[0083] 2. The amount to be loaded refers to the amount of ore that the ship needs to load and unload in the current time period. Specifically: For unloading berths: the volume of ore that the vessel is about to start unloading; For loading berths: the amount of ore on the yard that is about to be loaded onto the vessel.

[0084] In detail, the decision variable calculation logic includes:

[0085] 1) Input the current berth status at each discrete time, that is, the dynamic attributes listed in the information list.

[0086] 2) Complete the instantaneous decision of the free berth at each discrete moment and the state transition to the next moment. First, determine whether the berth preparation time is full. If not, no berthing will be arranged, and it will still be free at the beginning of the next time period; if it is full, determine whether the current time period is within the allowable range of the next ship's berthing time. If not, no berthing will be arranged, and it will still be free at the next moment; if it is, determine whether the ship type of the next berthing ship conflicts with the ships parked at other berths. If there is a conflict, no berthing will be arranged, and it will still be free at the next moment; if there is no conflict, determine whether the ship type of the next berthing ship and the ships parked at other berths have completed the berthing safety interval time. If not, no berthing will be arranged, and it will still be free at the beginning of the next time period; if it meets the requirements, arrange the next ship to berth, change the current state of the berth, and record the information of the next ship. The conflict combination and safety interval time are used for unloading berths.

[0087] 3) When the berth is busy, if the ship has not completed the pre-loading and unloading process time, the transportation task will not be arranged, and the remaining process time of one period will be reduced at the beginning of the next moment, and the other attributes will remain unchanged; if the pre-loading and unloading process has been completed, it is determined whether there is any remaining quantity / quantity to be loaded. If so, the direct loading task decision and state transfer are completed first, and then the entry and exit task decision and state transfer are completed; if there is no remaining quantity / quantity to be loaded, it is determined whether the current time period belongs to the allowable range of the current ship's departure time. If not, the initial attributes of the next moment remain unchanged; if it does, the ship is arranged to leave the berth, and the current state of the berth is changed to idle, and the initial berth preparation time at the next moment is increased by one period.

[0088] in:

[0089] The direct installation task decision is specifically as follows: The paired ships capable of direct loading are identified through direct loading conditions, and direct loading tasks are arranged for the paired ships; the paired ships refer to the pairing between the first-leg ship and the second-leg ship, ensuring that the cargo can be directly transferred from the first-leg ship to the second-leg ship; the first-leg ship refers to the ship that arrives at the terminal from the outside to unload; the second-leg ship refers to the ship that leaves the terminal after loading.

[0090] The direct installation conditions are specifically: Determine whether there is a matching ship that can be directly loaded among the docked ships; Determine whether the ore type to be loaded and unloaded by the paired ship at this stage is the paired ore type corresponding to the paired ship; Determine whether the unloading stage is assembling the ore of the ship's second-leg ship owner; It is determined whether the remaining quantity of the directly loaded ore unloaded from the paired ship is greater than the quantity to be loaded.

[0091] The entry and exit task decision is specifically as follows: Arrange the task of arranging the entry of ships into the yard and the exit of the yard to the ships.

[0092] In detail: Complete the direct loading task decision, specifically: identify the judgment rules for direct loading operations (i.e. direct loading conditions); determine whether there is a ship in the collection among the stopped ships; The paired ship in → Determine whether the ore type to be loaded and unloaded by the paired ship at this stage is → Determine whether the unloading stage is loading The owner's ore → Determine whether the remaining quantity of the paired ship's direct-loaded ore after unloading is greater than the quantity to be loaded.

[0093] If the above rules are met, then the paired ships that meet the rules The prerequisite for direct installation is met. However, it is necessary to ensure that each paired ship that is finally arranged for direct installation They must be different, because the second-pass vessel can only use one process at a time. It is also necessary to ensure that the total number of unloaders does not exceed , unloading The total number of ship unloaders used shall not exceed After the above conditions are met, for each paired ship Arrange a direct installation process and unloader, it is possible to determine the direct transfer of each pair within the period. Quantitative The next moment, Boat Reduction in the amount of mineral residues , Boat The amount of ore to be loaded is reduced .

[0094] It needs to be explained that “the final arrangement of direct shipment for each paired ship must be different" Refers to the identifier of the second-leg ship. Specifically, this ensures that when performing direct loading operations, each pair of first-leg ship and second-leg ship is unique, and there will not be multiple first-leg ships directly loading with the same second-leg ship. Among them, direct loading: refers to the process in which the first-leg ship directly transfers the goods to the second-leg ship without intermediate storage in the yard.

[0095] When all possible direct assembly ships are completed or there is no direct assembly ship, the next step is to make an entry and exit task decision. The entry and exit task decision specifically includes:

[0096] For the unloading berth, the approach operation is carried out: the approach process is arranged for the ships with remaining capacity according to the rule of 2 or 3 unloaders with one belt, and 3 are given priority. It is necessary to ensure that the number of approach belts called does not exceed the remaining available after the direct loading decision, and the number of unloaders called does not exceed the remaining available after the direct loading decision. The total number of ship unloaders used shall not exceed If unloading A total of If a ship unloader is used for the yard, it can be determined that the ship will be transferred to the yard during this period. The order of minerals is as follows: The order in which the goods are unloaded is as follows. The amount of minerals After all uninstallation is completed, start uninstallation Planting mines. The next moment, The remaining amount of the mineral species being unloaded by the ship is decreasing .

[0097] Carry out exit operation for loading berth: it can be determined that the yard needs to be transferred to the ship during this period The order of minerals is as follows: The order in which the goods are loaded is in sequence. The amount of minerals After all the installation is completed, start installing Planting mines. The next moment, The amount of minerals that are currently being loaded on the ship has decreased . Elemental attributes and other mine-related parameters will be allocated to each entry and exit task and used as input to the yard module.

[0098] Step S204, constructing a yard decision model based on the production scheduling information and the transportation tasks in each time period, and determining the yard information corresponding to the transportation tasks in each time period through the yard decision model, the yard information including the stacking allocation of the inbound and outbound tasks, the use of the stacker-reclaimer, and the update of the yard status; The construction of the storage yard decision model is specifically as follows: A third data set, a third parameter group and a third decision variable set are set according to the production scheduling information and the delivery task in each time period; Constructing a storage yard decision model with an allocation model and a decision logic through a third data set, a third parameter group and a third decision variable set; The yard information corresponding to the transportation task in each time period is determined by executing the decision logic and allocation model in the yard decision model.

[0099] In this step, the stockyard information corresponding to the transportation task in each time period can be determined according to the transportation task obtained in step S203 and the mixed ore task and stockyard attributes in the production scheduling information in step S201.

[0100] The yard information may include but is not limited to: 1. Each on-site transport task will enter which existing stacks or start a new stack at which scale position in which yard; 2. The length and direction of the existing stack need to be extended, as well as the length and direction of the new stack; 3. From which stack will each exit conveying task be taken? 4. Which stacker-reclaimer is used for each entry and exit task; 5. At each discrete moment, which three stackers are used to take materials from which two piles and to stack the finished ore on which pile or to start a new pile at which scale position in which stockpile; 6. Which two stackers are used to retrieve materials from which stack and stack them to which stack at each discrete moment; 7. The state of the yard at each discrete moment.

[0101] Specifically, step S204 can be executed in a pre-set yard decision module. After the transportation task decision in the above embodiment is determined, the yard decision will be determined next, which includes which stacks the above-mentioned entry transportation tasks will enter, which stacks the exit transportation tasks will be taken out from, which bucket wheels will be used, and how to use the remaining bucket wheels for mixed ore operations. The following lists the important sets, parameters, and decision variables of the yard decision model in the yard decision module. The contents already given in the berthing and unberthing modules and the transportation task module described in the above embodiments will not be repeated here.

[0102] 1. The third data set 1. On-site missions within the time frame , ; 2. Missions within time , ; 3. Bucket wheel excavator , ; 4. Mixed ore batches , ; 5. Stacking in yard , ; 6. Finished ore stacking Batch collection , ; 7. Yard and aisle , ; 8. Entry and exit tasks Optional stacking in accordance with the mineral properties , ; 9. Bucket wheel excavator Working stacking , ; 10. Bucket wheel excavator Tasks that can be worked on , ; 11. Bucket wheel excavator Work Tasks Optional stacking , .

[0103] (II) The third parameter group 1. Mission The amount of minerals ; 2. Mission Types of Minerals ; 3. Mission The mineral density ; 4. Mineral Type In the pile Upper limit ; 5. Mixed ore batches The remaining mixed ore ; 6. Mixed ore batches Finished Minerals ; 7. Mixed ore batches Silicon content of finished ore ; 8. Mixed ore batches Raw material minerals 1 ; 9. Mixed ore batches Raw material minerals 2 ; 10. Stacking Starting position ; 11. Stacking The end position ; 12. Stacking The pile road ; 13. Stacking The amount of minerals ; 14. Stacking Types of Minerals ; 15. Stacking Element attributes ; 16. Stacking The eigenvector of ; 17. Bucket wheel excavator Work Tasks Optimal stacking at ; 18. Bucket wheel excavator Work Tasks The impact value of the evaluation function on the yard .

[0104] (III) The third decision variable set 1. Whether to assign tasks Assigned to bucket wheel machine .

[0105] By executing the decision logic and the allocation model in the yard decision model, the decision variables in the third decision variable set are calculated, and the yard information corresponding to the transportation task in each time period is determined; The decision logic includes the steps of: Step 1: Arrange the transportation tasks into entry and exit task sets; Step 2, determine the stacking set that can be used by each task in the entry and exit task sets; Step 3, filter out bucket wheel machines that can perform tasks and their stacking sets that can work; Calculate the evaluation function influence value for each bucket wheel machine and the task it can work on, and select the optimal stack for each task based on the evaluation function influence value; Step 4: Obtain an allocation model by constructing a target allocation function and allocation constraints, and allocate each task to a feasible bucket wheel machine by solving the allocation model; Step 5: After the task allocation is completed, at the beginning of each time period, determine whether the current yard status and the task allocation given in the previous allocation model simultaneously meet multiple preset feasibility conditions. If so, proceed to the next step; if not, return to step 2; Step 6: Execute all entry and exit tasks in this time period according to the allocation results of the allocation model; Step 7: Update the status of the yard based on the execution status of all entry and exit tasks during this time period.

[0106] in: The allocation constraints include: In each discrete time period, each bucket wheel machine can only work on at most one workable task; Each task can only be assigned to one bucket wheel machine; Different bucket wheel machines cannot work on the same stack while doing their respective tasks in each discrete time period.

[0107] The target allocation function is used to maximize the sum of the impact values ​​of the evaluation functions corresponding to each task.

[0108] The feasibility conditions include: Whether all tasks in the current time period have been allocated and completed in the previous allocation model; Whether the stack arranged for the incoming task can still accommodate the amount of ore for the incoming task; Whether the stack arranged for the exit task still exists at this moment or whether it can meet the output of the ore.

[0109] For the decision logic in the yard decision model, let’s talk about it in detail:

[0110] Step 1: Arrange the decision variables in the transport module into the input set of the yard module and Please note that if multiple processes are arranged for unloading, they also need to be recorded as different tasks and assigned to different bucket wheel machines later.

[0111] Step 2: According to the task The mineral types, elemental properties, allowed elemental property ranges, and the mineral types and elemental properties of the existing stacks are used to determine whether it is possible to work on the stacks. , the entry mission Can be an empty set, which means that an empty field is needed. If it is an empty set, it means that the first-leg ship corresponding to the second-leg ship will unload in the next time or the finished ore will be mixed out in the next time, and the allocation model will not assign this task. Therefore, at least The allocation model needs to be executed again before the discrete time of the task. The task is assigned to a bucket wheel machine.

[0112] Step 3: If a bucket wheel machine of Included A subset of , and the bucket wheel machine can complete The task type (in or out) is belong .if belong , for and The intersection of .

[0113] For every bucket wheel machine And the tasks it can work on calculate , the yard status scoring function is , Used to control the implementation of longer idle gaps: , , is a convex function, which means that when the idle gap becomes longer, the marginal contribution to the scoring function gradually decreases. Are adjacent stacks on the same aisle The length of the gap between ), or the gap between the head and tail of the stack closest to the edge. Used to control the dispersion of minerals on the pile: , , which means that the more aisles the same mineral occupies, the better, but occupying too many aisles will reduce the marginal benefit. of Is a vector that records the types of minerals in each pile The weight and.

[0114] The task Arrange in Each stack in In, according to Calculate the length of the updated stack after this task is completed; if If it is an empty set, stack it from the free gap; thus, calculate the impact on the evaluation function, select the stack with the largest positive impact, and assign its value to .

[0115] Step 4: Construct the following allocation model to allocate tasks to bucket wheel machines and solve them directly. Ensure that in each discrete time period, each bucket wheel machine can only work on one working task at most; each task can only be assigned to one bucket wheel machine; in each discrete time period, different bucket wheel machines cannot work on the same stack when doing their respective tasks, which can ensure the problem of safety limit.

[0116] The formula expression of the target allocation function is: ;

[0117] The formula expression of the allocation constraint is: ; ; .

[0118] Step 5. After the task allocation is completed, at the beginning of each time period, the current stockpile status and the stacking arrangement given in the last allocation model are judged as follows: whether all the tasks in this discrete time period have been given in the last allocation model; whether the stacking arranged for the incoming task can still accommodate the ore volume of the incoming task; whether the stacking arranged for the outgoing task still exists or can meet the outgoing ore volume. When all three conditions are met, proceed to the next step; when one of the three conditions is not met, a new round of allocation is required, that is, return to step 2.

[0119] Step 6. Execute all inbound and outbound tasks in this time period according to the results of the allocation model. Identify idle belts and idle bucket wheels in this time period. The belts that need attention are: ABC line of the second part of the inbound and A line of the outbound. Combined with the idle bucket wheels, the first set of available blending processes can be determined. If the first set of blending processes is idle, the first set is used first. Then determine the available second set of blending processes based on the number of idle bucket wheels. As long as there are unfinished blending batches, they will be arranged in this discrete time period.

[0120] Step 7: Transfer the stockpile state to the next discrete time period according to the entry and exit tasks and the ore mixing tasks. If the stockpile does not generate a new stack, directly cover the original stack or extend the number of meters; if a new stack is generated, leave or do not leave a gap for material removal according to requirements. The length of the stack is reduced from left to right.

[0121] Step 8: After all the entry and exit arrangements within the decision time range are completed, identify the process number to be used based on the selection of belt conveyor and bucket wheel excavator.

[0122] Step S205, generating and executing a target production schedule based on the berthing information of each ship, the transportation tasks in each time period and the corresponding yard information.

[0123] Through the above steps S201 to S205, a berthing and unberthing planning model is constructed through the production scheduling information, and the berthing and unberthing planning model is solved for each port arrival sequence to obtain the optimal port arrival sequence, and the berthing information of each ship is determined through the optimal port arrival sequence; a transportation task mathematical model is constructed according to the production scheduling information, the optimal port arrival sequence and the berthing information of each ship, and the berthing status and ship information are judged through the transportation task mathematical model, and the berthing, direct loading and entry and exit tasks at each discrete moment are dynamically arranged to obtain the transportation tasks for each time period; the yard information corresponding to the transportation tasks for each time period is determined through the yard decision model; the target production scheduling plan is generated and executed according to the berthing information of each ship, the transportation tasks for each time period and its corresponding yard information, which solves the problem of low execution efficiency of terminal production scheduling schemes in related technologies and improves the execution efficiency of terminal production scheduling.

[0124] In some of the embodiments, before determining the berthing information of each ship based on the production scheduling information, the production scheduling information may be preprocessed, and the preprocessing is used to process the production scheduling information into a collection and parameter form.

[0125] In this embodiment, the pre-processing method may include multiple aspects: integrating parameters such as the length of the ship's remaining processes to ensure that the time of all relevant processes is considered when calculating the total berthing time of the ship; integrating the influence of environmental factors such as weather and tides into a set of time periods in which the ship can berth and leave the berth to ensure the safety and feasibility of the ship's berthing and leaving the berth; integrating berth berthing safety rules into conflict combination sets and safety time interval parameters to avoid conflicts in berth use and ensure safety intervals; integrating the mineral element attribute requirements of different cargo owners into a stacking set that can be selected for each entry and exit task to meet the mineral element attribute requirements of different cargo owners. These pre-processing measures help optimize the arrangement of ship berthing and leaving the berth and yard tasks and improve overall operational efficiency.

[0126] In some of the embodiments, determining the docking information of each ship includes: determining the priority of each ship in the production scheduling information according to the production scheduling information; and determining the docking information of each ship according to the priority of each ship.

[0127] In this embodiment, by determining the berthing information of each ship by priority, each ship can be better utilized, thereby further improving the reliability of the production scheduling plan.

[0128] In this embodiment, in addition to rule-based prioritization: this can be based on multiple factors, such as the size of the ship, the urgency of the cargo, the expected waiting time, port contract terms, weather conditions, tide and water depth requirements, etc. For example, large ships or ships carrying perishable cargo may be given higher priority.

[0129] Through the above process, port managers can effectively manage ship calls and maximize port throughput while ensuring safety and customer satisfaction.

[0130] In some of the embodiments, the target production schedule is displayed visually to facilitate user viewing and data monitoring.

[0131] In some of the embodiments, the logs of generating and executing the target production schedule are saved and visualized for users to view and monitor data. When implementing the target production schedule acquisition device, recording and visualizing the logs is crucial, which not only helps to monitor the operating status of the device, but also provides a basis for subsequent analysis and optimization.

[0132] In some of the embodiments, after the target production schedule is generated and executed, it can also be determined whether emergency data input by the user is received, wherein the emergency data includes: emergency ship docking data; if it is determined that the emergency data input by the user is received, the target production schedule is adjusted according to the emergency data.

[0133] In this embodiment, the target production schedule (i.e., the predetermined scheduling arrangement) is first generated and executed. However, due to the uncertainty in actual operation, such as sudden weather changes, equipment failures, urgent order requirements, etc., the device needs to have flexibility to deal with these emergencies.

[0134] When the device receives "urgent data", such as a ship that suddenly needs to dock without prior notice, or a ship that was originally scheduled to dock needs to be handled first for some reason, the device will trigger a reassessment and adjustment of the current production schedule. This adjustment may involve reallocating resources, changing the order of operations, optimizing path planning, etc., to minimize delays and costs while ensuring that new constraints are met.

[0135] In practice, this usually requires a module that can receive and parse emergency data in real time, as well as an intelligent optimization algorithm that can quickly calculate the optimal production schedule after taking into account new information. This process may involve methods and techniques in fields such as operations research, machine learning, and artificial intelligence.

[0136] For example, for emergency ship call data, the device may need to make planning adjustments taking into account the following factors: (1) The size of the vessel and the type of berth required; (2) the time and resources required for cargo loading and unloading; (3) The impact of other scheduled vessels and operations; (4) berth availability and capacity limitations; (5) Regulatory and safety requirements.

[0137] In this way, the device can maintain its efficiency and adaptability, allowing it to respond optimally even in the face of unpredictable events.

[0138] This embodiment also provides a device for obtaining a target production schedule for a terminal, which is used to implement the above-mentioned embodiments and preferred implementation modes, and will not be repeated here. As used below, the terms "module", "unit", "subunit", etc. can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0139] Figure 3is a structural block diagram of a device for acquiring a target production schedule for a terminal according to an embodiment of the present application, such as Figure 3 As shown, the device comprises: An acquisition module 31 is used to acquire production scheduling information; The berthing and unberthing module 32 is coupled to the acquisition module 31, and is used to set the objective function and the constraint condition function through the production scheduling information, and to construct the berthing and unberthing planning model through the objective function and the constraint condition function, to obtain the ships that need to be docked and the various port arrival sequences composed of the ships, to solve the berthing and unberthing planning model for each port arrival sequence, to obtain the optimal port arrival sequence, and to determine the berthing information of each ship through the optimal port arrival sequence; The transport task module 33 is coupled to the berthing and unberthing module 32, and is used to construct a transport task mathematical model based on the production scheduling information, the optimal port arrival sequence, and the berthing information of each ship, and through the transport task mathematical model, judge the berth status and ship information, and dynamically arrange the berthing, direct loading and entry and exit tasks at each discrete moment to obtain the transport task for each time period; The yard decision module 34 is coupled to the transport task module 33 and is used to build a yard decision model based on the production scheduling information and the transport tasks in each time period, and determine the yard information corresponding to the transport tasks in each time period through the yard decision model. The yard information includes the stacking allocation of the inbound and outbound tasks, the use of the stacker and reclaimer, and the update of the yard status; The generation module 35 is coupled to the yard decision module 34, and is used to generate and execute a target production schedule according to the docking information of each ship, the transportation task in each time period and the corresponding yard information.

[0140] In some of the embodiments, the device further includes: a first display module for visually displaying the target production schedule.

[0141] In some of the embodiments, the device further includes: a second display module for saving and visually displaying a log of generating and executing the target production schedule.

[0142] It should be noted that the above modules can be functional modules or program modules, and can be implemented by software or hardware. For modules implemented by hardware, the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0143] This embodiment further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0144] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0145] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program: S1, obtain production scheduling information; S2, setting the objective function and constraint function through the production scheduling information, and constructing the berthing and unberthing planning model through the objective function and constraint function, obtaining the ships that need to dock and the various port arrival sequences of the ships, solving the berthing and unberthing planning model for each port arrival sequence, obtaining the optimal port arrival sequence, and determining the berthing information of each ship through the optimal port arrival sequence; S3, builds a mathematical model of transportation tasks based on production scheduling information, the optimal port arrival sequence, and the berthing information of each ship. Through the mathematical model of transportation tasks, the berth status and ship information are judged, and the berthing, direct loading, and entry and exit tasks at each discrete moment are dynamically arranged to obtain the transportation tasks for each time period; S4, building a yard decision model based on the production scheduling information and the transportation tasks in each time period, and determining the yard information corresponding to the transportation tasks in each time period through the yard decision model. The yard information includes the stacking allocation of the inbound and outbound tasks, the use of the stacker-reclaimer, and the update of the yard status; S5, generating and executing a target production schedule according to the docking information of each ship, the transportation task in each time period and the corresponding yard information.

[0146] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0147] In addition, in combination with the target production scheduling acquisition method of the terminal in the above embodiment, the embodiment of the present application can provide a storage medium for implementation. The storage medium stores a computer program; when the computer program is executed by the processor, any of the target production scheduling acquisition methods of the terminal in the above embodiment is implemented.

[0148] Those skilled in the art should understand that the technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0149] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for obtaining a target production schedule of a terminal, characterized in that: include: Obtain production scheduling information; The production scheduling information includes: production scheduling demand task information, objective environment information, terminal static information and terminal dynamic information; The optimal port arrival sequence is obtained through the production scheduling information, and the berthing information of each ship is determined through the optimal port arrival sequence; the berthing information includes: berthing location and berthing time; A mathematical model of transportation tasks is constructed based on production scheduling information, the optimal port arrival sequence, and the berthing information of each ship. The mathematical model of transportation tasks is used to determine the berth status and ship information, and dynamically arrange the berthing, direct loading, and entry and exit tasks at each discrete moment to obtain the transportation tasks for each time period. The construction of the mathematical model of the transport task is specifically as follows: The second data set, the second parameter set and the second decision variable set are set according to the production scheduling information, the optimal port arrival sequence and the berthing information of each ship; A transportation task mathematical model with decision variable calculation logic is constructed through the second data set, the second parameter group and the second decision variable set, and the berth status and ship information are judged by executing the decision variable calculation logic in the transportation task mathematical model, and the berthing, direct loading and entry and exit tasks at each discrete moment are dynamically arranged to obtain the transportation task in each time period; The decision variable calculation logic includes: Get the current berth status at each discrete time; When the berth is idle, decide whether to arrange the ship to berth in the current period according to the berth preparation time, berthing time range, ship type conflict and safety interval time, and modify the current berth status based on the result of the decision; When the berth is busy, determine the remaining quantity and quantity to be loaded on the ship, and decide whether to make direct loading task decisions and entry and exit task decisions; The direct installation task decision is specifically as follows: Identify paired ships that can be directly loaded through direct loading conditions, and arrange direct loading tasks for the paired ships; the paired ships refer to the pairing between the first-leg ship and the second-leg ship, ensuring that the cargo can be directly transferred from the first-leg ship to the second-leg ship; the first-leg ship refers to the ship that arrives at the terminal from outside to unload; the second-leg ship refers to the ship that leaves the terminal after loading; The direct installation conditions are specifically: Determine whether there is a matching ship that can be directly loaded among the docked ships; Determine whether the ore type to be loaded and unloaded by the paired ship at this stage is the paired ore type corresponding to the paired ship; Determine whether the unloading stage is assembling the ore of the ship's second-leg ship owner; Determine whether the remaining quantity of the directly loaded ore unloaded from the paired ship is greater than the quantity to be loaded; A yard decision model is built based on the production scheduling information and the transportation tasks in each time period. The yard information corresponding to the transportation tasks in each time period is determined through the yard decision model. The yard information includes the stacking allocation of inbound and outbound tasks, the use of stackers and reclaimers, and the update of the yard status. According to the docking information of each ship, the transportation task in each time period and the corresponding yard information, a target production schedule is generated and executed.

2. A method for obtaining a target production schedule for a terminal according to claim 1, characterized in that: The entry and exit task decision is specifically as follows: Arrange the task of arranging the entry of ships into the yard and the exit of the yard to the ships.

3. The method for obtaining a target production schedule of a terminal according to claim 2, characterized in that: The construction of the yard decision model is specifically as follows: A third data set, a third parameter group and a third decision variable set are set according to the production scheduling information and the delivery task in each time period; Constructing a storage yard decision model with an allocation model and a decision logic through a third data set, a third parameter group and a third decision variable set; The yard information corresponding to the transportation task in each time period is determined by executing the decision logic and allocation model in the yard decision model.

4. A method for obtaining a target production schedule for a terminal according to claim 3, characterized in that: The decision logic includes the steps of: Step 1: Arrange the transportation tasks into entry and exit task sets; Step 2, determine the stacking set that can be used by each task in the entry and exit task sets; Step 3, filter out bucket wheel machines that can perform tasks and their stacking sets that can work; Calculate the evaluation function influence value for each bucket wheel machine and the task it can work on, and select the optimal stack for each task based on the evaluation function influence value; Step 4: Obtain an allocation model by constructing a target allocation function and allocation constraints, and allocate each task to a feasible bucket wheel machine by solving the allocation model; Step 5: After the task allocation is completed, at the beginning of each time period, determine whether the current yard status and the task allocation given in the previous allocation model simultaneously meet multiple preset feasibility conditions. If so, proceed to the next step; if not, return to step 2; Step 6: Execute all entry and exit tasks in this time period according to the allocation results of the allocation model; Step 7: Update the status of the yard based on the execution status of all entry and exit tasks during this time period.

5. A method for obtaining a target production schedule for a terminal according to claim 4, characterized in that: The allocation constraints include: In each discrete time period, each bucket wheel machine can only work on at most one workable task; Each task can only be assigned to one bucket wheel machine; Different bucket wheel machines cannot work on the same stack while doing their respective tasks in each discrete time period.

6. A method for obtaining a target production schedule for a terminal according to claim 5, characterized in that: The target allocation function is used to maximize the sum of the impact values ​​of the evaluation functions corresponding to each task.

7. A method for obtaining a target production schedule for a terminal according to claim 6, characterized in that: The feasibility conditions include: Whether all tasks in the current time period have been allocated and completed in the previous allocation model; Whether the stack arranged for the incoming task can still accommodate the amount of ore for the incoming task; Whether the stack arranged for the exit task still exists at this moment or whether it can meet the output of the ore.

8. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to execute the method for obtaining a target production scheduling plan for a terminal according to any one of claims 1 to 7.

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