A port material scheduling optimization simulation method and device, electronic equipment and medium
By constructing a simulation method in port material scheduling, and using multiple algorithms and real-time data acquisition to optimize the scheduling of dry bulk cargo flow, the problem of low unloading efficiency of dry bulk cargo was solved, and efficient and safe operation of port material movement was achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA WATERBORNE TRANSPORT RES INST
- Filing Date
- 2024-03-13
- Publication Date
- 2026-07-24
AI Technical Summary
Existing port terminal material movement scheduling methods are difficult to optimize efficiently when handling dry bulk cargo unloading, especially in cases of large quantities of dry bulk cargo, resulting in low unloading efficiency.
By constructing a port material scheduling optimization simulation method, and utilizing path planning algorithms, task scheduling algorithms, queuing theory algorithms, and machine learning algorithms, combined with real-time data acquisition and equipment control, the real-time positioning and control of dry bulk cargo flow is realized, thereby optimizing the port material movement scheduling.
It has enabled efficient scheduling of dry bulk cargo flow, improved the efficiency and safety of port material movement, and ensured the stable operation of the terminal.
Smart Images
Figure CN117952393B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of port terminal material movement and scheduling technology, and in particular to a port material scheduling optimization simulation method, device, electronic equipment and medium. Background Technology
[0002] Port terminal material movement scheduling is a complex process that requires consideration of many factors, including the type and quantity of goods, mode of transport, and time. Scheduling personnel need to rationally arrange the loading, unloading, transportation, and stacking of goods based on these factors to ensure terminal operational efficiency and cargo safety. The basic steps of existing port terminal material movement scheduling are as follows:
[0003] Planning and Scheduling: Develop a detailed scheduling plan based on cargo arrival times, loading and unloading capacity, and transportation arrangements. Cargo Classification: Classify cargo according to type, size, weight, and other factors, and assign it to appropriate storage areas. Loading and Unloading Arrangements: Arrange suitable loading and unloading equipment and personnel based on cargo classification and transportation methods. Transportation Arrangements: Arrange suitable transportation vehicles, such as ships and vehicles, based on the quantity and destination of the cargo. Monitoring and Adjustment: During the scheduling process, it is necessary to monitor the loading and unloading progress, transportation status, and storage conditions of the cargo in real time, and make adjustments based on the actual situation.
[0004] With the development of technology, advanced scheduling algorithms and technologies, such as artificial intelligence and big data analytics, are being adopted to improve the efficiency of material movement and scheduling at port terminals. At the same time, it is also necessary to strengthen communication and collaboration with relevant departments to ensure the smooth operation of scheduling work.
[0005] Existing port terminal material movement scheduling methods have the following problems in practical use;
[0006] Dry bulk cargo: For dry bulk cargo unloading, the belt conveyor simulates the unloading effect of the cargo and can calculate the unloading speed. Liquid bulk cargo: When the quantity of dry bulk cargo is huge, it is necessary to optimize the overall scheduling of dry bulk cargo to improve scheduling efficiency. To solve the above problems, a port material scheduling optimization simulation method has been developed. Summary of the Invention
[0007] The purpose of this invention is to provide a port material scheduling optimization simulation method, device, electronic equipment, and medium that supports real-time interaction between the support system and key objects such as terminal loading and unloading equipment, terminal horizontal transportation equipment, terminal yard equipment, and terminal loading and unloading equipment, enabling real-time material positioning and control command interaction. The terminal operating system, equipment control system, remote control system, and equipment under each system interact in real time to implement commands and provide real-time feedback on the current material status. Information flow interaction and coupling among the systems realize remote real-time online material movement control functions. This allows for simulation testing of port material movement scheduling optimization. The technical solution of this application provides a simulation of dry bulk cargo flow movement. After adjusting the parameters of the simulation scheme before the simulation begins, the simulation scheme can be performed according to the designed parameters, and finally, the data is summarized and analyzed.
[0008] In a first aspect, the present invention provides a port material scheduling optimization simulation method, comprising:
[0009] Obtain information on port roads, dock materials, and dock equipment;
[0010] Based on the port's road information, the terminal's material information, and the terminal's equipment information, a logical simulation strategy for optimizing the movement scheduling of dry bulk cargo terminals at the port is constructed.
[0011] Collect real-time information on port roads, dock materials, and dock equipment. Substitute this information into the port dry bulk cargo terminal's logical movement scheduling optimization simulation strategy logic to generate real-time optimized port dry bulk cargo terminal movement guidance information.
[0012] Based on real-time optimized port dry bulk cargo terminal movement guidance information, the simulation is performed to determine when a ship enters the dry bulk cargo berth, activates the dry bulk cargo controller, queries the existing conveyor belt system and the status of the dry bulk cargo yard, starts the dry bulk cargo belt conveyor and the unloading machine on the dry bulk cargo yard, and obtains simulation information.
[0013] The simulation data is optimized to obtain the port material movement scheduling optimization simulation results.
[0014] Furthermore, real-time information on port roads, dock materials, and dock equipment is collected. This real-time information is then incorporated into the port material movement scheduling optimization simulation strategy logic to generate real-time optimized port dock material movement guidance information, including:
[0015] The port material movement scheduling optimization simulation strategy logic includes path planning algorithm, task scheduling algorithm, queuing theory algorithm, simulation optimization algorithm and machine learning algorithm;
[0016] Obtain port material movement scheduling simulation requirement information, and match it with the port material movement scheduling optimization simulation strategy logic according to the port material movement scheduling simulation requirement information to obtain the algorithm matching result;
[0017] Furthermore, it also includes: the algorithm matching result is a path planning algorithm;
[0018] The acquired port road information, dock material information, and dock equipment information are substituted into the preset path planning algorithm;
[0019] The path planning algorithm obtains the starting point, target point, and wharf map information from the port material movement scheduling simulation demand information. The wharf map information includes walkable areas, obstacles, vehicle driving areas, and movement cost information of handling equipment.
[0020] Substitute the starting point, target point, and wharf map information into the heuristic function of the preset real-time port material movement scheduling simulation algorithm to initialize the data, establish an open information list and a closed information list, substitute the starting point into the open information list, perform data detection loop, and when the open list is not empty, execute the following steps;
[0021] Select the node with the smallest f value from the list of open information, f = g + h, where g is the actual cost from the starting point to the current node, and h is the cost from the current node to the target node estimated by the heuristic function.
[0022] Move the selected node from the open list to the closed list.
[0023] If the current node is the target node, then the path from the starting node to the current node is returned as the optimal path.
[0024] Otherwise, expand the neighboring nodes of the current node:
[0025] For each neighboring node, if it is not in the closed list: calculate the cost from the starting node to that neighboring node; if the neighboring node is not in the open list, or the cost to reach that neighboring node through the current node is lower, add the neighboring node to the open list and set its parent node to the current node.
[0026] Starting from the target node, backtrack through the parent node to the starting node, record the nodes on the path, return the recorded path as the optimal path, and output the optimal path for the dock.
[0027] Furthermore, it also includes: the algorithm matching result is a task scheduling algorithm;
[0028] The acquired port road information, dock material information, and dock equipment information are input into the preset task scheduling algorithm.
[0029] The task scheduling algorithm obtains a task list and a handling equipment list from the port material movement scheduling simulation demand information. The task list includes the starting position, target position, cargo information, and priority. The handling equipment list includes the equipment status (idle / busy), location, handling capacity, handling cargo capacity, and time constraints. The algorithm then obtains a task scheduling plan, which includes the execution order of each task, the allocated equipment, and the execution time.
[0030] Perform task initialization, and create a task queue and equipment status table based on the task list and the list of moving equipment;
[0031] Sort all tasks from highest to lowest priority and place them in the task queue;
[0032] When the task queue is not empty, perform the following steps: Select the highest priority task from the task queue, find available handling equipment, traverse the equipment status table, find equipment with a status of "idle", evaluate whether the equipment is suitable for executing the current task based on its current location and capabilities, if a suitable equipment is found, assign it to the current task and update the equipment status to "busy", add the current task to the scheduling plan and specify the execution time and the assigned equipment, remove the scheduled task from the task queue, if no suitable equipment is found, decide whether to wait for available equipment or abandon the task based on the task's priority and time constraints.
[0033] Optimization algorithms are introduced to optimize task scheduling plans, such as reducing equipment travel distance and balancing equipment load.
[0034] Consider the dependencies and collaborative operations between tasks to ensure maximum overall handling efficiency. Output a scheduling plan, including the execution order of each task, the allocated equipment, and the execution time.
[0035] Furthermore, the logic for optimizing the movement scheduling of the dry bulk cargo terminal also includes;
[0036] Obtain equipment type, quantity, dispatch time, work location, and point-to-point work instructions;
[0037] Collect spatial location information of various structures of dock loading and unloading equipment and dock yard equipment under remote control, as well as scanning information of ship type and material pile;
[0038] Detect whether the material has completed a point-to-point quantitative movement, including whether the material has completed the movement from one location to another within the dock;
[0039] When remote control intervention is required, the remote operator sends a request for manual intervention to the automatic control system.
[0040] Furthermore, the logic for optimizing the movement scheduling of the dry bulk cargo terminal also includes;
[0041] Acquire equipment allocation, develop and plan equipment operation schedules, and optimize operation time.
[0042] It receives fault information from dock loading and unloading equipment and autonomously determines the fault based on the status signals of sensors.
[0043] Determine the spatial location information of each structure of the dock's ship loading and unloading equipment, as well as the scanning information of the ship type;
[0044] Determine whether the materials have been moved from the ship's cargo hold and dock loading / unloading equipment to the dock's horizontal transport equipment or from the dock's horizontal transport equipment to the dock loading / unloading equipment and ship's cargo hold, whether the flow rate meets the planned requirements, and whether the operations at the equipment handover point are effectively connected.
[0045] Generate operational status information based on the dock loading and unloading equipment.
[0046] Furthermore, the logic for optimizing the movement scheduling of the dry bulk cargo terminal also includes;
[0047] When a piece of equipment experiences an emergency shutdown or startup due to a malfunction or emergency, the related equipment on the unified process line must also be shut down or started accordingly.
[0048] In a second aspect, the present invention provides a port material movement scheduling optimization simulation device, comprising:
[0049] Acquisition device: Acquires port road information, dock material information, and dock equipment information;
[0050] The port material movement scheduling optimization simulation device constructs port material movement scheduling optimization simulation strategy logic based on port road information, terminal material information, and terminal equipment information. It collects real-time information on port road information, terminal material information, and terminal equipment information, and inputs this real-time information into the port material movement scheduling optimization simulation strategy logic to generate real-time optimized port terminal material movement guidance information.
[0051] Thirdly, the present invention provides an electronic device, comprising:
[0052] A processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of any of the methods described above.
[0053] Fourthly, the present invention provides a readable medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of any one of the above steps.
[0054] The beneficial effects of the present invention are as follows: The present invention provides a port material scheduling optimization simulation method, which, in order to realize the port material movement scheduling optimization simulation design, performs rapid simulation of data structure layout based on external data input or according to the actual terminal layout.
[0055] This technical solution establishes a material movement process mechanism, key nodes, key objects, and their relationships at a container terminal, as shown in Figure 1. It supports real-time interaction between the support system and key objects such as terminal loading and unloading equipment, terminal horizontal transport equipment, terminal yard equipment, and inbound / outbound loading and unloading equipment, enabling real-time material positioning and control command interaction. The terminal operating system, equipment control system, and remote control system interact in real-time with the equipment under each system to implement commands and provide real-time feedback on the current material status. Information flow interaction and coupling among the systems achieve remote real-time online material movement control. This technical solution provides a simulation of dry bulk cargo logistics movement. After adjusting the parameters of the simulation scheme before the simulation begins, the simulation scheme can be performed according to the designed parameters, and finally, the data can be summarized and analyzed. Attached Figure Description
[0056] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.
[0057] Figure 1 The present invention provides a judgment logic for a dry bulk cargo berth control system. Detailed Implementation
[0058] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. The technical solutions provided by various embodiments of this invention will be described in detail below with reference to the accompanying drawings.
[0059] Dry bulk terminal logic: When a vessel enters the dry bulk berth, the dry bulk controller is activated. The controller queries the existing conveyor system and the status of the dry bulk yard to start the dry bulk belt conveyors and the unloading machines in the dry bulk yard. At this time, the opening and closing of three different conveyor belts can also be controlled via the dynamic control in the lower right corner. The quantity of dry bulk cargo (coal) is dynamically expressed based on the amount of cargo unloaded. The coal pile in the coal yard will gradually increase until the storage capacity in the dry bulk cargo (coal) yard reaches its limit, at which point the conveyor system will automatically shut down.
[0060] Data statistics logic: During unloading operations, all machinery and the storage yard generate data for background statistics. After statistics are collected, the data is updated promptly when the summary page is clicked.
[0061] The purpose of simulation verification;
[0062] Based on a focused study of the material movement process mechanism at dry bulk terminals and a comprehensive understanding of the interactive information related to material movement, this paper outlines the control logic structure required for remote real-time online material movement control, as shown in the figure, starting from the hierarchical levels of key objects and key nodes, to ensure the safe and stable operation of the terminal. The remote online material movement control system for dry bulk terminals includes an automatic control system, a management information system, a remote control system, and a support system. It integrates advanced technologies such as network architecture, system coupling, operation management, and cyber-physical systems to achieve real-time feedback on online material movement in production processes, comprehensive management, maintenance, and other aspects. Real-time control of material movement is implemented through real-time network information interaction. The support system interacts in real-time with key objects such as terminal loading and unloading equipment, terminal horizontal transport equipment, terminal yard equipment, inbound and outbound loading and unloading equipment, and terminal operation auxiliary equipment, achieving information fusion, command coordination, and online dynamic management to realize real-time material positioning and control. The automatic control system, management information system, and remote control system interact in real-time with equipment under each system to execute commands and provide real-time feedback on the current material status. The information flow interaction and coupling of each system realizes the remote real-time online material movement control function.
[0063] In a first aspect, the present invention provides a port material scheduling optimization simulation method, comprising:
[0064] Obtain information on port roads, dock materials, and dock equipment;
[0065] Based on the port's road information, the terminal's material information, and the terminal's equipment information, a logical simulation strategy for optimizing the movement scheduling of dry bulk cargo terminals at the port is constructed.
[0066] Collect real-time information on port roads, dock materials, and dock equipment. Substitute this information into the port dry bulk cargo terminal's logical movement scheduling optimization simulation strategy logic to generate real-time optimized port dry bulk cargo terminal movement guidance information.
[0067] Based on real-time optimized port dry bulk cargo terminal movement guidance information, the simulation is performed to determine when a ship enters the dry bulk cargo berth, activates the dry bulk cargo controller, queries the existing conveyor belt system and the status of the dry bulk cargo yard, starts the dry bulk cargo belt conveyor and the unloading machine on the dry bulk cargo yard, and obtains simulation information.
[0068] The simulation data is optimized to obtain the port material movement scheduling optimization simulation results.
[0069] In this embodiment, the technical solution of this application first obtains the port's road information, the terminal's material information, and the terminal's equipment information, and then constructs and optimizes the movement scheduling strategy through an algorithm function. Finally, we will simulate the scenario after a ship enters a dry bulk berth and obtain simulation information.
[0070] Here is a simple example code written in Python:
[0071]
[0072]
[0073] In this example, we define the `PortInfo` class to store port information and have a `collect_real_time_info` method to simulate real-time information collection. The `DryBulkController` class represents the dry bulk controller, responsible for managing the conveyor system and yard status, as well as startup operations. The `build_optimization_strategy` function is used to build optimization strategies, and the `simulate_ship_arrival` function simulates the operations after a ship enters its berth.
[0074] The technical solution presented in this application involves details such as specific optimization algorithms, real-time data acquisition, and equipment control logic during practical use. In actual applications, these parts need to be designed and implemented according to specific circumstances. For example, the optimization strategy may need to consider multiple factors such as path planning, equipment utilization efficiency, and material handling costs, while real-time data acquisition may involve integration with sensors and monitoring systems.
[0075] Furthermore, real-time information on port roads, dock materials, and dock equipment is collected. This real-time information is then incorporated into the port material movement scheduling optimization simulation strategy logic to generate real-time optimized port dock material movement guidance information, including:
[0076] The port material movement scheduling optimization simulation strategy logic includes path planning algorithm, task scheduling algorithm, queuing theory algorithm, simulation optimization algorithm and machine learning algorithm;
[0077] Obtain port material movement scheduling simulation requirement information, and match it with the port material movement scheduling optimization simulation strategy logic according to the port material movement scheduling simulation requirement information to obtain the algorithm matching result;
[0078] Furthermore, it also includes: the algorithm matching result is a path planning algorithm;
[0079] The acquired port road information, dock material information, and dock equipment information are substituted into the preset path planning algorithm;
[0080] The path planning algorithm obtains the starting point, target point, and wharf map information from the port material movement scheduling simulation demand information. The wharf map information includes walkable areas, obstacles, vehicle driving areas, and movement cost information of handling equipment.
[0081] Substitute the starting point, target point, and wharf map information into the heuristic function of the preset real-time port material movement scheduling simulation algorithm to initialize the data, establish an open information list and a closed information list, substitute the starting point into the open information list, perform data detection loop, and when the open list is not empty, execute the following steps;
[0082] Select the node with the smallest f value from the list of open information, f = g + h, where g is the actual cost from the starting point to the current node, and h is the cost from the current node to the target node estimated by the heuristic function.
[0083] Move the selected node from the open list to the closed list.
[0084] If the current node is the target node, then the path from the starting node to the current node is returned as the optimal path.
[0085] Otherwise, expand the neighboring nodes of the current node:
[0086] For each neighboring node, if it is not in the closed list:
[0087] Calculate the cost from the starting node to this neighboring node;
[0088] If the neighboring node is not in the open list, or if it is cheaper to reach the neighboring node through the current node, then add the neighboring node to the open list and set its parent node to the current node;
[0089] Starting from the target node, backtrack through the parent node to the starting node, record the nodes on the path, return the recorded path as the optimal path, and output the optimal path for the dock.
[0090] Once the vessel enters the dry bulk berth, the dry bulk controller is activated. The dry bulk controller will then query the existing conveyor system and the status of the dry bulk yard, and start the dry bulk belt conveyors and the unloading machines in the dry bulk yard.
[0091] Furthermore, it also includes: the algorithm matching result is a task scheduling algorithm;
[0092] The acquired port road information, dock material information, and dock equipment information are input into the preset task scheduling algorithm.
[0093] The task scheduling algorithm obtains a task list and a handling equipment list from the port material movement scheduling simulation demand information. The task list includes the starting position, target position, cargo information, and priority. The handling equipment list includes the equipment status (idle / busy), location, handling capacity, handling cargo capacity, and time constraints. The algorithm then obtains a task scheduling plan, which includes the execution order of each task, the allocated equipment, and the execution time.
[0094] Perform task initialization, and create a task queue and equipment status table based on the task list and the list of moving equipment;
[0095] Sort all tasks from highest to lowest priority and place them in the task queue;
[0096] When the task queue is not empty, perform the following steps: Select the highest priority task from the task queue.
[0097] Find available handling equipment, traverse the equipment status table, find equipment with a status of "idle", assess whether the equipment is suitable for performing the current task based on its current location and capabilities, if a suitable equipment is found, assign it to the current task and update the equipment status to "busy", add the current task to the scheduling plan, specify the execution time and the assigned equipment, remove the scheduled task from the task queue, if no suitable equipment is found, decide whether to wait for available equipment or abandon the task based on the task's priority and time constraints.
[0098] Optimization algorithms are introduced to optimize task scheduling plans, such as reducing equipment travel distance and balancing equipment load.
[0099] Consider the dependencies and collaborative operations between tasks to ensure maximum overall handling efficiency. Output a scheduling plan, including the execution order of each task, the allocated equipment, and the execution time.
[0100] Furthermore, the logic for optimizing the movement scheduling of the dry bulk cargo terminal also includes;
[0101] Obtain equipment type, quantity, dispatch time, work location, and point-to-point work instructions;
[0102] Collect spatial location information of various structures of dock loading and unloading equipment and dock yard equipment under remote control, as well as scanning information of ship type and material pile;
[0103] Detect whether the material has completed a point-to-point quantitative movement, including whether the material has completed the movement from one location to another within the dock;
[0104] When remote control intervention is required, the remote operator sends a request for manual intervention to the automatic control system.
[0105] Furthermore, the logic for optimizing the movement scheduling of the dry bulk cargo terminal also includes;
[0106] Acquire equipment allocation, develop and plan equipment operation schedules, and optimize operation time.
[0107] It receives fault information from dock loading and unloading equipment and autonomously determines the fault based on the status signals of sensors.
[0108] Determine the spatial location information of each structure of the dock's ship loading and unloading equipment, as well as the scanning information of the ship type;
[0109] Determine whether the materials have been moved from the ship's cargo hold and dock loading / unloading equipment to the dock's horizontal transport equipment or from the dock's horizontal transport equipment to the dock loading / unloading equipment and ship's cargo hold, whether the flow rate meets the planned requirements, and whether the operations at the equipment handover point are effectively connected.
[0110] Generate operational status information based on the dock loading and unloading equipment.
[0111] Furthermore, the logic for optimizing the movement scheduling of the dry bulk cargo terminal also includes;
[0112] When a piece of equipment experiences an emergency shutdown or startup due to a malfunction or emergency, the related equipment on the unified process line must also be shut down or started accordingly.
[0113] In a second aspect, the present invention provides a port material movement scheduling optimization simulation device, comprising:
[0114] Acquisition device: Acquires port road information, dock material information, and dock equipment information;
[0115] The port material movement scheduling optimization simulation device constructs port material movement scheduling optimization simulation strategy logic based on port road information, terminal material information, and terminal equipment information. It collects real-time information on port road information, terminal material information, and terminal equipment information, and inputs this real-time information into the port material movement scheduling optimization simulation strategy logic to generate real-time optimized port terminal material movement guidance information.
[0116] Thirdly, the present invention provides an electronic device, comprising:
[0117] A processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of any of the methods described above.
[0118] Fourthly, the present invention provides a readable medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of any one of the above steps.
[0119] Specifically, based on the above control logic, the control mechanism for collaboration and cooperation between key nodes and key objects in a dry bulk terminal is shown in the table below. Table: Collaborative Control Mechanism between Key Objects and Key Nodes in a Dry Bulk Terminal;
[0120] Simulation verification content;
[0121] Based on the above collaborative control mechanism, the simulation verification of collaborative control between key objects and key nodes in a dry bulk cargo terminal mainly verifies the following aspects:
[0122] 1. Inter-system interaction: This includes the transmission of work instructions, such as instructions on equipment type, quantity, dispatch time, and work location, as well as feedback on the execution status of work instructions. This information ensures that equipment operates as planned and materials move as required.
[0123] 2. System-to-equipment interaction: Information exchange between different types of work equipment, including assignment, equipment planning, work status, fault information, etc., to ensure coordinated operation between equipment.
[0124] 3. Inter-equipment interaction: The interaction of interlocking control signals between equipment to respond to faults or emergencies, ensuring that relevant equipment stops or starts accordingly to maintain safe and coordinated operation.
[0125] 4. Remote control: Operators can remotely control certain devices to adapt to specific needs or situations, while monitoring device status and feedback information.
[0126] The collaborative control mechanism within this system ensures the efficient operation of the terminal, enabling different equipment and objects to work in coordination to minimize congestion and maximize overall efficiency.
[0127] Example of simulation verification process
[0128] When a vessel is not yet berthed, the berth status is either waiting or ready, and the corresponding oil pump unit carries an idle and not ready flag. Oil pump units carrying the not ready flag will not enter the dry bulk conveyor allocation block. When a vessel begins to berth, the berth control activates the barge system, and the barge pushes the vessel into the berth. When the vessel begins berthing, the not ready flag for that berth will switch to the barge berthing flag. After the berth's waiting-for-berthing phase ends, the barge berthing flag will be changed back to the vessel berthing complete and ready flag. At this point, the oil pump unit status switches to the vessel berthing ready state. The oil pump unit then enters the conveyor allocation block.
[0129] The conveyor belt allocation block first determines the type of dry bulk cargo carried by the ship and activates the corresponding dry bulk cargo conveyor belt accordingly. The yard storage selection method involves checking if the current yard storage capacity is at its maximum limit. If it is, the input is cut off, and the yard storage status is changed to full, removed from the input list. At this point, a status check is performed, closing full or disabled yard storage to make way for subsequent available yard storage inputs.
[0130] The technical solution of this application realizes a port material movement scheduling optimization simulation design system, which simulates the operation process of dry bulk cargo flow and simulates the movement of materials on ships, cranes, vehicles, and dock yards based on the real-time arrival of ships. The function of the material movement scheduling system is verified through 3D real-scene simulation.
[0131] The simulation system can quickly simulate the data structure layout based on external data input or the actual terminal layout. The simulation platform provides simulation of the logistics movement of four types of cargo: containers, general cargo, dry bulk cargo, and liquid bulk cargo. After adjusting the parameters of the simulation scheme before the simulation starts, the simulation scheme can be simulated according to the designed parameters, and finally the data can be summarized and analyzed.
[0132] Those skilled in the art will clearly understand that the techniques in the embodiments of the present invention can be implemented using control software and necessary general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as an in-vehicle computer, an internet cloud server, a hard disk, ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions to cause a computer device (which may be an in-vehicle computer, a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments or certain parts of the embodiments of the present invention. The above-described embodiments of the present invention do not constitute a limitation on the scope of protection of the present invention.
Claims
1. A simulation method for optimizing port material scheduling, characterized in that, include; Obtain information on port roads, dock materials, and dock equipment; Based on the port's road information, the terminal's material information, and the terminal's equipment information, a logical simulation strategy for optimizing the movement scheduling of dry bulk cargo terminals at the port is constructed. Real-time information on port roads, terminal materials, and terminal equipment is collected. This information is then fed into the port's dry bulk cargo terminal logical movement scheduling optimization simulation strategy logic to generate real-time optimized port terminal dry bulk cargo terminal movement guidance information, including: The port material movement scheduling optimization simulation strategy logic includes path planning algorithm, task scheduling algorithm, queuing theory algorithm, simulation optimization algorithm and machine learning algorithm; Obtain port material movement scheduling simulation requirement information, and match it with the port material movement scheduling optimization simulation strategy logic according to the port material movement scheduling simulation requirement information to obtain the algorithm matching result; Acquire equipment allocation, develop and plan equipment operation schedules, and optimize operation time. It receives fault information from dock loading and unloading equipment and autonomously determines the fault based on the status signals of sensors. Determine the spatial location information of each structure of the dock's ship loading and unloading equipment, as well as the scanning information of the ship type; Determine whether the materials have been moved from the ship's cargo hold and dock loading / unloading equipment to the dock's horizontal transport equipment or from the dock's horizontal transport equipment to the dock loading / unloading equipment and ship's cargo hold, whether the flow rate meets the planned requirements, and whether the operations at the equipment handover point are effectively connected. Generate operational status information based on the dock loading and unloading equipment; Based on real-time optimized port dry bulk cargo terminal movement guidance information, the simulation is performed to determine when a ship enters the dry bulk cargo berth, activates the dry bulk cargo controller, queries the existing conveyor belt system and the status of the dry bulk cargo yard, starts the dry bulk cargo belt conveyor and the unloading machine on the dry bulk cargo yard, and obtains simulation information. The simulation data is optimized to obtain the port material movement scheduling optimization simulation results.
2. The port material scheduling optimization simulation method as described in claim 1, characterized in that, Also includes: The matching result of the algorithm is a path planning algorithm; The acquired port road information, dock material information, and dock equipment information are substituted into the preset path planning algorithm; The path planning algorithm obtains the starting point, target point, and wharf map information from the port material movement scheduling simulation demand information. The wharf map information includes walkable areas, obstacles, vehicle driving areas, and movement cost information of handling equipment. Substitute the starting point, target point, and wharf map information into the heuristic function of the preset real-time port material movement scheduling simulation algorithm to initialize the data, establish an open information list and a closed information list, substitute the starting point into the open information list, perform data detection loop, and when the open list is not empty, execute the following steps; Select the node with the smallest f value from the list of open information, f = g + h, where g is the actual cost from the starting point to the current node, and h is the cost from the current node to the target node estimated by the heuristic function. Move the selected node from the open list to the closed list; If the current node is the target node, then return the path from the starting node to the current node as the optimal path; Otherwise, expand the neighboring nodes of the current node: For each neighboring node, if it is not in the closed list: Calculate the cost from the starting node to this neighboring node; If the neighboring node is not in the open list, or if it is cheaper to reach the neighboring node through the current node, then add the neighboring node to the open list and set its parent node to the current node; Starting from the target node, backtrack through the parent node to the starting node, record the nodes on the path, return the recorded path as the optimal path, and output the optimal path for the dock.
3. The port material scheduling optimization simulation method as described in claim 2, characterized in that, Also includes: The matching result of the algorithm is a task scheduling algorithm; The acquired port road information, dock material information, and dock equipment information are substituted into the preset task scheduling algorithm; The task scheduling algorithm obtains a task list and a handling equipment list from the port material movement scheduling simulation demand information. The task list includes the starting position, target position, cargo information, and priority. The handling equipment list includes the idle / busy status of the equipment, its location, handling capacity, cargo carrying capacity, and time constraints. The algorithm then obtains a task scheduling plan, which includes the execution order of each task, the allocated equipment, and the execution time. Perform task initialization, and create a task queue and equipment status table based on the task list and the list of moving equipment; Sort all tasks in descending order of priority and place them in the task queue; When the task queue is not empty, perform the following steps: Select the highest priority task from the task queue, find available handling equipment, traverse the equipment status table, find equipment with a status of "idle", evaluate whether the equipment is suitable for executing the current task based on its current location and capabilities, if a suitable equipment is found, assign it to the current task and update the equipment status to "busy", add the current task to the scheduling plan and specify the execution time and the assigned equipment, remove the scheduled task from the task queue, if no suitable equipment is found, decide whether to wait for available equipment or abandon the task based on the task's priority and time constraints; An optimization algorithm is introduced to optimize the task scheduling plan and output the final scheduling plan, which includes the execution order of each task, the allocated equipment, and the execution time.
4. The port material scheduling optimization simulation method as described in claim 1, characterized in that, The simulation strategy logic for optimizing the logical movement scheduling of dry bulk cargo terminals also includes: Obtain equipment type, quantity, dispatch time, work location, and point-to-point work instructions; Collect spatial location information of various structures of dock loading and unloading equipment and dock yard equipment under remote control, as well as scanning information of ship type and material pile; Detect whether the material has completed a point-to-point quantitative movement, including whether the material has completed the movement from one location to another within the dock; When remote control intervention is required, the remote operator sends a request for manual intervention to the automatic control system.
5. The port material scheduling optimization simulation method as described in claim 1, characterized in that, The simulation strategy logic for optimizing the logical movement scheduling of dry bulk cargo terminals also includes: When a piece of equipment experiences an emergency shutdown or startup due to a malfunction or emergency, the related equipment on the unified process line must also be shut down or started accordingly.
6. A port material movement optimization simulation device, characterized in that, include: Acquisition device: Acquires port road information, dock material information, and dock equipment information; A port material movement scheduling optimization simulation device, wherein the port material movement scheduling optimization simulation device implements the steps of the method as described in any one of claims 1-5.
7. An electronic device, characterized in that, include: A processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method as described in any one of claims 1-5.
8. A readable medium, characterized in that, The readable medium stores a program or instructions that, when executed by a processor, implement the steps of the method as described in any one of claims 1-5.