CN117952393A - Port material scheduling optimization simulation method and device, electronic equipment and medium - Google Patents
Port material scheduling optimization simulation method and device, electronic equipment and medium Download PDFInfo
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Abstract
The invention relates to the technical field of port and dock material movement scheduling, in particular to a port material scheduling optimization simulation method, a port material scheduling optimization simulation device, electronic equipment and media, which simulate the operation process of dry bulk cargo logistics, and simulate the movement process of materials on ships, cranes, vehicles and dock yards according to the real-time arrival condition of the ships. Through 3D live-action simulation, the function of the system for material movement scheduling is verified. The simulation system performs a fast simulation of the data structure layout according to external data input or fast according to the actual dock layout. The simulation platform provides simulation of the movement of the dry bulk cargo logistics, parameters of a simulation scheme are adjusted before the simulation is started, and data can be summarized and analyzed finally according to the designed parameter simulation scheme.
Description
Technical Field
The invention relates to the technical field of port and dock material mobile scheduling, in particular to a port material scheduling optimization simulation method, a port material scheduling optimization simulation device, electronic equipment and media.
Background
The material movement scheduling of the port and dock is a complex process, and many factors including the type, quantity, transportation mode, time and the like of the goods need to be considered. The dispatcher needs to arrange the loading, unloading, transporting and stacking of goods reasonably according to these factors to ensure the operation efficiency of the wharf and the safety of the goods. The basic steps of the existing port and dock material mobile scheduling are as follows:
Scheduling: a detailed dispatch plan is formulated based on arrival time, loading and unloading capacity and transportation schedule of the cargo. Sorting goods: the goods are classified and arranged to the corresponding stacking areas according to the kinds, sizes, weights, etc. of the goods. Handling arrangement: suitable handling equipment and personnel are arranged according to the classification and transportation of the goods. A transportation arrangement: depending on the number of goods and the destination, suitable means of transportation, such as ships, vehicles, etc., are arranged. Monitoring and adjusting: in the dispatching process, the loading and unloading progress, the transportation state and the stacking condition of the cargoes need to be monitored in real time, and the cargoes are adjusted according to actual conditions.
Along with the development of the age, advanced scheduling algorithms and technologies, such as artificial intelligence, big data analysis and the like, are adopted to improve the efficiency of material movement scheduling of ports and wharfs. Meanwhile, communication and cooperation with related departments are required to be enhanced, and smooth scheduling work is ensured.
The existing port and dock material movement scheduling method has the following problems in the actual use process;
Dry bulk: for dry bulk cargo unloading, the belt conveyor simulates the unloading effect of cargoes, and the unloading speed of cargoes can be calculated. When the number of the liquid bulk cargos is huge, the whole dispatching optimization of the dry bulk cargos is needed, so that the dispatching efficiency of the dry bulk cargos is improved. In order to solve the problems, the port material scheduling optimization simulation method is developed.
Disclosure of Invention
The application aims to provide a port material dispatching optimization simulation method, a port material dispatching optimization simulation device, electronic equipment and media, which support real-time interaction of a guarantee system with key objects such as dock front loading and unloading equipment, dock horizontal transportation equipment, dock yard equipment, dock loading and unloading equipment and the like, and realize real-time positioning of materials and interaction of control instructions. The wharf operating system, the equipment control system and the remote control system interact with equipment under each system in real time, implement instructions and feed back the state of the current materials in real time. And the information flows of the systems are interactively coupled, so that a remote real-time online material movement control function is realized. Thereby realizing the optimal simulation test of the port material mobile scheduling. According to the technical scheme, the simulation of the movement of the dry bulk cargo logistics is provided, parameters of a simulation scheme are adjusted before the simulation is started, and data can be summarized and analyzed finally according to the designed parameter simulation scheme.
In a first aspect, the invention provides a port material scheduling optimization simulation method, which comprises the following steps of;
acquiring road information of a port, material information of a wharf and wharf equipment information;
constructing a port dry bulk cargo wharf logic mobile scheduling optimization simulation strategy logic according to the road information of the port, the material information of the wharf and the wharf equipment information;
Acquiring road information of a port, material information of a dock and real-time information of dock equipment information, substituting the road information of the port, the material information of the dock and the real-time information of the dock equipment information into a port dry bulk cargo dock logic mobile scheduling optimization simulation strategy logic, and generating real-time optimization port dry bulk cargo dock mobile guiding information;
according to the real-time optimized port and dock dry bulk cargo dock movement guiding information, after a ship enters a dry bulk cargo berth, a dry bulk cargo controller is activated, the dry bulk cargo controller inquires the existing conveyor belt system and the state of a dry bulk cargo yard, and a dry bulk cargo belt conveyor and an unloader on the dry bulk cargo yard are started to obtain simulated information;
And carrying out data optimization on the simulation information to obtain a port material movement scheduling optimization simulation result.
Further, collecting road information of a port, material information of a dock and real-time information of dock equipment information, substituting the road information of the port, the material information of the dock and the real-time information of the dock equipment information into port material movement scheduling optimization simulation strategy logic, and generating real-time optimization port and dock material movement guiding information, wherein the method comprises the steps of;
The port material movement scheduling optimization simulation strategy logic comprises a path planning algorithm, a task scheduling algorithm, a queuing theory algorithm, a simulation optimization algorithm and a machine learning algorithm;
acquiring port material movement scheduling simulation demand information, and matching in port material movement scheduling optimization simulation strategy logic according to the port material movement scheduling simulation demand information to obtain an algorithm matching result;
Further, it also includes; the algorithm matching result is a path planning algorithm;
Substituting the acquired road information of the port, the material information of the wharf and the wharf equipment information into a preset path planning algorithm;
The path planning algorithm acquires a starting point, a target point and wharf map information from port material movement scheduling simulation demand information, wherein the wharf map information comprises movable area, obstacle, motor vehicle running area and moving cost information of carrying equipment;
Substituting the acquired starting point, target point and wharf map information into heuristic functions in a preset real-time port material movement scheduling simulation algorithm, initializing data to establish an open information list and a closed information list, substituting the starting point into the open information list, performing data detection circulation, and executing the following steps when the open list is not empty;
selecting a node with the minimum f value from the open information list, wherein f=g+h, 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;
the selected node is moved from the open list to the closed list.
And if the current node is the target node, returning a path from the starting node to the current node as an optimal path.
Otherwise, extending the neighbor node of the current node:
For each neighbor node, if it is not in the close list: calculating a cost from the starting node to the neighbor node; if the neighbor node is not in the open list or the cost of reaching the neighbor node through the current node is lower, adding the neighbor node into the open list, and setting a father node as the current node;
Starting from the target node, backtracking to the starting node through the father node, recording nodes on the path, returning the recorded path to serve as an optimal path, and outputting the optimal path of the wharf.
Further, it also includes; the algorithm matching result is a task scheduling algorithm;
Substituting the acquired road information of the port, the material information of the wharf and the wharf equipment information into a preset task scheduling algorithm;
The task scheduling algorithm acquires a task list and a handling equipment list from port material movement scheduling simulation demand information, wherein the starting position, the target position, cargo information and priority of the task list are acquired; the handling equipment list includes status (free/busy), location, handling capacity, handling cargo capacity, and time constraints of the equipment; obtaining a task scheduling plan, wherein the task scheduling plan comprises an execution sequence, distributed equipment and execution time of each task;
Initializing a task, and creating a task queue and an equipment state table according to the task list and the handling equipment list;
sequencing all tasks from high to low according to the priority, and putting the tasks into a task queue;
When the task queue is not empty, the following steps are performed: selecting a task with the highest priority from a task queue, searching available carrying equipment, traversing an equipment state table, finding equipment with an idle state, evaluating whether the equipment is suitable for executing the current task according to the current position and the capacity of the equipment, if the equipment is found, distributing the equipment to the current task, updating the equipment state to be busy, adding the current task into a scheduling plan, designating execution time and distributed equipment, removing the scheduled task from the task queue, and if the suitable equipment is not found, deciding whether to wait for the available equipment or give up the task according to the priority and time constraint of the task.
And introducing an optimization algorithm to optimize the task scheduling plan, such as reducing the moving distance of equipment, balancing the load of the equipment and the like.
The dependency relationship between tasks and the cooperative work are considered to ensure the maximization of the overall conveyance efficiency. And outputting the scheduling plan, and outputting the final scheduling plan, wherein the final scheduling plan comprises the execution sequence of each task, the allocated equipment and the execution time.
Further, the dry bulk cargo wharf logic mobile scheduling optimizing simulation strategy logic further comprises;
acquiring equipment types, quantity, dispatching time, working places and point-to-point operation instructions;
acquiring space position information of each structure of dock loading and unloading ship equipment and dock yard equipment in a remote control state, scanning information of ship types and stockpiles and the like;
Detecting whether the material finishes the quantitative movement of the point-to-point, wherein the quantitative movement comprises whether the material finishes the movement from one position to another position in the wharf;
the remote operator requests manual intervention when remote intervention is required, which is a request for manual intervention sent to the automatic control system.
Further, the dry bulk cargo wharf logic mobile scheduling optimizing simulation strategy logic further comprises;
acquiring distribution of operation equipment, planning and planning of equipment operation, and optimizing time consumption of operation;
receiving fault information of dock loading and unloading ship equipment, and autonomously judging through a state signal of a sensor;
determining space position information of each structure of dock loading and unloading ship equipment and ship type scanning information;
Judging whether the material is moved from the ship cargo hold, the dock ship loading and unloading equipment to the dock horizontal transportation equipment or the dock horizontal transportation equipment to the dock ship loading and unloading equipment and the ship cargo hold, whether the flow meets the planning requirement, and whether the operation at the joint of the equipment is effectively linked;
And generating operation state information according to the dock loading and unloading equipment.
Further, the dry bulk cargo wharf logic mobile scheduling optimizing simulation strategy logic further comprises;
When a certain device is in emergency stop or start due to failure or emergency, the related devices on the unified flow line are also correspondingly stopped or started.
In a second aspect, the invention provides a port material movement scheduling optimization simulation device, which comprises;
acquisition device: acquiring road information of a port, material information of a wharf and wharf equipment information;
The port material movement scheduling optimization simulation device constructs port material movement scheduling optimization simulation strategy logic according to port road information, dock material information and dock equipment information, collects port road information, dock material information and dock equipment information real-time information, substitutes port road information, dock material information and dock equipment information real-time information into the port material movement scheduling optimization simulation strategy logic, and generates real-time optimized port and dock material movement guiding information.
In a third aspect, the present invention provides an electronic device comprising;
A processor, a memory and a program or instruction stored on the memory and executable on the processor, which when executed by the processor, performs the steps of the method of any of the above steps.
In a fourth aspect, the present invention provides a readable medium having stored thereon a program or instructions which when executed by a processor performs the steps of the method of any of the above steps.
The beneficial effects of the invention are as follows: the invention provides a port material dispatching optimization simulation method, which is used for realizing port material movement dispatching optimization simulation design and rapidly simulating data structure layout according to external data input or according to actual wharf layout.
The technical scheme of the application is based on establishing a container terminal material moving flow mechanism, key nodes, key objects and relations thereof, as shown in figure 1. The system is supported to interact with key objects such as dock front loading and unloading equipment, dock horizontal transportation equipment, dock yard equipment, dock loading and unloading equipment and the like in real time, and real-time positioning and control instruction interaction of materials is realized. The wharf operating system, the equipment control system and the remote control system interact with equipment under each system in real time, implement instructions and feed back the state of the current materials in real time. And the information flows of the systems are interactively coupled, so that a remote real-time online material movement control function is realized. According to the technical scheme, simulation of the movement of the dry bulk cargo logistics is provided, parameters of a simulation scheme are adjusted before simulation starts, and data can be summarized and analyzed finally according to the designed parameter simulation scheme.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings that are needed in the embodiments will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a diagram of the decision logic of the dry bulk berth control system according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to specific embodiments of the present invention and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention. The following describes in detail the technical solutions provided by the embodiments of the present invention with reference to the accompanying drawings.
Dry bulk terminal logic: when the ship is driven into the dry bulk berth, the dry bulk controller is activated, and inquires of the existing conveyor belt system and the state of the dry bulk yard to start the dry bulk belt conveyor and the unloader on the dry bulk yard. At this time, the opening and closing of three different conveyor belts can also be controlled by dynamic control of the lower right corner. The dry bulk (coal) will be dynamically expressed by the amount of cargo unloaded. The coal pile of the coal yard is gradually increased from small to small until the storage amount in the dry bulk (coal) yard reaches the upper limit, and the conveyor belt system is automatically closed.
Data statistics logic: when unloading operations are performed, all machines and yards can generate data for background statistics. After statistics, when clicking the summary page, data updating is performed in time.
Simulation verification purpose;
On the basis of focusing on a material movement flow mechanism of a dry bulk cargo wharf and comprehensively grasping material movement interaction information of the dry bulk cargo wharf, a control logic structure required for achieving a remote real-time on-line material movement control function on the basis of guaranteeing safe and stable operation of the wharf is carded out from the level of a key object and the level of a key node, as shown in the figure. The remote on-line material movement control system for the dry bulk cargo wharf comprises an automatic control system, a management information system, a remote control system, a support and guarantee system and the like, integrates advanced technologies such as network architecture, system coupling, operation management, information physical system and the like, realizes real-time feedback of on-line material movement in multiple aspects such as production process, comprehensive management, maintenance and the like, and implements real-time material movement control through real-time network information interaction. The system is supported to interact with key objects such as dock front loading and unloading equipment, dock horizontal transportation equipment, dock yard equipment, dock loading and unloading equipment, dock operation auxiliary equipment and the like in real time, information fusion and instruction coordination are realized, on-line dynamic management is realized, and real-time positioning and control of materials are realized. The automatic control system, the management information system and the remote control system interact with equipment under each system in real time, implement instructions and feed back the state of the current materials in real time. And the information flows of the systems are interactively coupled, so that a remote real-time online material movement control function is realized.
In a first aspect, the invention provides a port material scheduling optimization simulation method, which comprises the following steps of;
acquiring road information of a port, material information of a wharf and wharf equipment information;
constructing a port dry bulk cargo wharf logic mobile scheduling optimization simulation strategy logic according to the road information of the port, the material information of the wharf and the wharf equipment information;
Acquiring road information of a port, material information of a dock and real-time information of dock equipment information, substituting the road information of the port, the material information of the dock and the real-time information of the dock equipment information into a port dry bulk cargo dock logic mobile scheduling optimization simulation strategy logic, and generating real-time optimization port dry bulk cargo dock mobile guiding information;
according to the real-time optimized port and dock dry bulk cargo dock movement guiding information, after a ship enters a dry bulk cargo berth, a dry bulk cargo controller is activated, the dry bulk cargo controller inquires the existing conveyor belt system and the state of a dry bulk cargo yard, and a dry bulk cargo belt conveyor and an unloader on the dry bulk cargo yard are started to obtain simulated information;
And carrying out data optimization on the simulation information to obtain a port material movement scheduling optimization simulation result.
In the embodiment of the application, the technical scheme firstly acquires the road information of the port, the material information of the wharf and the wharf equipment information, and constructs and optimizes the mobile scheduling strategy through an algorithm function. Finally, the situation of the ship after the ship is driven into the berth of the dry bulk cargo is simulated, and simulation information is obtained.
The following is a simple example code written in the Python language:
In this example we define PortInfo classes to store port information and have a collect _real_time_info method to simulate real-time acquisition of information. The DryBulkController class represents a dry bulk controller responsible for managing conveyor system and yard status, as well as initiating operations. The build_optimization_strategy function is used for constructing an optimization strategy, and the simulate _ship_arrival function simulates the operation of a ship after the ship is driven into a berth.
The technical scheme of the application relates to details such as a specific optimization algorithm, real-time data acquisition, equipment control logic and the like in the actual use process. In practical applications, these parts need to be designed and implemented according to specific situations. For example, optimization strategies may require consideration of multiple factors such as path planning, equipment usage efficiency, material handling costs, etc., while real-time data acquisition may involve integration with sensors, monitoring systems, etc.
Further, collecting road information of a port, material information of a dock and real-time information of dock equipment information, substituting the road information of the port, the material information of the dock and the real-time information of the dock equipment information into port material movement scheduling optimization simulation strategy logic, and generating real-time optimization port and dock material movement guiding information, wherein the method comprises the steps of;
The port material movement scheduling optimization simulation strategy logic comprises a path planning algorithm, a task scheduling algorithm, a queuing theory algorithm, a simulation optimization algorithm and a machine learning algorithm;
acquiring port material movement scheduling simulation demand information, and matching in port material movement scheduling optimization simulation strategy logic according to the port material movement scheduling simulation demand information to obtain an algorithm matching result;
Further, it also includes; the algorithm matching result is a path planning algorithm;
Substituting the acquired road information of the port, the material information of the wharf and the wharf equipment information into a preset path planning algorithm;
The path planning algorithm acquires a starting point, a target point and wharf map information from port material movement scheduling simulation demand information, wherein the wharf map information comprises movable area, obstacle, motor vehicle running area and moving cost information of carrying equipment;
Substituting the acquired starting point, target point and wharf map information into heuristic functions in a preset real-time port material movement scheduling simulation algorithm, initializing data to establish an open information list and a closed information list, substituting the starting point into the open information list, performing data detection circulation, and executing the following steps when the open list is not empty;
selecting a node with the minimum f value from the open information list, wherein f=g+h, 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;
the selected node is moved from the open list to the closed list.
And if the current node is the target node, returning a path from the starting node to the current node as an optimal path.
Otherwise, extending the neighbor node of the current node:
For each neighbor node, if it is not in the close list:
calculating a cost from the starting node to the neighbor node;
if the neighbor node is not in the open list or the cost of reaching the neighbor node through the current node is lower, adding the neighbor node into the open list, and setting a father node as the current node;
Starting from the target node, backtracking to the starting node through the father node, recording nodes on the path, returning the recorded path to serve as an optimal path, and outputting the optimal path of the wharf.
When the ship is driven into the dry bulk berth, the dry bulk controller is activated, and inquires of the existing conveyor belt system and the state of the dry bulk yard, and the dry bulk belt conveyor and the unloader on the dry bulk yard are started.
Further, it also includes; the algorithm matching result is a task scheduling algorithm;
Substituting the acquired road information of the port, the material information of the wharf and the wharf equipment information into a preset task scheduling algorithm;
The task scheduling algorithm acquires a task list and a handling equipment list from port material movement scheduling simulation demand information, wherein the starting position, the target position, cargo information and priority of the task list are acquired; the handling equipment list includes status (free/busy), location, handling capacity, handling cargo capacity, and time constraints of the equipment; obtaining a task scheduling plan, wherein the task scheduling plan comprises an execution sequence, distributed equipment and execution time of each task;
Initializing a task, and creating a task queue and an equipment state table according to the task list and the handling equipment list;
sequencing all tasks from high to low according to the priority, and putting the tasks into a task queue;
when the task queue is not empty, the following steps are performed: the task with the highest priority is selected from the task queue,
Searching available handling equipment, traversing an equipment state table, finding equipment with an idle state, evaluating whether the equipment is suitable for executing a current task according to the current position and the capacity of the equipment, if the equipment is suitable for being found, distributing the equipment to the current task, updating the equipment state to be busy, adding the current task to a scheduling plan, designating execution time and distributed equipment, removing the scheduled task from a task queue, and if the equipment is not suitable for being found, deciding whether to wait for the available equipment or give up the task according to the priority and time constraint of the task.
And introducing an optimization algorithm to optimize the task scheduling plan, such as reducing the moving distance of equipment, balancing the load of the equipment and the like.
The dependency relationship between tasks and the cooperative work are considered to ensure the maximization of the overall conveyance efficiency. And outputting the scheduling plan, and outputting the final scheduling plan, wherein the final scheduling plan comprises the execution sequence of each task, the allocated equipment and the execution time.
Further, the dry bulk cargo wharf logic mobile scheduling optimizing simulation strategy logic further comprises;
acquiring equipment types, quantity, dispatching time, working places and point-to-point operation instructions;
acquiring space position information of each structure of dock loading and unloading ship equipment and dock yard equipment in a remote control state, scanning information of ship types and stockpiles and the like;
Detecting whether the material finishes the quantitative movement of the point-to-point, wherein the quantitative movement comprises whether the material finishes the movement from one position to another position in the wharf;
the remote operator requests manual intervention when remote intervention is required, which is a request for manual intervention sent to the automatic control system.
Further, the dry bulk cargo wharf logic mobile scheduling optimizing simulation strategy logic further comprises;
acquiring distribution of operation equipment, planning and planning of equipment operation, and optimizing time consumption of operation;
receiving fault information of dock loading and unloading ship equipment, and autonomously judging through a state signal of a sensor;
determining space position information of each structure of dock loading and unloading ship equipment and ship type scanning information;
Judging whether the material is moved from the ship cargo hold, the dock ship loading and unloading equipment to the dock horizontal transportation equipment or the dock horizontal transportation equipment to the dock ship loading and unloading equipment and the ship cargo hold, whether the flow meets the planning requirement, and whether the operation at the joint of the equipment is effectively linked;
And generating operation state information according to the dock loading and unloading equipment.
Further, the dry bulk cargo wharf logic mobile scheduling optimizing simulation strategy logic further comprises;
When a certain device is in emergency stop or start due to failure or emergency, the related devices on the unified flow line are also correspondingly stopped or started.
In a second aspect, the invention provides a port material movement scheduling optimization simulation device, which comprises;
acquisition device: acquiring road information of a port, material information of a wharf and wharf equipment information;
The port material movement scheduling optimization simulation device constructs port material movement scheduling optimization simulation strategy logic according to port road information, dock material information and dock equipment information, collects port road information, dock material information and dock equipment information real-time information, substitutes port road information, dock material information and dock equipment information real-time information into the port material movement scheduling optimization simulation strategy logic, and generates real-time optimized port and dock material movement guiding information.
In a third aspect, the present invention provides an electronic device comprising;
A processor, a memory and a program or instruction stored on the memory and executable on the processor, which when executed by the processor, performs the steps of the method of any of the above steps.
In a fourth aspect, the present invention provides a readable medium having stored thereon a program or instructions which when executed by a processor performs the steps of the method of any of the above steps.
Specifically, in combination with the above control logic, the control mechanism of the synergy and cooperation between the critical nodes and the critical objects of the dry bulk wharf is shown in the table. A cooperative control mechanism between a key object and a key node of the surface dry bulk cargo wharf;
Simulating and verifying content;
Based on the cooperative control mechanism, the cooperative control simulation verification between the key object and the key node of the dry bulk cargo wharf mainly verifies the following aspects:
1. Inter-system interactions: this includes the delivery of job instructions, such as instructions for equipment type, quantity, dispatch time and job site, and feedback regarding the status of job instruction execution. This information ensures that the equipment is operating as planned and that the material is moving as required.
2. Interaction between system and device: information exchange between different types of job equipment, including dispatch, equipment planning, job status, fault information, etc., to ensure collaborative jobs between the equipment.
3. Interaction between devices: the interaction of the interlock control signals occurs between the devices to ensure that the associated devices are shut down or started accordingly in response to a fault or emergency to maintain safety and collaborative operation.
4. Remote control: an operator may remotely manipulate certain devices to suit particular needs or conditions while monitoring device status and feedback information.
The cooperative control mechanism inside the system ensures the efficient operation of the wharf, and enables different devices and objects to work in a coordinated manner to minimize congestion and improve overall efficiency.
Simulation verification procedure example
When the ship is not berthed into the berth, the berth state is a waiting or ready state, and the oil well pump machine corresponding to the berth is provided with an idle and not ready marker. The pump machine carrying the not ready flag does not enter the dry bulk conveyor dispensing block. When the ship starts to be berthed, berthing control starts the barge system, the barge pushes the ship into the berth, when the ship starts to be berthed, the berthing non-ready marker is switched to a barge berthing marker, and after the berthing waiting state is finished, the barge berthing marker is changed to a ship berthing completion and ready marker. At this time, the state of the oil pump is switched to the ship berthing ready state. The pump enters a conveyor belt distribution block.
The conveyor belt distribution program block judges the type of the dry bulk cargo carried by the ship, and activates the corresponding dry bulk cargo conveying conveyor belt according to the type of the dry bulk cargo. The storage yard storage selection judging mode is to check whether the current storage yard storage capacity is the maximum upper limit value, if so, the input is cut off, the storage yard storage state is switched to a full state and deleted from the inputtable list, at the moment, the state check is executed once, and the full or forbidden storage yard storage is closed and is communicated with the input for the subsequent storable storage yard storage.
According to the technical scheme, the port material movement scheduling optimization simulation design system is realized, the operation process of dry bulk cargo logistics is simulated, and the movement process of materials on ships, cranes, vehicles and wharf yards is simulated according to the real-time arrival condition of the ships. Through 3D live-action simulation, the function of the system for material movement scheduling is verified.
The simulation system performs a fast simulation of the data structure layout according to external data input or fast according to the actual dock layout. The simulation platform provides simulation of the logistics movement of four goods, namely a container, a piece of sundry goods, a dry bulk goods and a liquid bulk goods, parameters of a simulation scheme are adjusted before the simulation is started, and data can be finally summarized and analyzed according to the designed parameter simulation scheme.
It will be apparent to those skilled in the art that the techniques of embodiments of the present invention may be implemented by means of control software plus a necessary general purpose hardware platform. Based on such understanding, the technical solutions in the embodiments of the present invention may be embodied in essence or contributing to the prior art in the form of a software product, which may be stored in a storage medium, such as a vehicle-mounted computer, an internet cloud server, a hard disk, a ROM/RAM, a magnetic disk, an optical disk, etc., and include several instructions for causing a computer device (which may be a vehicle-mounted computer, a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments of the present invention. The embodiments of the present invention described above do not limit the scope of the present invention.
Claims (10)
1. The port material dispatching optimization simulation method is characterized by comprising the following steps of;
acquiring road information of a port, material information of a wharf and wharf equipment information;
constructing a port dry bulk cargo wharf logic mobile scheduling optimization simulation strategy logic according to the road information of the port, the material information of the wharf and the wharf equipment information;
Acquiring road information of a port, material information of a dock and real-time information of dock equipment information, substituting the road information of the port, the material information of the dock and the real-time information of the dock equipment information into a port dry bulk cargo dock logic mobile scheduling optimization simulation strategy logic, and generating real-time optimization port dry bulk cargo dock mobile guiding information;
according to the real-time optimized port and dock dry bulk cargo dock movement guiding information, after a ship enters a dry bulk cargo berth, a dry bulk cargo controller is activated, the dry bulk cargo controller inquires the existing conveyor belt system and the state of a dry bulk cargo yard, and a dry bulk cargo belt conveyor and an unloader on the dry bulk cargo yard are started to obtain simulated information;
And carrying out data optimization on the simulation information to obtain a port material movement scheduling optimization simulation result.
2. The port material scheduling optimization simulation method of claim 1, wherein the collecting of the road information of the port, the material information of the dock and the real-time information of the dock equipment, substituting the road information of the port, the material information of the dock and the real-time information of the dock equipment into the port material movement scheduling optimization simulation strategy logic, generating the real-time optimized port and dock material movement guiding information, comprises;
The port material movement scheduling optimization simulation strategy logic comprises a path planning algorithm, a task scheduling algorithm, a queuing theory algorithm, a simulation optimization algorithm and a machine learning algorithm;
And acquiring port material movement scheduling simulation demand information, and matching in port material movement scheduling optimization simulation strategy logic according to the port material movement scheduling simulation demand information to obtain an algorithm matching result.
3. The port material scheduling optimization simulation method as claimed in claim 2, further comprising;
the algorithm matching result is a path planning algorithm;
Substituting the acquired road information of the port, the material information of the wharf and the wharf equipment information into a preset path planning algorithm;
The path planning algorithm acquires a starting point, a target point and wharf map information from port material movement scheduling simulation demand information, wherein the wharf map information comprises movable area, obstacle, motor vehicle running area and moving cost information of carrying equipment;
Substituting the acquired starting point, target point and wharf map information into heuristic functions in a preset real-time port material movement scheduling simulation algorithm, initializing data to establish an open information list and a closed information list, substituting the starting point into the open information list, performing data detection circulation, and executing the following steps when the open list is not empty;
selecting a node with the minimum f value from the open information list, wherein f=g+h, 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;
moving the selected node from the open list to the closed list;
If the current node is the target node, returning a path from the starting node to the current node as an optimal path;
Otherwise, extending the neighbor node of the current node:
For each neighbor node, if it is not in the close list:
calculating a cost from the starting node to the neighbor node;
If the neighbor node is not in the open list, or the cost of reaching the neighbor node through the current node is lower,
Adding the neighbor node into an open list, and setting a father node as a current node;
Starting from the target node, backtracking to the starting node through the father node, recording nodes on the path, returning the recorded path to serve as an optimal path, and outputting the optimal path of the wharf.
4. The port material scheduling optimization simulation method as claimed in claim 2, further comprising;
The algorithm matching result is a task scheduling algorithm;
Substituting the acquired road information of the port, the material information of the wharf and the wharf equipment information into a preset task scheduling algorithm;
The task scheduling algorithm acquires a task list and a handling equipment list from port material movement scheduling simulation demand information, wherein the starting position, the target position, cargo information and priority of the task list are acquired; the handling equipment list includes status (free/busy), location, handling capacity, handling cargo capacity, and time constraints of the equipment; obtaining a task scheduling plan, wherein the task scheduling plan comprises an execution sequence, distributed equipment and execution time of each task;
Initializing a task, and creating a task queue and an equipment state table according to the task list and the handling equipment list;
sequencing all tasks from high to low according to the priority, and putting the tasks into a task queue;
When the task queue is not empty, the following steps are performed: selecting a task with the highest priority from a task queue, searching available carrying equipment, traversing an equipment state table, finding equipment with an idle state, evaluating whether the equipment is suitable for executing the current task according to the current position and the capacity of the equipment, if so, distributing the equipment to the current task, updating the equipment state to be busy, adding the current task into a scheduling plan, designating execution time and distributed equipment, removing the scheduled task from the task queue, and if not, deciding whether to wait for the available equipment or give up the task according to the priority and time constraint of the task;
And introducing an optimization algorithm to optimize the task scheduling plan, outputting the scheduling plan, and outputting a final scheduling plan, wherein the final scheduling plan comprises the execution sequence, the allocated equipment and the execution time of each task.
5. The port material scheduling optimization simulation method according to claim 1, wherein the dry bulk dock logic mobile scheduling optimization simulation strategy logic further comprises;
acquiring equipment types, quantity, dispatching time, working places and point-to-point operation instructions;
acquiring space position information of each structure of dock loading and unloading ship equipment and dock yard equipment in a remote control state, scanning information of ship types and stockpiles and the like;
Detecting whether the material finishes the quantitative movement of the point-to-point, wherein the quantitative movement comprises whether the material finishes the movement from one position to another position in the wharf;
the remote operator requests manual intervention when remote intervention is required, which is a request for manual intervention sent to the automatic control system.
6. The port material scheduling optimization simulation method according to claim 1, wherein the dry bulk dock logic mobile scheduling optimization simulation strategy logic further comprises;
acquiring distribution of operation equipment, planning and planning of equipment operation, and optimizing time consumption of operation;
receiving fault information of dock loading and unloading ship equipment, and autonomously judging through a state signal of a sensor;
determining space position information of each structure of dock loading and unloading ship equipment and ship type scanning information;
Judging whether the material is moved from the ship cargo hold, the dock ship loading and unloading equipment to the dock horizontal transportation equipment or the dock horizontal transportation equipment to the dock ship loading and unloading equipment and the ship cargo hold, whether the flow meets the planning requirement, and whether the operation at the joint of the equipment is effectively linked;
And generating operation state information according to the dock loading and unloading equipment.
7. The port material scheduling optimization simulation method according to claim 1, wherein the dry bulk dock logic mobile scheduling optimization simulation strategy logic further comprises;
When a certain device is in emergency stop or start due to failure or emergency, the related devices on the unified flow line are also correspondingly stopped or started.
8. The port material movement optimization simulation device is characterized by comprising the following components;
acquisition device: acquiring road information of a port, material information of a wharf and wharf equipment information;
The port material movement scheduling optimization simulation device constructs port material movement scheduling optimization simulation strategy logic according to port road information, dock material information and dock equipment information, collects port road information, dock material information and dock equipment information real-time information, substitutes port road information, dock material information and dock equipment information real-time information into the port material movement scheduling optimization simulation strategy logic, and generates real-time optimized port and dock material movement guiding information.
9. An electronic device, comprising;
a processor, a memory and a program or instruction stored on the memory and executable on the processor, which when executed by the processor, implements the steps of the method according to any one of claims 1-7.
10. A readable medium, characterized in that it has stored thereon a program or instructions which, when executed by a processor, implement the steps of the method according to any of claims 1-7.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118097454A (en) * | 2024-04-26 | 2024-05-28 | 尔特数据科技(江苏)有限公司 | Method and system for measuring congestion condition of container terminal of port by satellite image |
| CN118428563A (en) * | 2024-07-05 | 2024-08-02 | 山东港口日照港集团有限公司 | Intelligent planning method and system for storage yard |
| CN118469300A (en) * | 2024-07-09 | 2024-08-09 | 山东港源管道物流有限公司 | Liquid bulk cargo safety control system and device of digital twin platform |
| CN118521128A (en) * | 2024-07-22 | 2024-08-20 | 天津港股份有限公司 | Intelligent dispatching method and system for automatic field bridge of container terminal |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118097454A (en) * | 2024-04-26 | 2024-05-28 | 尔特数据科技(江苏)有限公司 | Method and system for measuring congestion condition of container terminal of port by satellite image |
| CN118428563A (en) * | 2024-07-05 | 2024-08-02 | 山东港口日照港集团有限公司 | Intelligent planning method and system for storage yard |
| CN118469300A (en) * | 2024-07-09 | 2024-08-09 | 山东港源管道物流有限公司 | Liquid bulk cargo safety control system and device of digital twin platform |
| CN118521128A (en) * | 2024-07-22 | 2024-08-20 | 天津港股份有限公司 | Intelligent dispatching method and system for automatic field bridge of container terminal |
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