A port scheduling simulation system and method considering automatic driving precision regulation

Abstract

The application relates to a port scheduling simulation system and method considering automatic driving precision regulation and control, and the method comprises the following steps: S1, setting a vehicle detector in the simulation system, the vehicle detector being used for detecting vehicles entering a work point in real time; S2, setting a vehicle queue of a quay crane / yard crane loading and unloading service, and simulating a first-come-first-served service mode; S3, starting simulation, recording vehicle states and work equipment states at the end of each simulation step; S4, setting or removing a vehicle path in the simulation system; S4, setting / removing a parking line in the simulation system, so that parking work simulation is realized; S5, taking the vehicle states and the work states as inputs, and performing single-step solving of a model; and S7, taking a scheduling algorithm to be evaluated as an input of the simulation system, and performing joint simulation with the simulation system. Compared with the prior art, the application can accurately simulate an automatic container port work scene, so that the scheduling algorithm can be evaluated and tested, work efficiency is improved, and operation cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of automated container port simulation technology, and in particular to a port area scheduling simulation system and method that considers precise control of autonomous driving. Background Technology

[0002] Automated container ports refer to the automation of container terminal operations through automated technologies and equipment. Traditionally, container port loading and unloading operations were typically performed manually, including by crane operators, stevedores, and supervisors. However, with continuous technological advancements, automated container ports are gradually emerging. Automated container ports utilize advanced technologies and equipment, such as automated stacker cranes, unmanned cranes, IoT sensors, and automated management systems, to automate the loading, unloading, stacking, storage, and management of containers. These technologies and equipment can complete various tasks through pre-set programs and instructions without human intervention. Currently, most ports across China have completed the construction of automated ports.

[0003] Automated guided trucks are a crucial component of automated horizontal transport in ports. They are driverless vehicles used for automated transport and handling tasks in ports and container terminals. Automated guided trucks typically employ automatic navigation technology and sensor systems, enabling them to navigate and operate autonomously along pre-planned routes. They can carry and move containers, cargo, container stackers, etc., completing loading, unloading, stacking, and transport tasks. The introduction of automated guided trucks improves the efficiency of port transport and handling, reduces labor costs and human error, and optimizes space utilization. They are widely used in modern ports and logistics.

[0004] To coordinate the transportation of autonomous trucks, automated ports have generally established automated scheduling systems to generate scheduling plans, including vehicle transportation task allocation and route planning. Due to the relatively closed nature of ports, on-site testing of scheduling plans is difficult, making simulation a crucial tool for evaluating them. However, existing port scheduling simulation platforms do not consider the characteristics of autonomous trucks and have the following significant shortcomings: 1. Existing port simulation systems are insufficient to meet the scheduling simulation requirements for real-time control of automated container trucks. Specifically, existing port simulation systems mostly model and simulate port operations from a macroscopic perspective, failing to accurately control the transportation tasks, routes, and real-time speeds of each vehicle. Therefore, existing port simulation systems are not suitable for automated container terminal operation scenarios.

[0005] 2. Existing port simulation systems primarily model and simulate human-driven container truck behavior. The vehicle dynamics models are inaccurate, and the vehicle dynamics parameters cannot be modified. Human driving behavior differs significantly from that of connected and automated container trucks. Therefore, using human driving dynamics models to simulate connected and automated vehicles will result in simulation results that deviate considerably from actual automated container port operations.

[0006] 3. Existing port simulation systems have fewer customization interfaces and do not provide secondary development interfaces such as C++ and Python. They cannot customize port layouts, such as the horizontal and vertical layouts of container areas, nor can they customize the operating times and modes of port equipment. Therefore, existing port simulation systems are difficult to expand or have complex development processes.

[0007] 4. Existing port simulation systems provide relatively limited evaluation results and cannot provide key indicators for automated container ports, such as execution accuracy, transportation safety, and vehicle route conflicts. Therefore, existing port simulation systems are insufficient to meet the evaluation indicator requirements for automated container ports. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a method that can accurately simulate automated container port operation scenarios, thereby evaluating and testing scheduling algorithms, achieving precise control of the transportation and speed of autonomous trucks, and helping ports improve operational efficiency and reduce operating costs.

[0009] The objective of this invention can be achieved through the following technical solutions: This invention addresses the scenario of autonomous truck transportation in automated container ports, simulating precise control of vehicle transportation tasks, trajectories, and speeds to meet the scheduling simulation requirements of automated container ports. This invention can simulate container loading and unloading and truck transportation operations in automated container ports, test different vehicle transportation scheduling schemes, and evaluate each scheme.

[0010] This invention provides a port area scheduling simulation method considering precise control of autonomous driving, comprising the following steps: S1: Set up a vehicle detector in the simulation system. The vehicle detector will detect vehicles entering the work point in real time. S2: Set up a vehicle queue for loading and unloading services of quay cranes or yard cranes in the simulation system to simulate a first-come, first-served operation mode and carry out loading and unloading services according to the order of vehicle arrival. S3: Start simulation. At the end of each simulation step, record the vehicle status and the status of the working equipment. S4: Based on the vehicle number and transportation route obtained in S3, set or clear the vehicle route in the simulation system. By setting the vehicle route, the transportation process of the vehicle between work points is simulated. S5: Based on the vehicle number and work stop point obtained in S3, set or clear the parking line in the simulation system to realize the parking operation simulation; S6: Using vehicle status and operation status as input, perform single-step model solution; S7: Use the scheduling algorithm to be evaluated as input to the simulation system and perform joint simulation with the simulation system.

[0011] Furthermore, in S1, the process involves real-time detection of the vehicle's position to determine whether it has entered the quay crane's operating lane or the internal road of the container area, including the following steps: S11: Traverse all vehicles; S12: Obtain the vehicle number and its lane number; S13: If the lane number is a quay crane operation lane or a container area lane, extract the corresponding vehicle number and the name of the quay crane or container area.

[0012] Furthermore, in S2, the implementation process involves first obtaining the current simulation time as the vehicle arrival time, and then setting up a separate queue for each quay crane or yard crane to store the operational vehicle information. This specifically includes the following steps: S21: Run the vehicle detector to obtain vehicles entering the quay crane or yard. If the vehicle is empty, store it as an empty value. S22: Iterate through the vehicles entering the quay crane or yard and add them to the corresponding crane's work queue; S23: If the current simulation time is greater than the crane's idle time, then obtain the information of the working vehicle according to the first-come, first-served rule.

[0013] Furthermore, in S3, vehicle status includes vehicle number, vehicle location coordinates, vehicle speed and acceleration, vehicle transport route, current task, and current stopping point; equipment status includes current task number and work schedule.

[0014] Furthermore, in S4, the implementation process is as follows: first, a static path selector is set in the simulation system, and then the vehicle with path selection function is set through the path selector to realize the dynamic change of vehicle path. A static path selector is an algorithm or rule used to determine the initial path of a vehicle. The initial path is either a pre-defined fixed path or a path generated according to rules. In the simulation system, select the vehicles that need to be routed, and mark them as vehicles that need to be routed based on their attributes or requirements. In the simulation system, the vehicle's status is monitored in real time, including position, speed, and acceleration information; the vehicle's path is dynamically changed according to the vehicle's status and the rules of the static path selector; when the vehicle meets the preset conditions, the static path selector recalculates the vehicle's path according to the preset rules and applies the new path to the vehicle. Furthermore, the implementation process of S5 involves first setting static parking sign lines in the simulation, and then selecting the vehicles to be affected by setting parking signs to achieve dynamic parking operations.

[0015] Furthermore, in S6, the implementation process involves first obtaining the vehicles and times of job completion, then determining the vehicles and containers to be scheduled, and finally solving for the scheduling scheme. The specific steps are as follows: S61: When a vehicle completes its task in the simulation system, obtain the vehicle ID and the current time; S62: Input the vehicle ID and the current time into the scheduling model to solve; S63: The scheduling model performs a rolling time-domain solution, waits for the current time-domain execution to complete, and then continues to solve the problem based on the actual situation. Rolling time-domain solution involves dynamically adjusting the vehicle scheduling scheme based on the current vehicle status and task conditions. After the current time-domain execution is completed, the scheduling scheme is solved again based on the actual situation.

[0016] Furthermore, in S7, the simulation execution process is as follows: S71: Initialize each module; S72: Solve the scheduling model to obtain the initial job allocation and vehicle route scheduling results; S73: Single-step simulation execution; S74: Determine in real time whether a vehicle has completed the transportation operation. If a vehicle has completed the operation, proceed to step S75; otherwise, repeat step S73. S75: Obtain the current status of the operating equipment and vehicles, solve the scheduling model, and re-execute step S73.

[0017] Furthermore, the vehicle detector is one or more of the following: magnetic sensor, photoelectric sensor, ultrasonic sensor, radar sensor, and visual sensor.

[0018] The present invention also provides a simulation system for implementing a port area scheduling simulation method that takes into account the precise control of autonomous driving, including a vehicle arrival at the work point perception module, a port area loading and unloading service module, a vehicle path control module, a vehicle work bay parking module, and a scheduling model solving module. The vehicle arrival sensing module is equipped with a vehicle detector to detect vehicles entering the work site in real time. The port loading and unloading service module is used to set up vehicle queues for quay crane or yard crane loading and unloading services, simulating a first-come, first-served operation mode. The vehicle routing control module is used to set or clear vehicle routes; The vehicle operation bay parking module is used to set or clear parking lines to simulate parking operations; The scheduling model solving module is used to establish and solve the main function of the scheduling model and simulation.

[0019] This invention provides a port area scheduling simulation system and method that considers precise control of autonomous driving. Addressing the unique characteristics of automated container port operations and the features of autonomous trucks, it develops a vehicle arrival point perception module, a port area loading and unloading service module, a vehicle path control module, a vehicle docking module, and a scheduling model solving module. Compared with existing port simulation systems, this system can achieve precise control of autonomous trucks and better reflects the actual operation scenarios of automated container ports.

[0020] This invention is applicable to various automated container port operation scenarios, such as container loading operations, container unloading operations, simultaneous loading and unloading operations of multiple ships, and mixed operations of 40-foot and 20-foot containers.

[0021] Compared with the prior art, the present invention has the following advantages: (1) It can achieve precise control of the transportation and speed of autonomous container trucks. Through the vehicle path control module and the vehicle operation bay docking module, the driving path, operation docking point location and real-time speed of each transport container truck can be precisely controlled to meet the operation requirements of automated container ports.

[0022] (2) The present invention has a more realistic vehicle dynamics model. All dynamic parameters are calibrated with actual operating vehicles. For different autonomous vehicles, the vehicle dynamics model inside the system can be changed by writing C++ code in the form of dynamic link library (dll).

[0023] (3) This invention provides a wealth of evaluation indicators for the operation of automated container ports, such as vehicle execution deviation, vehicle transportation efficiency, operating equipment utilization rate, and transportation cost. It also provides basic data, such as real-time vehicle location and real-time vehicle speed, and allows for the customization of evaluation indicators.

[0024] (4) This invention can be used for secondary development using common programming languages ​​such as C++, Python, and Matlab, and can be used to further develop existing functional modules, thus making it suitable for different operating modes of various ports. Detailed Implementation

[0025] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. Component models, material names, connection structures, control methods, algorithms, and other features not explicitly described in this technical solution are considered common technical features disclosed in the prior art.

[0026] Example 1 This embodiment provides a port area scheduling simulation method that considers precise control of autonomous driving, including the following steps: S1: Set up a vehicle detector in the simulation system. The vehicle detector will detect vehicles entering the work point in real time. S2: Set up vehicle queues for quay crane or yard crane loading and unloading services in the simulation system to simulate the first-come, first-served (FCFS) operation mode and carry out loading and unloading services according to the order of vehicle arrival; S3: Start simulation. At the end of each simulation step, record the vehicle status and the status of the working equipment. S4: Based on the vehicle number and transportation route obtained in S3, set or clear the vehicle route in the simulation system. By setting the vehicle route, the transportation process of the vehicle between work points is simulated. S5: Based on the vehicle number and work stop point obtained in S3, set or clear the parking line in the simulation system to realize the parking operation simulation; S6: Using vehicle status and operation status as input, perform single-step model solution; S7: Use the scheduling algorithm to be evaluated as input to the simulation system and perform joint simulation with the simulation system.

[0027] In a specific implementation, S1 involves real-time detection of the vehicle's position to determine whether it has entered the quay crane's operating lane or the internal road of the container area, including the following steps: S11: Traverse all vehicles; S12: Obtain the vehicle number and its lane number; S13: If the lane number is a quay crane operation lane or a container area lane, return the corresponding vehicle number and the name of the quay crane or container area.

[0028] In a specific implementation, S2 involves first obtaining the current simulation time as the vehicle arrival time, and then setting up a separate queue for each quay crane or yard crane to store the operating vehicle information. This includes the following steps: S21: Run the vehicle detector to obtain vehicles entering the quay crane or yard. If the vehicle list is empty, return an empty value. S22: Iterate through the vehicles entering the quay crane or yard and add them to the corresponding crane's work queue; S23: If the current simulation time is greater than the crane's idle time, then the work vehicle will be acquired according to the first-come, first-served rule.

[0029] In a specific implementation, S3, the vehicle status includes vehicle number, vehicle position coordinates, vehicle speed and acceleration, vehicle transport route, current work task, and current stopping point; the work equipment status includes current task number and work schedule.

[0030] In a specific implementation, in S4, the static path selector is an algorithm or rule used to determine the initial path of the vehicle. The initial path is a pre-set fixed path or a path generated according to the rules. In the simulation system, the vehicle that needs path selection is selected and identified as the vehicle that needs path selection based on its specific attributes or requirements. In the simulation system, the vehicle's status is monitored in real time, including position, speed, and acceleration information. The vehicle's path is dynamically changed according to the vehicle's status and the rules of the static path selector. When the vehicle meets the preset conditions, the static path selector recalculates the vehicle's path according to the preset rules and applies the new path to the vehicle.

[0031] In a specific implementation, in S5, the process involves first setting static parking sign lines in the simulation, and then selecting the vehicles to be parked by setting parking signs, thereby achieving dynamic parking operations for vehicles.

[0032] In S6, the implementation process involves first obtaining the vehicles and times when the job is completed, then determining the vehicles and containers to be scheduled, and finally solving the scheduling scheme. The specific steps are as follows: S61: During the simulation, the vehicle completes the task and obtains the vehicle ID and current time; S62: Input the vehicle ID and the current time into the scheduling model to solve; S63: The scheduling model performs a rolling time-domain solution, waits for the current time-domain execution to complete, and then continues to solve the solution based on the actual situation. The rolling time-domain solution refers to dynamically adjusting the vehicle scheduling scheme according to the current vehicle status and task situation, and then continuing to solve the scheduling scheme based on the actual situation after the current time-domain execution is completed.

[0033] In a specific implementation, the simulation execution process in S7 is as follows: S71: First, initialize each module; S72: Solve the scheduling model to obtain the initial job allocation and vehicle route scheduling results; S73: Single-step simulation execution; S74: Determine in real time whether a vehicle has completed the transportation operation. If a vehicle has completed the operation, proceed to step S75; otherwise, return to step S73. S75: Obtain the current status of the operating equipment and vehicles, solve the scheduling model, and return to step S73.

[0034] The vehicle detector is one or more of the following: magnetic sensor, photoelectric sensor, ultrasonic sensor, radar sensor, and vision sensor.

[0035] This embodiment also provides a simulation system for implementing a port area scheduling simulation method that considers precise control of autonomous driving, including a vehicle arrival at the work point perception module, a port area loading and unloading service module, a vehicle path control module, a vehicle work bay parking module, and a scheduling model solving module. The vehicle arrival sensing module is equipped with a vehicle detector to detect vehicles entering the work site in real time. The port loading and unloading service module is used to set up vehicle queues for quay crane or yard crane loading and unloading services, simulating a first-come, first-served operation mode. The vehicle routing control module is used to set or clear vehicle routes; The vehicle operation bay parking module is used to set or clear parking lines to simulate parking operations; The scheduling model solving module is used to establish and solve the main function of the scheduling model and simulation.

[0036] This embodiment provides a port area scheduling simulation system and method that considers precise control of autonomous driving. Addressing the unique characteristics of automated container port operations and the features of autonomous trucks, it develops a vehicle arrival point perception module, a port area loading and unloading service module, a vehicle path control module, a vehicle docking module, and a scheduling model solving module. Compared with existing port simulation systems, this system can achieve precise control of autonomous trucks and is more consistent with the actual operation scenarios of automated container ports.

[0037] This embodiment is applicable to various automated container port operation scenarios, such as container loading operations, container unloading operations, simultaneous loading and unloading operations of multiple ships, and mixed operations of 40-foot and 20-foot containers.

[0038] Components not described in detail in this embodiment are all existing components that can be purchased through public channels.

[0039] The above description of the embodiments is provided to enable those skilled in the art to understand and use the invention. It will be apparent to those skilled in the art that various modifications can be made to these embodiments, and the general principles described herein can be applied to other embodiments without inventive effort. Therefore, the present invention is not limited to the above embodiments, and any improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the invention should be within the protection scope of the present invention.

Claims

1. A port area scheduling simulation method considering precise control of autonomous driving, characterized in that, Includes the following steps: S1: A vehicle detector is set up in the simulation system, which detects vehicles entering the work site in real time; S2: Set up a vehicle queue for loading and unloading services of quay cranes or yard cranes in the simulation system to simulate a first-come, first-served operation mode and carry out loading and unloading services according to the order of vehicle arrival. S3: Start simulation. At the end of each simulation step, record the vehicle status and the status of the working equipment. S4: Based on the vehicle number and transportation route obtained in S3, set or clear the vehicle route in the simulation system. By setting the vehicle route, the transportation process of the vehicle between work points is simulated. The implementation process involves first setting a static path selector in the simulation system, and then using the path selector to set the vehicles that perform path selection, thereby realizing the dynamic change of vehicle paths. The static path selector is an algorithm or rule used to determine the initial path of a vehicle, wherein the initial path is a pre-set fixed path or a path generated according to a rule. In the simulation system, select the vehicles that need to be routed, and mark them as vehicles that need to be routed based on their attributes or requirements. In the simulation system, the vehicle's status is monitored in real time, including position, speed, and acceleration information; the vehicle's path is dynamically changed according to the vehicle's status and the rules of the static path selector; when the vehicle meets the preset conditions, the static path selector recalculates the vehicle's path according to the preset rules and applies the new path to the vehicle. S5: Based on the vehicle number and work stop point obtained in S3, set or clear the parking line in the simulation system to realize the parking operation simulation; S6: Using vehicle status and operation status as input, perform single-step model solution; S7: Use the scheduling algorithm to be evaluated as input to the simulation system and perform joint simulation with the simulation system.

2. The port area scheduling simulation method considering precise control of autonomous driving according to claim 1, characterized in that, In S1, the process involves real-time detection of the vehicle's position to determine whether it has entered the quay crane's operating lane or the internal road of the container area, and includes the following steps: S11: Traverse all vehicles; S12: Obtain the vehicle number and its lane number; S13: If the lane number is a quay crane operation lane or a container area lane, extract the corresponding vehicle number and the name of the quay crane or container area.

3. The port area scheduling simulation method considering precise control of autonomous driving according to claim 1, characterized in that, In S2, the implementation process involves first obtaining the current simulation time as the vehicle arrival time, and then setting up a separate queue for each quay crane or yard crane to store the operational vehicle information. This includes the following steps: S21: Run the vehicle detector to obtain vehicles entering the quay crane or yard. If the vehicle is empty, store it as an empty value. S22: Iterate through the vehicles entering the quay crane or yard and add them to the corresponding crane's work queue; S23: If the current simulation time is greater than the crane's idle time, then obtain the information of the working vehicle according to the first-come, first-served rule.

4. The port area scheduling simulation method considering precise control of autonomous driving according to claim 1, characterized in that, In S3, vehicle status includes vehicle number, vehicle position coordinates, vehicle speed and acceleration, vehicle transport route, current task, and current stopping point; equipment status includes current task number and work schedule.

5. A port area scheduling simulation method considering precise control of autonomous driving according to claim 1, characterized in that, The implementation process of S5 involves first setting static parking sign lines in the simulation, and then selecting the vehicles to be affected by setting parking signs to achieve dynamic parking operations.

6. The port area scheduling simulation method considering precise control of autonomous driving according to claim 1, characterized in that, In S6, the implementation process involves first obtaining the vehicles and times when the job is completed, then determining the vehicles and containers to be scheduled, and finally solving the scheduling scheme. The specific steps are as follows: S61: When a vehicle completes its task in the simulation system, obtain the vehicle ID and the current time; S62: Input the vehicle ID and the current time into the scheduling model to solve; S63: The scheduling model performs a rolling time-domain solution, waits for the current time-domain execution to complete, and then continues to solve the problem based on the actual situation. The rolling time-domain solution involves dynamically adjusting the vehicle scheduling scheme based on the current vehicle status and task conditions. After the current time-domain execution is completed, the scheduling scheme solution is continued based on the actual situation.

7. A port area scheduling simulation method considering precise control of autonomous driving according to claim 1, characterized in that, In S7, the simulation execution process is as follows: S71: Initialize each module; S72: Solve the scheduling model to obtain the initial job allocation and vehicle route scheduling results; S73: Single-step simulation execution; S74: Determine in real time whether a vehicle has completed the transportation operation. If a vehicle has completed the operation, proceed to step S75; otherwise, repeat step S73. S75: Obtain the current status of the operating equipment and vehicles, solve the scheduling model, and re-execute step S73.

8. A port area scheduling simulation method considering precise control of autonomous driving according to claim 1, characterized in that, The vehicle detector is one or more of the following: magnetic sensor, photoelectric sensor, ultrasonic sensor, radar sensor, and vision sensor.

9. A simulation system for implementing a port area scheduling simulation method considering precise control of autonomous driving as described in any one of claims 1-8, characterized in that, It includes a vehicle arrival at the work point perception module, a port area loading and unloading service module, a vehicle route control module, a vehicle work bay parking module, and a scheduling model solving module; The vehicle arrival at the work site sensing module includes a vehicle detector, which is used to detect vehicles entering the work site in real time. The port loading and unloading service module is used to set up vehicle queues for quay crane or yard crane loading and unloading services, simulating a first-come, first-served operation mode. The vehicle route control module is used to set or clear vehicle routes; The vehicle operation bay parking module is used to set or clear parking lines to simulate parking operations. The scheduling model solving module is used to establish and solve the main function of the scheduling model and simulation.