An AGV-based intelligent multi-level parking lot scheduling simulation method and device

Abstract

The application discloses a kind of based on AGV's intelligent three-dimensional parking lot scheduling simulation method and device, according to the layout of parking lot, the environment map of parking lot is constructed, and the environment map includes the key node in the environment of parking lot and the edge between node;According to the site condition or the preset site condition of parking lot, the model of each site in parking lot is constructed, a certain number of initial state virtual AGV is generated when simulation starts;The instruction issued by the scheduling system is analyzed as the instruction model in simulation;Through the way of time slice advancement, the motion when AGV executes instruction and the operation of AGV on site, and the state change of site are simulated.The application can complete the test and verification of the scheduling function of scheduling system with shorter time cost and manpower cost;Software system function can be tested in advance before real hardware equipment installation and layout are completed;It can effectively help the development of scheduling system, improve the efficiency of development and evolution of scheduling system.

Description

Technical Field

[0001] This invention belongs to the field of computer simulation technology, and in particular relates to a simulation method and device for intelligent three-dimensional parking lot scheduling based on AGV. Background Technology

[0002] With rapid socio-economic development, the number of private cars in cities has continued to grow, leading to an increasingly prominent contradiction between the supply and demand of parking spaces. Meanwhile, urban land resources are scarce, making it difficult to meet the ever-increasing parking demands. The shortage of parking spaces and the difficulty of parking in urban areas have become common problems that urgently need to be solved in the development process of large and medium-sized cities. Against this backdrop, applying AGVs (Automated Guided Vehicles) to parking scenarios and combining them with multi-level parking garages to create AGV intelligent multi-level parking systems has become a feasible solution to the parking problem. AGV technology has been widely applied in various industries such as manufacturing and logistics. As a robot capable of moving in multiple directions and possessing transportation functions, AGVs are naturally suitable for use in parking scenarios. Parking AGVs applied in automated parking scenarios use specially designed transport mechanisms to lift customer vehicles and transport them to their destination, thus completing the parking task.

[0003] In AGV intelligent automated parking systems, the scheduling system is responsible for managing the scheduling of AGVs throughout the parking lot, including task allocation, path planning, and traffic management. The scheduling system is the brain that controls all AGVs in the parking lot to achieve automatic vehicle storage and retrieval, and it is an indispensable core component for realizing an efficient and safe intelligent parking system. However, in reality, testing AGV scheduling systems is costly, time-consuming, and often cannot meet the experimental and testing needs of developing a new system. The development and testing of scheduling systems are often carried out simultaneously with the construction of the actual site and the production and installation of hardware equipment. When the new site is not yet completed or the hardware equipment is not fully installed, the developers of the scheduling system still need an environment where the system can operate. When conducting experiments or tests in a real-world environment, the scheduling system may still have some undiscovered problems, and the experiment may potentially cause property damage or safety accidents. Furthermore, field testing also suffers from low testing efficiency and high labor costs. In addition, with more and more companies advocating remote work, simulation systems allow developers to debug the system online, replacing traditional real-machine testing. Summary of the Invention

[0004] This invention provides an intelligent automated parking system scheduling simulation method and device based on AGVs to save time and manpower costs in the development and testing of scheduling systems and improve the efficiency of scheduling system development and testing.

[0005] The objective of this invention is achieved through the following technical solution: a simulation method for scheduling intelligent automated parking lots based on AGVs, comprising the following steps:

[0006] S1: Based on the layout of the parking lot, construct a parking lot environment map, which includes key nodes in the parking lot environment and the edges connecting the nodes.

[0007] S2: Based on the status of stations in the parking lot, construct a model of each station in the parking lot. The station can interact with the AGV in the parking lot. The stations in the parking lot environment specifically include parking spaces, handover areas, vehicle carrier racks, and charging stations. The station model includes the current status of each station and the specific node in the environment map corresponding to the station in step S1.

[0008] S3: Generate a certain number of initial virtual AGVs based on the initial state information of the AGVs in the parking lot and the number of parking spaces on the map;

[0009] S4: In the parking AGV scheduling simulation, the task to be performed by the virtual AGV is issued by the external control system. The scheduling simulation converts the control commands issued by the external control system into the standard instruction model in the simulation, including the starting point stationary movement action instruction, the next movement instruction after completing the previous stationary movement, and the operation instruction to reach the destination position. The instruction queue composed of a series of instructions completely describes the task to be performed by the virtual AGV.

[0010] S5: Simulate AGV operation using time-slice progression; after the simulation begins, the simulator will simulate the AGV's movement when executing commands, the AGV's operation at the station, and the station's state changes. Each simulation time slice progression includes the following steps:

[0011] S51: Advancing Simulation Time: The simulation scheduling includes a simulation timer, which is responsible for recording the number of frames advanced by the simulation and the duration of the simulated reality. Each time the simulation time slice advances, the simulation timer is advanced by one simulation step.

[0012] S52: Calculate and update the state of the virtual AGV in the simulation: During each simulation time slice, the state of the virtual AGV in the simulation will be updated; the AGV state update here specifically includes the current running state, moving speed and rotation speed of the AGV;

[0013] S53: Execute the movement of the virtual AGV: The movement is executed based on the AGV's current speed. Specifically, the displacement Δd of the AGV within this simulation step is calculated based on the current speed and the simulation step size, and then added to the cumulative displacement of the previous simulation step size to obtain the cumulative displacement d of the moving object relative to the starting point of the edge on the current edge. t If the cumulative displacement d at this time t If the length of this edge has been exceeded, it means the AGV will move to a new edge. Therefore, update the edge containing the AGV and its cumulative displacement d relative to the starting point of the edge. t ;

[0014] S54: Execute the rotation of the virtual AGV: When the AGV needs to execute the rotation command in the instruction, the target direction angle that the AGV needs to reach by rotating will be calculated first based on the current direction angle of the AGV and the rotation command, and the next rotation direction of the AGV will be determined; the angular velocity of the AGV will be calculated during rotation, and the current direction angle of the AGV will be updated based on the current angular velocity in each time slice advancement until the target direction angle is reached;

[0015] S55: Execute virtual AGV interaction with the station: After the AGV completes its movement and arrives at the station, it will execute the interaction action with the station according to the operation in the instruction. The simulation will use an action timer to simulate the time required for the AGV to execute the interaction action; after the AGV completes the interaction with the station, it will update the AGV's carrying status and the status of the corresponding station.

[0016] Furthermore, the key nodes in the parking lot environment map correspond to the positioning points laid out in the parking lot for AGVs to use for positioning. The node information includes the node ID, the node's location coordinates, the node type, the node's set of adjacent nodes, the node's outgoing edge set, and the node's incoming edge set. The edges between nodes correspond to the paths between positioning points in the parking lot, describing the connectivity between nodes, including the edge ID, the edge's starting point, the edge's ending point, the edge's length, the edge's disallowability, the AGV's passable width limit on the edge, and the AGV's passable height limit on the edge.

[0017] Furthermore, the initial state information of the AGV includes the ID, position, and direction of the virtual AGV, as well as the parameters configured for the AGV, including vehicle length, vehicle width, and maximum vehicle speed.

[0018] Furthermore, the motion state of the virtual AGV in the current frame is calculated based on its current motion target and the AGV's state in the previous frame; the motion target is determined by each dequeue instruction in the instruction queue.

[0019] Furthermore, the AGV scheduling simulation includes a simulator state change monitoring mechanism. When the simulator state changes, by registering the corresponding simulator state change event listener, a predetermined task can be executed when the simulator state changes. The simulator's lifecycle includes the following states: simulation uninitialized state, simulation initialized state, and simulation in progress state. The simulator has the following state change events: simulation initialization event, simulation start event, simulation advance event, and simulation end event. The simulation initialization event is triggered when the simulator changes from the uninitialized state to the initialized state. The simulation start event is triggered when the simulator changes from the initialized state to the in progress state. The simulation advance event is triggered when the simulator advances one frame each time it is in the in progress state. The simulation end event is triggered when the simulator changes from the in progress state to the uninitialized state. The above four events correspond to the simulation initialization event listener, simulation start event listener, simulation advance event listener, and simulation end event listener, respectively.

[0020] Furthermore, the AGV scheduling simulation includes a simulation process playback module. Each time the simulator advances one simulation step, it traverses all AGVs in the simulation, recording the current state, current position, and current orientation angle information of each AGV sequentially in a memory data container. At the end of the simulation, the simulation process record stored in the data container is persisted, and this data is used as the simulation process record. Then, using the simulation process record, the movement process of the AGVs is reproduced. That is, at each simulation step, the corresponding state of each AGV in the current frame is obtained from the simulation record, and the corresponding moving objects are created or updated, thus completing the playback of the completed simulation process.

[0021] Furthermore, the AGV scheduling simulation includes a visualization module; the visualization module uses a web client to graphically display the specific environment of the parking lot and the current position, direction, and current carrying status of each virtual AGV in the parking lot, showing the real-time operation status of the parking lot.

[0022] Furthermore, the virtual AGVs in the AGV scheduling simulation have virtual communicators. Each virtual AGV in the simulation is equipped with a virtual communicator, which simulates the status update reporting of real AGVs. During the movement and operation of the virtual AGVs in the simulation, the virtual AGVs send their latest status and reported task progress updates to the upper-level scheduling system through the virtual communicator. Only then can the scheduling system perform the next scheduling step based on the task execution progress update information of the AGVs.

[0023] Secondly, the present invention also provides an intelligent automated parking system scheduling simulation device based on AGV, including a memory and one or more processors. The memory stores executable code, and when the processor executes the executable code, it implements the aforementioned intelligent automated parking system scheduling simulation method based on AGV.

[0024] Thirdly, the present invention also provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the aforementioned intelligent three-dimensional parking lot scheduling simulation method based on AGV.

[0025] In summary, this invention provides a simulation method for intelligent automated parking system scheduling based on AGVs in real-world parking scenarios, achieving simulation of the operating conditions of various devices in a real-world work environment. The simulation system can receive instruction strings from the upstream scheduling system and simulate the specific operating states of the AGVs when executing these instructions, providing an efficient experimental and testing environment for the automated parking AGV scheduling system. Furthermore, it allows for convenient collection and statistical analysis of the calculation results during the simulation process.

[0026] This invention primarily addresses the problem of testing and verifying the functions of an AGV-based intelligent automated parking system. By implementing this invention, the testing and verification of the scheduling functions of the system can be completed with relatively short time and manpower costs. The functions of the software system can be tested before the actual hardware equipment is installed and deployed. This invention can effectively assist in the development of the scheduling system and improve the efficiency of its development and evolution. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a flowchart of a simulation method for scheduling intelligent automated parking lots based on AGVs.

[0029] Figure 2 This is a diagram showing the relationship between the simulator's states, state transitions, and the corresponding simulator state change event listeners.

[0030] Figure 3 This diagram illustrates how to use a simulator to advance the recording of the simulation process by using an event listener.

[0031] Figure 4To display the real-time operation status of the parking lot AGV simulation on the web client during simulation.

[0032] Figure 5 This is a schematic diagram of the structure of an intelligent three-dimensional parking lot scheduling simulation device based on AGV according to the present invention. Detailed Implementation

[0033] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments, but the present invention is not limited to the examples given.

[0034] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0035] This invention provides a simulation method for scheduling intelligent automated parking lots based on AGVs, such as... Figure 1 As shown, it includes the following steps:

[0036] (1) Based on the on-site layout of the parking lot (CAD drawings or other forms of construction drawings) or based on the preset parking lot layout, construct a parking lot environment map. The environment map includes key nodes in the parking lot environment and the edges connecting the nodes. The positions of the nodes and their relative distances are consistent with those in the real environment or the preset environment. The nodes in the parking lot environment map correspond to the positioning points laid in the parking lot for AGVs to use for positioning. The node information includes the node ID, the node's position coordinates, the node type, the node's adjacent point set, the node's outgoing edge set, and the node's incoming edge set. The edges between nodes correspond to the paths between positioning points in the parking lot, describing the connectivity between nodes, including the edge ID, the edge's starting point, the edge's ending point, the edge's length, the edge's disallowability (AGVs cannot pass through disabled edges), the AGV's passable width limit on the edge, and the AGV's passable height limit on the edge.

[0037] (2) Based on the actual conditions of the stations in the parking lot or based on the preset conditions of the stations in the parking lot, construct a model of each station in the parking lot. The station is a special location in the parking lot that the AGV can interact with. In the parking lot environment, it specifically includes parking spaces, handover areas, vehicle carrier racks, and charging positions. The parking space is the parking space where vehicles are parked in the parking lot, which can be a flat or three-dimensional parking space; the handover area refers to the entrance and exit of the parking lot, the location where the parking AGV obtains or delivers vehicles; the vehicle carrier rack is the location in the parking lot where vehicle carrier racks are placed; the charging position is the location in the parking lot where the AGV can park and charge. The station model includes the current state of each station and the specific node in the environment map corresponding to the station in step (1). One station corresponds to multiple specific nodes in the map. The area covered by the station is slightly larger than the AGV. The node corresponding to the station refers to all nodes in the area covered by the station.

[0038] (3) Obtain the initial state information of AGVs in the parking lot by reading files or calling network interfaces. Generate a certain number of initial state virtual AGVs based on the initial state information of AGVs and the number of parking spaces on the map. The initial state of AGVs includes the ID, position, and direction of the virtual AGVs. At the same time, configure the parameters of AGVs, including vehicle length, vehicle width, and maximum vehicle speed. During initialization, each virtual AGV in the simulation will be equipped with a virtual communicator. The function of the virtual communicator is to simulate the status update reporting of AGVs in reality. During the movement and operation of virtual AGVs in the simulation, virtual AGVs send their latest status and reported task progress updates to the upper-level scheduling system through the virtual communicator. Only then can the scheduling system perform the next scheduling step based on the task execution progress update information of the AGVs.

[0039] (4) In the parking AGV scheduling simulation, the tasks to be performed by the virtual AGV are issued by an external scheduling system or other external control system. The scheduling simulation system converts the control commands issued by the external control system into a standard instruction model in the simulation, including the starting point stationary movement command, the next movement command after completing the previous stationary movement, and the operation command to reach the destination position. The task to be performed by the virtual AGV is completely described by a series of instruction queues. For different external control commands, different instruction parsing modules can be customized and developed to unify them into standard commands.

[0040] Because external control systems often issue control commands in various forms, the specific format of the instruction string may also change. Therefore, to enable the virtual AGV in the AGV scheduling simulation system to receive and execute various forms of control commands, a standard instruction model is defined for the AGV scheduling simulation system. The tasks to be performed by the AGV are described at a smaller granularity. Each task is defined as consisting of several instructions, each consisting of three parts in sequence: MOTION_ON_SOURCE, STEP, and OPERATION. MOTION_ON_SOURCE describes the in-place movement that the AGV needs to perform at the current starting point. This part is empty when no in-place movement is required. STEP describes the AGV's next movement trajectory after completing the previous in-place movement (or no movement), including the starting point, the ending point, and the directed edge between the two points. The starting point and the ending point in each STEP are adjacent nodes on the map. This part is empty when movement between nodes is required. The OPERATION section describes the operation that the AGV will perform after completing the movement represented by the previous STEP, and the related parameters of the operation. This section is empty when no operation is required. For an instruction, one or two of its components MOTION_ON_SOURCE, STEP, and OPERATION can be empty, but at least one of them must be empty; otherwise, the instruction is meaningless.

[0041] (5) Simulate AGV operation using time-slice progression. After the simulation begins, the simulator will simulate the AGV's movement when executing commands, the AGV's operation at the station, and the station's state changes. Each simulation time slice progression includes the following steps:

[0042] (5.1) Advancing simulation time: The simulation scheduling includes a simulation timer, which is responsible for recording the number of frames advanced by the simulation and the duration of the simulated reality. Each time the simulation time slice advances, the simulation timer is advanced by one simulation step.

[0043] (5.2) Calculate and update the state of the virtual AGV in the simulation: The state of the virtual AGV in the simulation is updated during each simulation time slice. This update specifically includes the AGV's current running state, moving speed, and rotational speed. The motion state of the virtual AGV in the current frame is calculated based on its current motion target and the AGV's state in the previous frame. The motion target is determined by each dequeue instruction in the instruction queue. The AGV's speed in the simulation can be calculated separately for three scenarios: acceleration, constant speed, and deceleration / braking. When the AGV is not within the deceleration and braking range and its speed has not reached its maximum value, the AGV is in the acceleration phase. The current acceleration is set to a preset AGV acceleration value, and the AGV's current speed is increased frame by frame based on the current simulation step size and acceleration value. After the AGV reaches its maximum speed, it will move at a constant speed. When the AGV reaches a preset braking distance range before the stopping point, the AGV's deceleration is calculated based on the remaining distance from the stopping point, and the AGV's current speed is decreased frame by frame based on the simulation step size and deceleration value until it stops at the stopping point. In some cases, when the AGV starts from its current position, its distance to the stopping point is already within the braking distance range. In this case, it can be approximated that the AGV will move slowly and uniformly to the stopping point at a lower speed.

[0044] (5.3) Execute the movement of the virtual AGV: The movement is executed according to the current speed of the AGV, that is, the displacement Δd of the AGV in this simulation step is calculated based on the current speed and the simulation step size, and then it is added to the cumulative displacement of the previous simulation step size to obtain the cumulative displacement d of the moving object on the current edge relative to the starting point of the edge. t If the cumulative displacement d at this time t If the length of this edge has been exceeded, it means the AGV will move to a new edge. Therefore, update the edge containing the AGV and its cumulative displacement d relative to the starting point of the edge. t .

[0045] (5.4) Executing the rotation of the virtual AGV: When the AGV needs to execute the rotation command in the instruction, the target direction angle to be reached by the AGV through rotation will be calculated first based on the current direction angle of the AGV and the rotation command, and the subsequent rotation direction of the AGV will be determined, whether to rotate left or right. The process of executing the rotation is similar to the process of movement, the difference being that the angular velocity of the AGV is calculated during rotation, and the current direction angle of the AGV is updated according to the current angular velocity in each time slice advancement, until the target direction angle is reached.

[0046] (5.5) Executing Virtual AGV Interaction Actions with Stations: After the AGV completes its movement and arrives at the station, it will execute interaction actions with the station according to the instructions, such as storing the vehicle pallet to a specific shelf in the parking space. The simulation will use an action timer to simulate the time required for the AGV to execute the interaction actions. After the AGV completes its interaction with the station, it will update the AGV's carrying status and the status of the corresponding station.

[0047] The parking AGV scheduling simulation includes a simulator state change monitoring mechanism. When the simulator state changes, by registering a corresponding simulator state change event listener, a predetermined task can be executed when the simulator state changes. For example... Figure 2 As shown, the simulation defines the following simulation state change events and corresponding event listeners: the simulation initialization event is triggered when the simulator changes from an uninitialized state to an initialized state, and there is a corresponding simulation initialization event listener; the simulation start event is triggered when the simulator changes from an initialized state to a running state, and there is a corresponding simulation start event listener; the simulation advance event is triggered when the simulator advances one frame each time it is in the running state, and there is a corresponding simulation advance event listener; the simulation end event is triggered when the simulator changes from a running state to an uninitialized state, and there is a corresponding simulation end event listener.

[0048] In specific implementation, such as Figure 3 As shown, the AGV scheduling simulation includes a simulation process playback module. Through a simulation advancement event listener, each time the simulator advances one simulation step, it iterates through all the AGVs in the simulation, recording the current state, current position, and current direction angle of each AGV sequentially in a memory data container. At the end of the simulation, the simulation process record stored in the data container is persisted, becoming the simulation process record. Then, in the simulation playback module, the motion process of the AGVs is reproduced using the simulation process record. That is, at each simulation step, the corresponding state of each AGV in the current frame is obtained from the simulation record, and the corresponding moving objects are created or updated, thus completing the playback of the completed simulation process.

[0049] In specific implementation, such as Figure 4As shown, the AGV scheduling simulation includes a visualization module. The simulation can graphically display the specific environment of the parking lot and the current position, direction, and carrying status of each virtual AGV within the parking lot through a web client, providing real-time updates on the parking lot's operation. Specifically, during the simulation, the simulation backend actively pushes the latest position, direction, and status of the AGVs to the frontend using WebSocket. After an AGV interacts with a station, it actively pushes the latest status of that station. Furthermore, in practice, the simulation backend needs to push the elapsed time of the current simulation to the frontend as the simulation progresses.

[0050] Corresponding to the aforementioned embodiment of an AGV-based intelligent automated parking system scheduling simulation method, the present invention also provides an embodiment of an AGV-based intelligent automated parking system scheduling simulation device.

[0051] See Figure 5 The present invention provides an intelligent automated parking system scheduling simulation device based on AGV, comprising a memory and one or more processors. The memory stores executable code, and when the processor executes the executable code, it is used to implement an intelligent automated parking system scheduling simulation method based on AGV as described in the above embodiment.

[0052] The embodiment of the intelligent automated parking system scheduling simulation device based on AGV provided by this invention can be applied to any device with data processing capabilities, such as a computer. The device embodiment can be implemented through software, hardware, or a combination of both. Taking software implementation as an example, as a logical device, it is formed by the processor of any data processing device loading the corresponding computer program instructions from non-volatile memory into memory for execution. From a hardware perspective, such as... Figure 5 The diagram shown is a hardware structure diagram of any data processing-capable device, including the AGV-based intelligent automated parking system scheduling simulation device provided by this invention. (Except for...) Figure 5 In addition to the processor, memory, network interface, and non-volatile memory shown, any data processing device in the embodiment may also include other hardware depending on the actual function of the data processing device, which will not be described in detail here.

[0053] The specific implementation process of the functions and roles of each unit in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0054] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of the present invention according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0055] This invention also provides a computer-readable storage medium storing a program thereon, which, when executed by a processor, implements a simulation method for scheduling an intelligent automated parking system based on AGVs as described in the above embodiments.

[0056] The computer-readable storage medium can be an internal storage unit of any data processing device as described in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium can also be an external storage device of any data processing device, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc., equipped on the device. Furthermore, the computer-readable storage medium can include both internal storage units and external storage devices of any data processing device. The computer-readable storage medium is used to store the computer program and other programs and data required by the data processing device, and can also be used to temporarily store data that has been output or will be output.

[0057] The above embodiments are used to explain and illustrate the present invention, but not to limit the present invention. Any modifications and changes made to the present invention within the spirit and scope of the claims shall fall within the protection scope of the present invention.

Claims

1. A simulation method for scheduling intelligent automated parking lots based on AGVs, characterized in that, Includes the following steps: S1: Based on the layout of the parking lot, construct a parking lot environment map, which includes key nodes in the parking lot environment and the edges connecting the nodes. S2: Based on the status of stations in the parking lot, construct a model of each station in the parking lot. The station can interact with the AGV in the parking lot. The stations in the parking lot environment specifically include parking spaces, handover areas, vehicle carrier racks, and charging stations. The station model includes the current status of each station and the specific node in the environment map corresponding to the station in step S1. S3: Generate a certain number of virtual AGVs in their initial state based on the initial state information of the AGVs in the parking lot and the number of parking spaces on the map; S4: In the parking AGV scheduling simulation, the task to be performed by the virtual AGV is issued by the external control system. The scheduling simulation converts the control commands issued by the external control system into the standard instruction model in the simulation, including the starting point stationary movement action instruction, the next movement instruction after completing the previous stationary movement, and the operation instruction to reach the destination position. The instruction queue composed of a series of instructions completely describes the task to be performed by the virtual AGV. S5: Simulate AGV operation using time-slice progression; after the simulation begins, the simulator will simulate the AGV's movement when executing commands, the AGV's operation at the station, and the station's state changes. Each simulation time slice progression includes the following steps: S51: Advancing Simulation Time: The simulation scheduling includes a simulation timer, which is responsible for recording the number of frames advanced by the simulation and the duration of the simulated reality. Each time the simulation time slice advances, the simulation timer is advanced by one simulation step. S52: Calculate and update the state of the virtual AGV in the simulation: During each simulation time slice, the state of the virtual AGV in the simulation will be updated; the AGV state update here specifically includes the current running state, moving speed and rotation speed of the AGV; S53: Execute the movement of the virtual AGV: The movement is executed based on the AGV's current speed. Specifically, the displacement Δd of the AGV within this simulation step is calculated based on the current speed and the simulation step size, and then added to the cumulative displacement of the previous simulation step size to obtain the cumulative displacement d of the moving object relative to the starting point of the edge on the current edge. t If the cumulative displacement d at this time t If the length of this edge has been exceeded, it means the AGV will move to a new edge. Therefore, update the edge containing the AGV and its cumulative displacement d relative to the starting point of the edge. t ; S54: Execute the rotation of the virtual AGV: When the AGV needs to execute the rotation command in the instruction, the target direction angle that the AGV needs to reach by rotating will be calculated first based on the current direction angle of the AGV and the rotation command, and the next rotation direction of the AGV will be determined; the angular velocity of the AGV will be calculated during rotation, and the current direction angle of the AGV will be updated based on the current angular velocity in each time slice advancement until the target direction angle is reached; S55: Execute virtual AGV interaction actions with the station: After the AGV completes its movement and arrives at the station, it will execute the interaction actions with the station according to the operation in the instruction. The simulation will use an action timer to simulate the time required for the AGV to execute the interaction actions. After the AGV completes its interaction with the station, it will update the AGV's carrying status and the status of the corresponding station.

2. The intelligent automated parking system scheduling simulation method based on AGV as described in claim 1, characterized in that, Key nodes in the parking lot environment map correspond to positioning points laid out within the parking lot for AGVs to use for positioning. The information of a node includes the node ID, the node's location coordinates, the node type, the node's set of adjacent nodes, the node's outgoing edge set, and the node's incoming edge set. The edges between nodes correspond to the paths between positioning points within the parking lot, describing the connectivity between nodes, including the edge ID, the edge's start point, the edge's end point, the edge's length, the edge's disallowability, the AGV's passable width limit on the edge, and the AGV's passable height limit on the edge.

3. The intelligent automated parking system scheduling simulation method based on AGV as described in claim 1, characterized in that, The initial status information of the AGV includes the ID, position, and direction of the virtual AGV, as well as the parameters of the AGV, including vehicle length, vehicle width, and maximum vehicle speed.

4. The intelligent automated parking system scheduling simulation method based on AGV as described in claim 1, characterized in that, The motion state of a virtual AGV in the current frame is calculated based on its current motion target and the AGV's state in the previous frame; the motion target is determined by each dequeue instruction in the instruction queue.

5. The intelligent automated parking system scheduling simulation method based on AGV as described in claim 1, characterized in that, AGV scheduling simulation includes a simulator state change monitoring mechanism. When the simulator state changes, a predetermined task can be executed by registering the corresponding simulator state change event listener. The simulator's lifecycle includes the following states: simulation uninitialized state, simulation initialized state, and simulation in progress state. The simulator has the following state change events: simulation initialization event, simulation start event, simulation progress event, and simulation end event. The simulation initialization event is triggered when the simulator changes from an uninitialized state to an initialized state. The simulation start event is triggered when the simulator changes from an initialized state to a running state. The simulation advance event is triggered when the simulator advances one frame each time it is in the running state. The simulation end event is triggered when the simulator changes from a running state to an uninitialized state. The above four events correspond to the simulation initialization event listener, simulation start event listener, simulation advance event listener, and simulation end event listener, respectively.

6. The intelligent automated parking system scheduling simulation method based on AGV as described in claim 1, characterized in that, AGV scheduling simulation includes a simulation process playback module. Each time the simulator advances one simulation step, it iterates through all AGVs in the simulation, recording the current state, position, and orientation angle of each AGV sequentially in a memory data container. At the end of the simulation, the simulation process record stored in the data container is persisted, and this data is used as the simulation process record. Then, using the simulation process record, the movement of the AGVs is reproduced. That is, at each simulation step, the corresponding state of each AGV in the current frame is obtained from the simulation record, and the corresponding moving objects are created or updated, thus completing the playback of the completed simulation process.

7. The intelligent automated parking system scheduling simulation method based on AGV as described in claim 1, characterized in that, The AGV scheduling simulation includes a visualization module. The visualization module uses a web client to graphically display the specific environment of the parking lot and the current position, direction, and current carrying status of each virtual AGV in the parking lot, showing the real-time operation status of the parking lot.

8. The intelligent automated parking system scheduling simulation method based on AGV as described in claim 1, characterized in that, The virtual AGVs in the AGV scheduling simulation have virtual communicators. Each virtual AGV in the simulation is equipped with a virtual communicator. The function of the virtual communicator is to simulate the status update reporting of the AGV in reality. During the movement and operation of the virtual AGV in the simulation, the virtual AGV sends its latest status and reported task progress updates to the upper-level scheduling system through the virtual communicator. Only then can the scheduling system make the next scheduling step based on the task execution progress update information of the AGV.

9. A simulation device for scheduling an intelligent automated parking system based on AGVs, comprising a memory and one or more processors, wherein the memory stores executable code, characterized in that... When the processor executes the executable code, it implements a simulation method for scheduling an intelligent three-dimensional parking lot based on AGV as described in any one of claims 1-8.

10. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed by the processor, it implements a simulation method for scheduling intelligent three-dimensional parking lots based on AGVs as described in any one of claims 1-8.

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