Industrial simulation control system and control method

Abstract

The present invention discloses an industrial simulation control system and control method. The control system includes a global control layer, a simulation agent layer, an interpreter layer, an agent regulation layer, and a data management layer. The control method includes the following steps: S1. Creating a simulation agent: instantiating a simulation agent through the simulation model in the simulation agent layer model library, and setting the parameters of each component of the simulation agent; S2. Configuring the control objectives, decision-making methods, and resource equipment queues involved in the simulation agent completing the simulation task, and initializing the starting point of the simulation task; S3. After all simulation agents are created, arranging the simulation scene, running the simulation, and completing the simulation task. Each component in the simulation is represented by an instantiated simulation agent. Each simulation agent is independent of each other and only interacts and collaborates when there is a task association. The simulation is completed by a group of simulation agents, thereby realizing self-driving simulation. The design of the simulation agent is simplified, and the control of the industrial production line simulation agent during simulation is simplified.

Description

Technical Field

[0001] The present invention belongs to the technical field of industrial simulation, and in particular relates to an industrial simulation control system and a control method. Background Art

[0002] With the development of industrial production, before a new industrial production line goes online, a production test will be conducted in a simulation environment to simulate the industrial production line in order to better evaluate the feasibility and production efficiency of the industrial production line. After the industrial scene is arranged and the simulation agent is set up according to the needs of the industrial production line, the simulation agent involved completes a specific workflow according to the production task requirements of the industrial production line. In existing simulation technologies, when simulating a complete industrial production line, the relationship establishment mechanism between the simulation agents in the industrial scene and the interaction and coordination mechanism between the agents are not perfect enough. It is impossible to fully realize the self-driven operation of the simulation system with different agents in the simulation environment, making it impossible to simulate and test production tasks that require the collaboration of multiple equipment agents, thereby affecting the progress of the industrial production line. Summary of the Invention

[0003] In order to solve the above technical problems, the present invention proposes an industrial simulation control system and a control method.

[0004] The purpose of the present invention is achieved through the following technical solutions:

[0005] The present invention provides an industrial simulation control system, comprising a global control layer, a simulation agent layer, an interpreter layer, an agent regulation layer, and a data management layer. The global control layer includes global configuration, simulation material configuration, and entity generation, and sends global configuration and simulation material configuration data to the interpreter layer, and generates entities according to simulation tasks during the simulation process. The simulation agent layer includes a model library composed of simulation models and an agent library composed of simulation agents instantiated from the simulation models. The simulation agent sends task requests to the interpreter layer, receives execution plans returned by the interpreter layer, and completes the simulation tasks according to the execution plans. The interpreter layer parses the task request of the simulation agent and sends the parsed result to the agent control layer. After parsing the control result returned by the agent control layer, it replies to the simulation agent and sends it to the data management layer. The agent control layer receives the parsed task request sent by the interpreter layer, obtains the data required for control from the data management layer, controls the simulation agent, assigns a task execution plan to the simulation agent, and replies to the interpreter layer. The data management layer receives and stores the information obtained during the simulation process from the simulation agent layer, the interpreter layer, and the agent control layer, and provides the data required by the agent control layer during the simulation process.

[0006] As a further improvement, the simulation model consists of a simulation physical model, a function device, a behavior tree, a communicator, a motion device and a timer: the simulation physical model is an executor for the simulation model to perform specific operations; the function device configures parameters of the executor according to the simulation task; the behavior tree is used to customize the execution or operation process of the simulation physical model; the communicator is used to communicate with the interpreter layer; the motion device is used to drive the simulation physical model to move and perform activities; the timer is used to record the movement duration of the simulation physical model.

[0007] As a further improvement, the intelligent agent control layer includes a task queue, a resource queue, a control target, a decision method and a scheduler; the task queue: arranges tasks in the order that the simulated intelligent agent needs to execute; the resource queue: the collection of devices needed to execute the simulation task; the control target: sets the overall goal and local task goal that the simulation task needs to achieve based on the task queue and resource queue; the decision method: the strategy method adopted to enable the simulation task to achieve the control target; the scheduler: manages the task queue, resource queue, control target and decision method and calculates the task execution plan.

[0008] As a further improvement, the data management layer includes data management of simulation logs, task status, resource status, agent status, and agent attributes. The simulation log records relevant information of the simulation process for backtracking the simulation process; the task status records the status of the simulation task; the resource status records the status of other device resources in the simulation task except the simulation agent; the agent status records the current status of the simulation agent in the simulation task; and the agent attributes record the attributes of the simulation agent in the simulation task and the parameter information of the storage function configuration.

[0009] As a further improvement, the industrial simulation control system includes an input module, a storage module, a processing module and a display module. The input module is used to input simulation tasks and perform parameter configuration. The storage module is used to store data information during the simulation process. The processing module is used to send task requests to the interpreter layer according to the task request of the simulation agent. The interpreter layer parses the task request and sends it to the agent control layer. The task execution plan is regulated and allocated, and then returned to the simulation agent after parsing by the interpreter layer to complete the simulation task. The display module is used to display the simulation data during the simulation process.

[0010] The industrial simulation control system provided by the present invention includes a global control layer, a simulation agent layer, an interpreter layer, an agent regulation layer, and a data management layer. The global control layer includes global configuration, simulation material configuration, and entity generation, sends global configuration and simulation material configuration data to the interpreter layer, and generates entities according to simulation tasks during the simulation process. The simulation agent layer includes a model library composed of simulation models and an agent library composed of simulation agents generated by instantiating the simulation models. The simulation agent sends task requests to the interpreter layer and receives execution plans returned by the interpreter layer, completing the simulation tasks according to the execution plans. The interpreter layer parses the task requests of the simulation agent and sends the parsed results to the agent regulation layer. After parsing the control results returned from the agent regulation layer, it replies to the simulation agent and sends them to the data management layer. The agent regulation layer receives the parsed task requests sent by the interpreter layer, obtains the data required for regulation from the data management layer, regulates the simulation agent, assigns task execution plans to the simulation agent, and replies to the interpreter layer. The data management layer obtains and stores information during the simulation process from the simulation agent layer, the interpreter layer, and the agent regulation layer. During simulation, each component can be represented by an instantiated agent. Each agent is independent of the others and only interacts and collaborates when tasks are relevant. Simulations are completed through the collaboration of a group of agents, achieving self-driving simulation. Only the agent control layer, based on information such as the status of all agents, allocates task execution plans. This narrows the agent's domain of concern, eliminating the need to constantly monitor the status of other agents. This allows agents to focus on reporting simulation tasks to the interpreter layer and executing the tasks returned by the interpreter layer. This streamlines the design of the agent and simplifies the control of the agent in industrial production lines during simulation.

[0011] The present invention also provides an industrial simulation control method, including further improvements based on any one of the above-mentioned industrial simulation control systems, and the specific steps are as follows: S1. Creating a simulation agent: Instantiating the simulation model in the model library to generate a simulation agent, and setting the parameter content of each component of the simulation agent; S2. Configuring global items: including configuring the control objectives, decision-making methods and resource queues involved in the simulation agent completing the simulation task, and initializing the starting point of the simulation task; S3. Running the simulation scenario: After creating all simulation agents and arranging the simulation scenario, run the simulation according to the simulation task, simulate production and complete the production task.

[0012] As a further improvement, in step S3, running the simulation scenario according to the simulation task includes the following steps:

[0013] S31. Initialize the data management layer: clear the cached data and historical residual data of the data management layer; S32. Agent registration: configure the component parameters, simulation physical model size and operation process of the simulation agent, and store them in the data management layer through the interpreter layer; S33. Determine whether the simulation task is executed by a single simulation agent. If so, the single simulation agent completes the simulation task; otherwise, arrange the simulation scene according to multiple simulation agents, and interact according to the simulation task to complete the simulation task.

[0014] As a further improvement, in step S33, when arranging the simulation scene according to multiple simulation agents, at least one workstation simulation agent, one material simulation agent and one equipment simulation agent are included.

[0015] As a further improvement, in the step S33, multiple simulation agents interact according to the simulation task to complete the simulation task, including the following steps: S331, after the material simulation agent arrives at the designated workstation according to the simulation task, it sends a material in place signal to the workstation simulation agent; S332, the workstation simulation agent starts the operation process and executes the workstation behavior tree related to the task according to the simulation task; S333, the simulation agent layer sends the task request of the workstation simulation agent to the interpreter layer according to the operation process of the workstation simulation agent, and the interpreter layer parses the task request and sends it to the agent control layer; S334, the agent scheduling layer receives the task request sent by the interpreter layer and adds the task to the task queue, calculates and allocates the task execution plan according to the resource queue, control target and decision method and the real-time resource status and agent status data of the data management layer, and replies the execution plan to the interpreter layer; S335, the interpreter layer parses the received execution plan and returns it to the workstation simulation agent, and at the same time sends the execution plan to the interpreter layer. The program is sent to the data management layer for storage; S336, the workstation simulation intelligent body receives the return data from the interpreter layer and determines whether the task request is successful. Otherwise, it means that the task request of the workstation simulation intelligent body fails, and jumps to step S337; if yes, it means that the task request of the workstation simulation intelligent body is successful, the workstation simulation intelligent body assigns the task to the equipment simulation intelligent body and notifies the execution of the task, and jumps to step S338; S337, according to the simulation task, determines whether the workstation simulation intelligent body has any remaining tasks, if yes, jumps to step S333 to continue to execute other simulation tasks; if no, it means that the workstation simulation intelligent body has completed the simulation task, terminates the workstation simulation intelligent body's operation process and notifies the material simulation intelligent body to complete the operation process, and jumps to step S339; S338, the equipment simulation intelligent body starts the operation process according to the received assigned task, and executes the equipment behavior tree related to the task; S339, after the simulation intelligent body completes the operation process, it sends a completion signal to the interpreter layer, and saves the data to the data management layer after parsing by the interpreter layer.

[0016] As a further improvement, in step S336, the workstation simulation agent assigns tasks to the device simulation agent based on the device identifier returned by the interpreter layer. The corresponding task is then determined to be for a single device simulation agent. If so, the device simulation agent receives the assigned task and then executes step S338. Otherwise, if the assigned task is for multiple device simulation agents, it is necessary to confirm that the corresponding device simulation agents receive the assigned tasks and reach the task starting target point before executing step S338.

[0017] The industrial simulation control method provided by the present invention adopts the above-mentioned technical content, and should have the same or corresponding technical effects as the industrial simulation control system, so it will not be described in detail. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative effort.

[0019] Figure 1 It is a structural diagram of the industrial simulation control system of the present invention;

[0020] Figure 2 Schematic diagram of the process of the industrial simulation control method of the present invention;

[0021] Figure 3 Schematic diagram of the simulation-agent interaction process of the industrial simulation control method of the present invention. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features therein can be combined with each other unless there is a conflict.

[0023] Combine Figure 1 As shown, an embodiment of the present invention provides an industrial simulation control system, including a global control layer, a simulation agent layer, an interpreter layer, an agent regulation layer and a data management layer.

[0024] The global control layer is responsible for the entire simulation system. It sends global configuration and simulation material configuration data to the interpreter layer, which then sends it to the data manager for storage. During the simulation process, entities are generated based on the simulation tasks. The global control layer includes global configuration, simulation material configuration, and entity generation. Global configuration involves the initial configuration of global settings such as simulation control objectives and decision-making methods based on the industrial production line required for simulation. Simulation material configuration involves configuring the quantity and style of simulation materials, i.e., production tasks. Entity generation involves dynamically generating the corresponding number of simulation materials during the simulation process.

[0025] The simulation agent layer includes a model library composed of simulation models and an agent library composed of simulation agents instantiated from the simulation models. The simulation agents send task requests to the interpreter layer and receive execution plans returned by the interpreter layer, completing the simulation tasks according to the execution plans. The simulation model consists of six components: a simulation physical model, a function controller, a behavior tree, a communicator, a motion controller, and a timer. The simulation physical model is the actuator that performs specific operations on the simulation model, including its appearance, movable joints, and internal structure. The function controller configures the parameters of the actuator according to the simulation task, including activity domains and functional configuration. The behavior tree is used to customize the execution or operation process of the simulation physical model. The communicator is used to communicate with the interpreter layer. The motion controller is used to drive the simulation physical model to move and perform activities. The timer is used to record the duration of the simulation physical model's movement. The simulated intelligent body generated by instantiating the simulation model can be a material simulation intelligent body, a workstation simulation intelligent body, and an equipment simulation intelligent body; the simulated intelligent body generates a unique intelligent body identifier through registration and stores the intelligent body attribute values ​​in the data management layer to facilitate the distinction between intelligent body attribute management and intelligent body control layer regulation; the simulated intelligent body generates a task number during the simulation process based on the functional behavior nodes and functional configuration of the behavior tree to facilitate control and identification by the intelligent body control layer.

[0026] The interpreter layer, located between the simulated agent layer and the agent control layer, is responsible for parsing and forwarding data. The interpreter layer parses the simulated agent's task requests and sends the results to the agent control layer and the data management layer. It also parses the control results returned from the agent control layer, responds to the simulated agent, and sends them to the data management layer. As an intermediate layer, the interpreter facilitates isolation between system layers, ensuring their independence and non-interference.

[0027] The agent control layer receives parsed task requests from the interpreter layer, obtains the data required for control from the data management layer, calculates a task execution plan, assigns the task execution plan to the simulated agent, and replies to the interpreter layer. The agent control layer includes a task queue, a resource queue, a control target, a decision method, and a scheduler. Initialization and configuration are performed according to the requirements of the industrial production line simulation. The task queue is a list of production tasks generated by the production line in real time, arranged in order by the time the task is generated, and the simulated agent execution is scheduled using a first-in-first-out method. The resource queue is a collection of devices that are idle and can execute tasks in real time. The control target is the overall production objective function of the simulated industrial production line and the execution objective function of the local tasks, such as minimizing the simulated operation time or minimizing the simulation resource utilization. The decision method is the strategy adopted to achieve the control target. The scheduler manages the task queue, resource queue, control target, and decision method, calculates the real-time task queue and resource queue, and obtains the task execution plan based on the control target and decision method.

[0028] The data management layer receives and stores information from the simulation agent layer, interpreter layer, and agent control layer during the simulation process, and provides the information required by the agent control layer during the simulation process. This includes data management for simulation logs, task status, resource status, agent status, and agent attributes. Data management includes storage, query, update, and deletion. The simulation log records relevant information about the simulation process, including simulation time, events, and the simulation agent executing the simulation task, for use in tracing back the simulation process. The task status records the status of the simulation task, which includes statuses such as pending, received, and completed. The resource status records the status of other resources in the simulation task besides the simulation agent equipment, such as the status of the material frame resources: whether the material frame is occupied, the material frame label, and the distribution of parts within the material frame. The agent status records the current status of the simulation agent in the simulation task, including idle, operating, waiting, blocked, and real-time location. The agent attributes record the attributes of the simulation agent in the simulation task and store parameter information for the function configuration.

[0029] As a further preferred embodiment, it includes an input module, a storage module, a processing module and a display module.

[0030] The input module may be a keyboard and / or a mouse, and is used to input simulation tasks and configure parameters;

[0031] The storage module may be a storage hard disk for storing data information during the simulation process.

[0032] The processing module, which may be a processor, is configured to send a task request to the interpreter layer based on the task request of the simulation agent. The interpreter layer parses the task request and sends it to the agent control layer. The interpreter layer controls and allocates a task execution plan, which is parsed by the interpreter layer and then returned to the simulation agent to complete the simulation task.

[0033] The display module may be a display, which is used to display simulation data during the simulation process.

[0034] An embodiment of the present invention provides an industrial simulation control system. During simulation, each component in the simulation can be represented by an instantiated simulation agent. Each simulation agent is independent of each other and only interacts and collaborates when tasks are associated. The simulation is completed by a group of simulation agents, thus achieving self-driving simulation. Only the agent control layer performs control based on information such as the status of all simulation agents and then allocates task execution plans. This narrows the focus of the simulation agent, eliminating the need to constantly monitor the status of other simulation agents. This allows the simulation agent to focus on reporting simulation tasks to the interpreter layer and executing tasks returned by the interpreter layer. This simplifies the design of the simulation agent and simplifies the control of the industrial production line simulation agent during simulation.

[0035] The embodiment of the present invention further provides an industrial simulation control method, including any preferred embodiment of the above industrial simulation control system, and the mutual combination of the various embodiments of the industrial simulation control system in the case of no conflict, specifically, in combination with Figure 2 As shown, the following steps are included:

[0036] S1. Create a simulation agent: Generate a simulation agent by instantiating the simulation model in the model library. Depending on the needs of the industrial production line, the simulation agent can be a material simulation agent, a workstation simulation agent, or an equipment simulation agent. Set the parameters of each component of the simulation agent.

[0037] S2. Configure global items: This includes initial configuration of the control objectives, decision-making methods, and equipment queues involved in the simulation task for the simulation agent based on the industrial production line required by the simulation, and initializing the starting point of the simulation task.

[0038] S3. Run the simulation scenario: After creating all the simulation agents and arranging the simulation scenario, run the simulation to simulate production and complete the production task. Specifically, it includes the following steps:

[0039] S31. Initialize the data management layer: clear cached data and historical residual data in the data management layer;

[0040] S32, Intelligent Agent Registration: Configure the component parameters, simulation physical model dimensions, and operation process of the simulated intelligent agent, generate a unique intelligent agent identification code for the simulated intelligent agent, and store it in the intelligent agent attributes in the data management layer after parsing by the interpreter layer for identification during control by the intelligent agent control layer.

[0041] S33. Determine whether the simulation task is performed by a single simulation agent. If so, the single simulation agent completes the simulation task; otherwise, arrange the simulation scene according to multiple simulation agents. According to the simulation requirements of the industrial production line, each component involved will be presented as an instantiated simulation agent, which includes at least one workstation simulation agent, one material simulation agent, and one equipment simulation agent. Through the interaction between the workstation simulation agent, the material simulation agent, and the equipment simulation agent, the simulation operation is driven to complete the simulation; through the interaction between the equipment simulation agent and the equipment simulation agent, multi-device collaboration is achieved to complete the multi-device collaboration task. Specifically, combined with Figure 3 As shown, the following steps are included:

[0042] S331. After the material simulation agent arrives at the designated workstation according to the simulation task, it sends a material in place signal to the workstation simulation agent.

[0043] S332: The workstation simulation agent starts the operation process and executes a system's functional behavior node according to the workstation behavior tree related to the simulation task. If the executed functional behavior node is the "send task request" node, the process jumps to step S333.

[0044] S333: The simulation agent layer sends a task request to the interpreter layer through the communicator according to the work flow of the station simulation agent. The interpreter layer parses the task request and sends it to the agent control layer.

[0045] S334: The agent scheduling layer receives the task request from the interpreter layer and adds the task to the task queue. It calculates and allocates a task execution plan based on the resource queue, control objectives, decision-making method, and the real-time resource and agent status data of the data management layer, and replies to the interpreter layer with the execution plan.

[0046] S335: The interpreter layer parses the received execution plan and returns it to the workstation simulation agent. It also sends the execution plan to the data management layer and saves it in the simulation log.

[0047] S336: The workstation simulation agent receives the return data from the interpreter layer, obtains the execution plan content that the workstation simulation agent can recognize, and determines whether the task request is successful. If not, it indicates that the workstation simulation agent's task request failed. The reasons for the request failure may be that the agent scheduling layer's resource queue is empty, the available resources are empty, the task queue is empty, the decision method does not support the control target, etc., and the process jumps to step S337. If yes, it indicates that the workstation simulation agent's task request is successful. The workstation simulation agent assigns the task to the equipment simulation agent and notifies it to execute the task, and the process jumps to step S338.

[0048] S337: Determine whether the workstation simulation agent has any remaining tasks based on the simulation task. If so, jump to step S333 to continue executing other simulation tasks. If not, it indicates that the workstation simulation agent has completed the simulation task, terminates the workstation simulation agent's operation process, notifies the material simulation agent to complete the operation process, and jumps to step S339.

[0049] S338. The device simulation agent starts the operation process according to the received assigned task and executes the device behavior tree related to the assigned task.

[0050] S339. After the intelligent agent completes the operation process, it sends a completion signal to the interpreter layer, which is parsed by the interpreter layer and saved to the data management layer, including data such as task status and intelligent agent status.

[0051] As a further preferred embodiment, in step S336, the workstation simulation agent assigns tasks to the device simulation agent, and the corresponding tasks are assigned to the device simulation agent according to the device identifier returned by the interpreter layer, and then it is determined whether the assigned task is for a single device simulation agent. If so, the device simulation agent receives the assigned task and then executes step S338; otherwise, if the assigned task is for multiple device simulation agents, it is necessary to confirm that the corresponding device simulation agents receive the assigned tasks and arrive at the task starting target point before executing step S338, so as to avoid the time difference caused by the time difference between the workstation simulation agent receiving the execution plan returned by the interpreter layer and the time difference between the time the task is assigned to the device simulation agent and the time difference between the time the device simulation agent arrives at the task starting point. In this embodiment, combined with Figure 3As shown, the execution plan received by the workstation simulation agent includes the allocation of tasks T1 and T2, where T1 needs to be completed by two equipment simulation agents, so the allocation task contains task instructions for two equipment simulation agents; T2 needs to be completed by one equipment simulation agent, so the task only contains task instructions for one equipment simulation agent. The workstation simulation agent simultaneously assigns task T1 to device A agent and device B agent, and then assigns task T2 to device C agent. After receiving task T1-1, device B agent receives task T1-2, and device C agent receives task T2, they respectively send the received assigned tasks to the interpreter layer. After being parsed by the interpreter layer, they send them to the data management layer to update the agent status and task status. At the same time, device A agent arrives at task point LT1-1 and notifies device B agent. Device B agent arrives at task point LT1-2 and notifies device A agent. Device A agent and device B agent associated with the task simultaneously start the operation process. Device C agent starts the operation process at LT2 after arriving at task point LT2 and executes the assigned device behavior tree. After execution, they send completion signals to the interpreter layer respectively. After being parsed by the interpreter layer, they are saved to the data management layer, including data such as task status and agent status.

[0052] Since the above technical contents are adopted, an industrial simulation control method according to an embodiment of the present invention should have the same or corresponding technical effects as described in the industrial simulation control system, and therefore will not be described in detail.

[0053] The above description sets forth many specific details to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein, and therefore, it should not be understood as limiting the scope of protection of the present invention.

[0054] In summary, although the present invention lists the above-mentioned preferred embodiments, it should be noted that although technicians in this field can make various changes and modifications, unless such changes and modifications deviate from the scope of the present invention, they should be included in the scope of protection of the present invention.

Claims

1. An industrial simulation control method, characterized in that: An industrial simulation control system is used; the system includes a global control layer, a simulation intelligence layer, an interpreter layer, an intelligence regulation layer, and a data management layer: The global control layer includes global configuration, simulation material configuration and entity generation, sends global configuration and simulation material configuration data to the interpreter layer, and generates entities according to simulation tasks during the simulation process; The simulation agent layer includes a model library composed of simulation models and an agent library composed of simulation agents instantiated from simulation models. The simulation agent sends task requests to the interpreter layer and receives execution plans returned by the interpreter layer, and completes the simulation task according to the execution plans. The interpreter layer parses the task request of the simulated agent and sends the parsing result to the agent control layer. It also parses the control result returned by the agent control layer and sends it back to the simulated agent and the data management layer. The agent control layer receives the parsed task request sent by the interpreter layer, obtains the data required for control from the data management layer, controls the simulated agent, assigns the task execution plan to the simulated agent, and responds to the interpreter layer; The data management layer receives and stores information obtained from the simulation agent layer, the interpreter layer, and the agent control layer during the simulation process, and provides the agent control layer with data required during the simulation process; The method includes: S1. Creating a simulation agent: Instantiating the simulation model in the model library to generate a simulation agent, and setting the parameters of each component of the simulation agent; S2. Configuring global items: This includes configuring the control objectives, decision-making methods, and resource queues involved in the simulation agent completing the simulation task, and initializing the starting point of the simulation task; S3. Running the simulation scenario: After creating all simulation agents and arranging the simulation scenario, running the simulation according to the simulation task, simulating production, and completing the simulation task; In step S3, running the simulation according to the simulation task includes the following steps: S31, initializing the data management layer: clearing cached data and historical residual data in the data management layer; S32, agent registration: configuring the simulation agent's component parameters, simulation physical model dimensions, and operation process, and storing them in the data management layer through the interpreter layer; S33, determining whether the simulation task is executed by a single simulation agent. If so, the single simulation agent completes the simulation task; otherwise, the simulation scene is arranged according to multiple simulation agents, and they interact according to the simulation task to complete the simulation task; In step S33, multiple simulation agents interact according to the simulation task to complete the simulation task, including the following steps: S331, after the material simulation agent arrives at the designated workstation according to the simulation task, it sends a material in place signal to the workstation simulation agent; S332, the workstation simulation agent starts the operation process and executes the workstation behavior tree related to the task according to the simulation task; S333, the simulation agent layer sends the task request of the workstation simulation agent to the interpreter layer according to the operation process of the workstation simulation agent, and the interpreter layer parses the task request and sends it to the agent control layer; S334, the agent scheduling layer receives the task request sent by the interpreter layer and adds the task to the task queue, calculates and allocates the task execution plan according to the resource queue, control target and decision method and the real-time resource status and agent status data of the data management layer, and replies the execution plan to the interpreter layer; S335, the interpreter layer parses the received execution plan and returns it to the workstation simulation agent, and sends the execution plan to the data management layer. The management layer saves; S336, the workstation simulation agent receives the return data from the interpreter layer and determines whether the task request is successful. Otherwise, it means that the task request of the workstation simulation agent fails, and jumps to step S337; if yes, it means that the task request of the workstation simulation agent is successful, the workstation simulation agent assigns the task to the equipment simulation agent and notifies the execution of the task, and jumps to step S338; S337, according to the simulation task, determines whether the workstation simulation agent has any remaining tasks, if yes, jumps to step S333 to continue to execute other simulation tasks; if no, it means that the workstation simulation agent has completed the simulation task, terminates the operation process of the workstation simulation agent and notifies the material simulation agent to complete the operation process, and jumps to step S339; S338, the equipment simulation agent starts the operation process according to the received assigned task, and executes the equipment behavior tree related to the task; S339, after the simulation agent completes the operation process, it sends a completion signal to the interpreter layer, and saves the data to the data management layer after parsing by the interpreter layer.

2. The industrial simulation control method according to claim 1, wherein: In the industrial simulation control system, the simulation model consists of a simulation physical model, a function device, a behavior tree, a communicator, a motion device, and a timer: The simulation physical model is an executor for the simulation model to perform specific operations; The function device is used to configure parameters of the actuator according to the simulation task; The behavior tree is used to customize the execution or operation process of the simulation physical model; The communicator is used to communicate with the interpreter layer; The motion device is used to drive the simulated physical model to move and perform activities; The timer is used to record the movement duration of the simulated physical model.

3. The industrial simulation control method according to claim 1, wherein: In the industrial simulation control system, the intelligent agent control layer includes task queues, resource queues, control targets, decision methods, and schedulers: The task queue is arranged according to the order of tasks that the simulated agent needs to execute; The resource queue: a collection of devices required to execute simulation tasks; The control target is to set the overall target and local tasks that the simulation task needs to achieve according to the task queue and resource queue. Target; The decision-making method is a strategic method used to make the simulation task achieve the control target; The scheduler manages the task queue, resource queue, control objectives and decision-making methods and calculates the task execution plan.

4. The industrial simulation control method according to claim 1, wherein: In the industrial simulation control system, the data management layer includes data management of simulation logs, task status, resource status, agent status, and agent attributes: The simulation log records the relevant information of the simulation process and is used to trace the simulation process; The task status: records the status of the simulation task; The resource status: records the status of other device resources in the simulation task except the simulation agent; The agent status: records the current status of the simulated agent in the simulation task; The agent attributes are used to record the attributes of the simulation agent in the simulation task and the parameter information of the storage function configuration.

5. The industrial simulation control method according to claim 1, wherein: The industrial simulation control system includes input module, storage module, processing module and display module: The input module is used to input simulation tasks and perform parameter configuration; The storage module is used to store data information during the simulation process; The processing module is used to send a task request to the interpreter layer according to the task request of the simulation agent. The interpreter layer parses the task request and sends it to the agent control layer. The interpreter layer controls and allocates a task execution plan, which is parsed by the interpreter layer and then returned to the simulation agent to complete the simulation task. The display module is used to display simulation data during the simulation process.

6. The industrial simulation control method according to claim 1, wherein: In the step S33, when arranging the simulation scene according to multiple simulation agents, at least one workstation simulation agent, one material simulation agent, and one equipment simulation agent are included.

7. The industrial simulation control method according to any one of claims 1 to 6, characterized in that: In the step S336, the workstation simulation agent assigns tasks to the device simulation agent based on the device identifier returned by the interpreter layer. The corresponding tasks are then assigned to the device simulation agent, and it is determined whether the assigned tasks are for a single device simulation agent. If so, the device simulation agent receives the assigned tasks and then executes step S338. Otherwise, the assigned tasks are for multiple device simulation agents, and it is necessary to confirm that the corresponding device simulation agents receive the assigned tasks and arrive at the task starting target point before executing step S338.

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