Transmission line control method based on Linux qt + ARM

Abstract

The invention discloses a Linux qt + ARM-based transmission line control method, which belongs to the technical field of edge computing, and comprises the following steps: establishing an edge node and a plurality of control nodes, deploying a Linux QT system in the edge node, establishing a multi-agent module in the Linux QT system, and establishing a multi-agent system MAS in the multi-agent module, the technical problem of distributed scheduling optimization of a multi-agent system in complex equipment management is solved, multi-agent collaborative scheduling is realized, the collaborative efficiency among multiple regions is remarkably improved, scheduling lagging is reduced, equipment expansion and functional module replacement are convenient and rapid, and adaptability is high.

Description

Technical Field

[0001] The present invention belongs to the field of edge computing technology, and in particular relates to a conveyor line control method based on Linux qt+ARM. Background Art

[0002] Conveyor lines are one of the key equipment in the automation industry, used to efficiently and continuously transport materials from one place to another. In the wafer production process of the semiconductor industry, conveyor lines are needed to transport wafers to designated locations for subsequent processing. In intelligent warehousing systems, conveyor lines are needed for material input, output, and cargo transportation. In intelligent logistics systems, conveyor lines are needed for sorting and transporting to designated storage locations. Today, with the continuous upgrading of industry, more and more production and manufacturing are pursuing intelligence, efficiency, and automation. From production and manufacturing to packaging and delivery, the process of material transmission is often inseparable, and conveyor lines play an important role in it. It can be seen that it has a wide range of uses and has penetrated into various industries.

[0003] In traditional technology, the automation control and dispatch coordination of regional equipment usually rely on a centralized monitoring system. At present, most control units such as PLC are used to manage the equipment in different regions. PLC is a controller that requires an external IO expansion module to control the driver to control the motor to achieve the rotation of the conveyor roller. The cost of the PLC control system is high, and the price of both the hardware and software parts is relatively expensive. In addition, the installation and debugging of the PLC control system also requires a certain cost. The PLC control system is suitable for small and medium-sized control tasks, but for large-scale control tasks, the processing capacity of the PLC control system may not meet the requirements. The functions of the PLC control system are limited by the design of its hardware and software parts. In some specific control tasks, the PLC control system may not meet the requirements. In addition, the programming function and expansion capability of the PLC have certain limitations, the interface freedom is not high, and the centralized control structure in the traditional technology is prone to scheduling lags, which is not suitable for the collaborative needs of multiple regions and multiple devices. The PLC control system cannot sense and locate faults in real time, and the recovery time after the equipment fails is long, which affects the system efficiency. The PLC control system is also difficult to quickly add or replace equipment, and the system expansion requires major adjustments, which is time-consuming and costly. Summary of the invention

[0004] The purpose of the present invention is to provide a conveyor line control method based on Linux qt+ARM, which solves the technical problem of distributed scheduling optimization of multi-agent systems in complex equipment management.

[0005] To achieve the above object, the present invention adopts the following technical solution:

[0006] A conveyor line control method based on Linux qt+ARM includes the following steps:

[0007] Step 1: Establish edge nodes and multiple control nodes, deploy Linux QT system in edge nodes, establish multi-agent module in LinuxQT system, build multi-agent system MAS in multi-agent module, initialize Linux QT system and multi-agent system MAS, and all control nodes are registered in multi-agent module;

[0008] Step 2: The Linux QT system generates an overall task based on the task requirements sent by the upper-level scheduling system WCS, and sends the overall task to the multi-agent system MAS; the multi-agent system MAS decomposes the overall task into regional tasks and generates scheduling instructions;

[0009] Step 3: The MAS module distributes regional tasks and scheduling instructions to each control node, and each control node performs the control tasks of its own region;

[0010] Step 4: The control node uploads the task execution data and status data generated in its own area to the Linux QT system. The Linux QT system generates task execution feedback data for each control node based on the uploaded task execution data.

[0011] The Linux QT system transmits the task execution feedback data to the multi-agent system MAS;

[0012] Step 5: The multi-agent system MAS optimizes the scheduling plan based on the task execution feedback data, generates the optimized scheduling instructions, and sends the optimized scheduling instructions to the control node;

[0013] Step 6: The Linux QT system monitors the status data and task execution data of the control node, and issues a fault alarm when an abnormal situation is detected.

[0014] Preferably, the initialization of the Linux QT system includes configuring a basic communication interface and establishing a communication link with an upper scheduling system WCS;

[0015] The architecture of the multi-agent system MAS consists of multiple agents, each of which corresponds to a control task module;

[0016] Each control node is connected to the edge node through a data line, and completes registration in the multi-agent system MAS to obtain registration information, which includes a unique node ID; the multi-agent system MAS confirms the regional control task corresponding to each control node;

[0017] The Linux QT system initializes the multi-agent system MAS, resets the status of each control node to the initial state, clears the task and device status, and checks and ensures that the real-time communication link between the multi-agent module and the control node is normal.

[0018] Preferably, when executing step 2, the following steps are specifically included:

[0019] Step 2-1: The Linux QT system receives task requirements from the upper-level scheduling system WCS, analyzes and breaks them down into specific operation plans, and generates task logic;

[0020] Step 2-2: Define specific mission objectives in multiple areas based on mission logic;

[0021] Step 2-3: Generate an overall task according to the task goal, and send the overall task to the multi-agent system MAS. After receiving the overall task, the multi-agent system MAS starts task decomposition and generation of scheduling instructions;

[0022] Step 2-4: The multi-agent system MAS decomposes the overall task into regional tasks of each agent and generates corresponding scheduling instructions for each control node.

[0023] Preferably, the multi-agent system MAS sends regional tasks and scheduling instructions to each control node, and each control node performs the regional tasks of its own region, controls the sensors in the region to collect signals and execute equipment;

[0024] During the execution process, each control node collects sensor data and task execution status data in real time to generate task execution data and status data;

[0025] Each control node uploads task execution data and status data to the Linux QT system in real time. After receiving the task execution data, the Linux QT system analyzes and organizes the task execution feedback of each control node and generates a feedback data packet for optimization use by the MAS module.

[0026] The Linux QT system passes the feedback data packet to the multi-agent system MAS, providing a basis for its subsequent scheduling optimization.

[0027] Preferably, when executing step 5, the following steps are specifically included:

[0028] Step 5-1: The multi-agent system MAS analyzes the feedback data packet to obtain the completion time of the tasks in each area;

[0029] Step 5-2: Generate an optimized scheduling plan based on the completion time and the following weight formula:

[0030]

[0031] Where N is the number of regions, w i is the weight of region i, and its value is assigned according to the importance of the task, ti The time required to complete the task for area i;

[0032] Step 5-3: Generate new scheduling instructions according to the optimized scheduling plan, and send the new scheduling instructions to each control node.

[0033] Preferably, the Linux QT system continuously monitors the uploaded data of the control node and detects abnormal data in real time. When abnormal data is detected, the Linux QT system triggers an alarm mechanism, generates alarm information, and uploads it to the upper scheduling system WCS.

[0034] Preferably, the edge node is an edge computing server or an edge computing device, and the multi-agent system MAS regards each control node as an agent and establishes a control task module for each agent.

[0035] The Linux qt+ARM-based conveyor line control method described in the present invention solves the technical problem of distributed scheduling optimization of multi-agent systems in complex equipment management. The present invention realizes multi-agent collaborative scheduling, significantly improves the collaboration efficiency among multiple areas, reduces scheduling lag, and is convenient and fast to expand equipment and replace functional modules with strong adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is the main flow chart of the present invention;

[0037] Figure 2 is a flow chart of step 2 of the present invention;

[0038] Figure 3 is a flow chart of step 5 of the present invention;

[0039] Figure 4 It is a system architecture diagram of the present invention. DETAILED DESCRIPTION

[0040] like Figure 1-Figure 4 A conveyor line control method based on Linux qt+ARM is shown, comprising the following steps:

[0041] Step 1: Establish edge nodes and multiple control nodes, deploy Linux QT system in edge nodes, establish multi-agent module in LinuxQT system, build multi-agent system MAS in multi-agent module, initialize Linux QT system and multi-agent system MAS, and all control nodes are registered in multi-agent module;

[0042] The initialization of the Linux QT system includes configuring the basic communication interface and establishing a communication link with the upper scheduling system WCS;

[0043] The architecture of the multi-agent system MAS consists of multiple agents, each of which corresponds to a control task module;

[0044] Each control node is connected to the edge node through a data line, and completes registration in the multi-agent system MAS to obtain registration information, which includes a unique node ID; the multi-agent system MAS confirms the regional control task corresponding to each control node;

[0045] The Linux QT system initializes the multi-agent system MAS, resets the status of each control node to the initial state, clears the task and device status, and checks and ensures that the real-time communication link between the multi-agent module and the control node is normal.

[0046] The edge node is an edge computing server or an edge computing device. The multi-agent system MAS regards each control node as an agent and establishes a control task module for each agent.

[0047] Step 2: The Linux QT system generates an overall task based on the task requirements sent by the upper-level scheduling system WCS, and sends the overall task to the multi-agent system MAS; the multi-agent system MAS decomposes the overall task into regional tasks and generates scheduling instructions;

[0048] The specific steps include:

[0049] Step 2-1: The Linux QT system receives task requirements from the upper-level scheduling system WCS, analyzes and breaks them down into specific operation plans, and generates task logic;

[0050] Step 2-2: Define specific mission objectives in multiple areas based on mission logic;

[0051] Step 2-3: Generate an overall task according to the task goal, and send the overall task to the multi-agent system MAS. After receiving the overall task, the multi-agent system MAS starts task decomposition and generation of scheduling instructions;

[0052] Step 2-4: The multi-agent system MAS decomposes the overall task into regional tasks of each agent and generates corresponding scheduling instructions for each control node.

[0053] Step 3: The MAS module distributes regional tasks and scheduling instructions to each control node, and each control node performs the control tasks of its own region;

[0054] Step 4: The control node uploads the task execution data and status data generated in its own area to the Linux QT system. The Linux QT system generates task execution feedback data for each control node based on the uploaded task execution data.

[0055] The Linux QT system transmits the task execution feedback data to the multi-agent system MAS;

[0056] The multi-agent system MAS sends regional tasks and scheduling instructions to each control node, and each control node performs the regional tasks of its own area, controls the sensors in the area to collect signals and execute equipment;

[0057] During the execution process, each control node collects sensor data and task execution status data in real time to generate task execution data and status data;

[0058] Each control node uploads task execution data and status data to the Linux QT system in real time. After receiving the task execution data, the Linux QT system analyzes and organizes the task execution feedback of each control node and generates a feedback data packet for optimization use by the MAS module.

[0059] The Linux QT system passes the feedback data packet to the multi-agent system MAS, providing a basis for its subsequent scheduling optimization.

[0060] Step 5: The multi-agent system MAS optimizes the scheduling plan based on the task execution feedback data, generates the optimized scheduling instructions, and sends the optimized scheduling instructions to the control node;

[0061] The specific steps include:

[0062] Step 5-1: The multi-agent system MAS analyzes the feedback data packet to obtain the completion time of the tasks in each area;

[0063] Step 5-2: Generate an optimized scheduling plan based on the completion time and the following weight formula:

[0064]

[0065] Where N is the number of regions, w i is the weight of region i, and its value is assigned according to the importance of the task, t i The time required to complete the task for area i;

[0066] In this embodiment, w i The value of is generated by the Linux QT system. In step 2-2, the Linux QT system defines specific task objectives in multiple regions according to the task logic, and then prioritizes the task objectives in each region and determines the weight of the tasks in the region according to the priority division, that is, w i .

[0067] Step 5-3: Generate new scheduling instructions according to the optimized scheduling plan, and send the new scheduling instructions to each control node.

[0068] Step 6: The Linux QT system monitors the status data and task execution data of the control node, and issues a fault alarm when an abnormal situation is detected.

[0069] The Linux QT system continuously monitors the uploaded data of the control node and detects abnormal data in real time. When abnormal data is detected, the Linux QT system triggers the alarm mechanism, generates alarm information, and uploads it to the upper-level scheduling system WCS.

[0070] The following is a specific application scenario example of this embodiment:

[0071] In the semiconductor industry wafer production process, the control objects of each conveyor line area include sensors and roller motors, and each area is equipped with a control node (ARM board). These control nodes are responsible for data collection and equipment control, and the system goal is to efficiently dispatch and transport goods.

[0072] The scene implementation process is as follows:

[0073] Step S1: System startup and initialization: Linux QT system, MAS module, and ARM control nodes in each area of ​​the conveyor line are started and initialized, and the registration of agents in the multi-agent system MAS is completed;

[0074] Step S2: Task demand generation and distribution. The upper-level WCS system generates a transportation task, such as "transporting goods from area A to area B". The Linux QT system receives the demand, breaks it down to generate the overall task, and sends the task to the MAS module. The MAS module decomposes the scheduling instructions according to the task and distributes them to the relevant control nodes.

[0075] Step S3: Distribute scheduling instructions and task execution. The multi-agent system MAS issues handling instructions to the ARM boards responsible for areas A and B. The ARM board in area A controls the roller motor to carry out cargo handling. After receiving the handling completion signal, the ARM board in area B starts the roller motor to complete the transportation task.

[0076] Step S4: Data upload and feedback, the ARM board collects task execution and sensor data in real time and transmits it to the LinuxQT system. The system generates a task feedback data packet and transmits it to the multi-agent system MAS to provide a reference for scheduling optimization.

[0077] Step S5: Scheduling optimization and instruction issuance. The MAS module optimizes the transportation sequence and task resource allocation based on the feedback data, and issues new instructions to each ARM board to ensure efficient completion of the task.

[0078] Step S6: If an ARM board in the conveyor line detects an abnormality in the roller motor, the Linux QT system triggers an alarm and adjusts the scheduling task. The MAS module reallocates the scheduling plan to ensure the overall smooth operation of the system.

[0079] The Linux qt+ARM-based conveyor line control method described in the present invention solves the technical problem of distributed scheduling optimization of multi-agent systems in complex equipment management. The present invention realizes multi-agent collaborative scheduling, significantly improves the collaboration efficiency among multiple areas, reduces scheduling lag, and is convenient and fast to expand equipment and replace functional modules with strong adaptability.

Claims

1. A conveyor line control method based on Linux qt+ARM, characterized in that: The steps include: Step 1: Establish edge nodes and multiple control nodes, deploy Linux QT system in edge nodes, establish multi-agent module in Linux QT system, build multi-agent system MAS in multi-agent module, initialize Linux QT system and multi-agent system MAS, and all control nodes are registered in multi-agent module; Step 2: The Linux QT system generates an overall task based on the task requirements sent by the upper-level scheduling system WCS, and sends the overall task to the multi-agent system MAS; the multi-agent system MAS decomposes the overall task into regional tasks and generates scheduling instructions; Step 3: The MAS module distributes regional tasks and scheduling instructions to each control node, and each control node performs the control tasks of its own region; Step 4: The control node uploads the task execution data and status data generated in its own area to the Linux QT system. The Linux QT system generates task execution feedback data for each control node based on the uploaded task execution data. The Linux QT system transmits the task execution feedback data to the multi-agent system MAS; Step 5: The multi-agent system MAS optimizes the scheduling plan based on the task execution feedback data, generates the optimized scheduling instructions, and sends the optimized scheduling instructions to the control node; Step 6: The Linux QT system monitors the status data and task execution data of the control node, and issues a fault alarm when an abnormal situation is detected.

2. The Linux qt+ARM based conveyor line control method according to claim 1, characterized in that: The initialization of the Linux QT system includes configuring the basic communication interface and establishing a communication link with the upper scheduling system WCS; The architecture of the multi-agent system MAS consists of multiple agents, each of which corresponds to a control task module; Each control node is connected to the edge node through a data line, and completes registration in the multi-agent system MAS to obtain registration information, which includes a unique node ID; the multi-agent system MAS confirms the regional control task corresponding to each control node; The Linux QT system initializes the multi-agent system MAS, resets the status of each control node to the initial state, clears the task and device status, and checks and ensures that the real-time communication link between the multi-agent module and the control node is normal.

3. The Linux qt+ARM based conveyor line control method according to claim 1, characterized in that: When executing step 2, the specific steps include: Step 2-1: The Linux QT system receives task requirements from the upper-level scheduling system WCS, analyzes and breaks them down into specific operation plans, and generates task logic; Step 2-2: Define specific mission objectives in multiple areas based on mission logic; Step 2-3: Generate an overall task according to the task goal, and send the overall task to the multi-agent system MAS. After receiving the overall task, the multi-agent system MAS starts task decomposition and generation of scheduling instructions; Step 2-4: The multi-agent system MAS decomposes the overall task into regional tasks of each agent and generates corresponding scheduling instructions for each control node.

4. The Linux qt+ARM based conveyor line control method according to claim 1, characterized in that: The multi-agent system MAS sends regional tasks and scheduling instructions to each control node, and each control node performs the regional tasks of its own area, controls the sensors in the area to collect signals and execute equipment; During the execution process, each control node collects sensor data and task execution status data in real time to generate task execution data and status data; Each control node uploads task execution data and status data to the Linux QT system in real time. After receiving the task execution data, the Linux QT system analyzes and organizes the task execution feedback of each control node and generates a feedback data packet for optimization use by the MAS module. The Linux QT system passes the feedback data packet to the multi-agent system MAS, providing a basis for its subsequent scheduling optimization.

5. The Linux qt+ARM based conveyor line control method according to claim 4, characterized in that: When executing step 5, the specific steps include: Step 5-1: The multi-agent system MAS analyzes the feedback data packet to obtain the completion time of the tasks in each area; Step 5-2: Generate an optimized scheduling plan based on the completion time and the following weight formula: Where N is the number of regions, w i is the weight of region i, and its value is assigned according to the importance of the task, t i The time required to complete the task for area i; Step 5-3: Generate new scheduling instructions according to the optimized scheduling plan, and send the new scheduling instructions to each control node.

6. The Linux qt+ARM based conveyor line control method according to claim 2, characterized in that: The Linux QT system continuously monitors the uploaded data of the control node and detects abnormal data in real time. When abnormal data is detected, the Linux QT system triggers the alarm mechanism, generates alarm information, and uploads it to the upper-level scheduling system WCS.

7. The Linux qt+ARM based conveyor line control method according to claim 2, characterized in that: The edge node is an edge computing server or an edge computing device. The multi-agent system MAS regards each control node as an agent and establishes a control task module for each agent.

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