A conveyor line control method based on Linux qt + ARM

Abstract

The present invention discloses a conveyor line control method based on Linux qt + ARM, belonging to the technical field of edge computing. It includes establishing an edge node and multiple control nodes, deploying the Linux QT system in the edge node, establishing a multi-agent module in the Linux QT system, and constructing a multi-agent system MAS in the multi-agent module. It solves the technical problem of distributed scheduling optimization of multi-agent systems in complex device management. The present invention realizes multi-agent collaborative scheduling, significantly improves the collaboration efficiency among multiple regions, reduces scheduling lag, and is convenient and fast for expanding devices and replacing functional modules, with strong adaptability.

Description

Technical Field

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

[0002] The conveyor line is one of the key devices in the automation industry, used to efficiently and continuously transport materials from one place to another. In the process of wafer production in the semiconductor industry, the conveyor line is required to transport wafers to designated locations for subsequent processing. In an intelligent warehousing system, the conveyor line is required for feeding, discharging, and goods transportation. In an intelligent logistics system, the conveyor line is required to sort and transport to designated storage locations. With the continuous upgrading of the industry today, more and more production and manufacturing pursuits are intelligent, efficient, and automated. From production and manufacturing to packaging and warehousing, the process of material transportation is often involved, and the conveyor line plays an important role in it. It can be seen that its uses are extensive and penetrate various industries.

[0003] In traditional technologies, the automatic control and scheduling coordination of regional devices usually rely on a centralized monitoring system. Currently, most use control units such as PLCs to manage devices in different regions separately. The PLC, as a controller, needs to externally connect an 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 relatively high, and the prices of both the hardware part and the software part are relatively expensive. In addition, the installation and debugging of the PLC control system also require certain costs. The PLC control system is suitable for medium and small-scale 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 certain specific control tasks, the PLC control system may not meet the requirements. Moreover, the programming function and expansion ability of the PLC have certain limitations, the interface freedom is not high, and the centralized control structure in traditional technologies easily leads to scheduling lags and does not adapt to the collaborative requirements of multiple regions and multiple devices. The PLC control system cannot real-time sense and locate faults, and the recovery time is relatively long after the device fails, affecting the system efficiency. The PLC control system is also difficult to quickly add or replace devices, and significant adjustments are required for system expansion, with a relatively high time cost. 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 in the complex device management of a multi-agent system.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions:

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

[0007] Step 1: Establish edge nodes and multiple control nodes. Deploy the Linux QT system in the edge nodes, establish a multi-agent module in the Linux QT system, construct a multi-agent system MAS in the multi-agent module, initialize the Linux QT system and the multi-agent system MAS, and all control nodes are registered in the multi-agent module;

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

[0009] Step 3: The MAS module distributes regional tasks and scheduling instructions to each control node respectively, and each control node executes the control tasks in its respective area;

[0010] Step 4: The control node uploads the task execution data and status data generated within its own area to the Linux QT system, and the Linux QT system generates task execution feedback data for each control node according to the uploaded task execution data;

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

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

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

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

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

[0016] Each control node is connected to the edge node through a data cable, completes registration in the multi-agent system MAS, obtains registration information, and the registration information includes a unique node ID; the multi-agent system MAS confirms the regional control tasks 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 performing step 2, it specifically includes the following steps:

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

[0020] Step 2-2: Define specific task objectives within multiple regions according to the task logic;

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

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

[0023] Preferably, the multi-agent system MAS sends the regional tasks and scheduling instructions to each control node, and each control node executes the regional tasks in its respective area, and the sensors in the control area collect signals and execution devices;

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

[0025] Each control node uploads the 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 collates the task execution feedback of each control node, and generates a feedback data packet for the MAS module to optimize and use;

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

[0027] Preferably, when performing step 5, it specifically includes the following steps:

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

[0029] Step 5-2: According to the completion time, based on the following weight formula, generate an optimized scheduling plan:

[0030]

[0031] Among them, N is the number of regions, w i is the weight of region i, and the value is assigned according to the task importance, ti Time required to complete the task for area i;

[0032] Step 5-3: According to the optimized scheduling scheme, generate new scheduling instructions 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, detects abnormal data in real time. When abnormal data is detected, the Linux QT system triggers an alarm mechanism, generates an alarm message, and uploads it to the upper-layer scheduling system WCS.

[0034] Preferably, 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.

[0035] A conveyor line control method based on Linux qt + ARM according to 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 cooperation efficiency among multiple regions, reduces scheduling lag, and is convenient and fast for expanding equipment and replacing functional modules, with strong adaptability. Brief Description of the Drawings

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

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

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

[0039] Figure 4 Is the system architecture diagram of the present invention. Detailed Embodiments

[0040] Such as Figures 1 - 4 A conveyor line control method based on Linux qt + ARM shown includes the following steps:

[0041] Step 1: Establish an edge node and multiple control nodes, deploy the Linux QT system in the edge node, establish a multi-agent module in the Linux QT system, construct a multi-agent system MAS in the multi-agent module, initialize the Linux QT system and the multi-agent system MAS, and all control nodes are registered in the 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-layer scheduling system WCS;

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

[0044] Each control node accesses the edge node through a data line, completes registration in the multi-agent system MAS, obtains registration information, and the registration information contains a unique node ID; the multi-agent system MAS confirms the area 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 creates a control task module for each agent.

[0047] Step 2: The Linux QT system generates an overall task according to the task requirements sent by the upper-layer scheduling system WCS and sends the overall task to the multi-agent system MAS; the multi-agent system MAS decomposes the overall task to generate area tasks and generates scheduling instructions;

[0048] Specifically, it includes the following steps:

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

[0050] Step 2-2: Define specific task objectives within multiple areas according to the task logic;

[0051] Step 2-3: Generate an overall task according to the task objectives, send the overall task to the multi-agent system MAS, and 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 area tasks for each agent and generates corresponding scheduling instructions for each control node.

[0053] Step 3: The MAS module distributes area tasks and scheduling instructions to each control node respectively, and each control node executes its respective area control task;

[0054] Step 4: The control node uploads the task execution data and status data generated within its own area to the Linux QT system, and the Linux QT system generates task execution feedback data for each control node according to the uploaded task execution data;

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

[0056] The multi-agent system MAS issues the regional tasks and scheduling instructions to each control node. Each control node executes the regional tasks in its respective area, and the sensors in the control area collect signals and execution devices;

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

[0058] Each control node uploads the 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 collates the task execution feedback of each control node, and generates a feedback data packet for the MAS module to optimize and use;

[0059] The Linux QT system transfers 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 scheme based on the task execution feedback data, generates an optimized scheduling instruction, and issues the optimized scheduling instruction to the control node;

[0061] Specifically, it includes the following steps:

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

[0063] Step 5-2: According to the completion time, based on the following weight formula, generate an optimized scheduling scheme:

[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 is the time required for region i to complete the task;

[0066] In this embodiment, the value of w i is generated by the Linux QT system. In step 2-2, the Linux QT system can define the specific task objectives in multiple regions according to the task logic, and then divide the priorities of the task objectives in each region, and formulate the weights 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 nodes, and issues a fault alarm after detecting an abnormal situation.

[0069] The Linux QT system continuously monitors the uploaded data of the control nodes, 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-layer scheduling system WCS.

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

[0071] In the process of wafer production in the semiconductor industry, the control objects in 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 schedule and transport goods.

[0072] The scenario implementation process is as follows:

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

[0074] Step S2: Task requirement generation and distribution. The upper-layer WCS system generates a transportation task, such as "transport the goods in area A to area B". The Linux QT system receives the requirement, disassembles it to generate an overall task, and sends the task to the MAS module. The MAS module distributes it to the relevant control nodes according to the task decomposition scheduling instructions.

[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 execute the goods 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 Linux QT 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 distribution. The MAS module optimizes the transportation order and task resource allocation based on the feedback data, and issues new instructions to each ARM board to ensure the efficient completion of tasks.

[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 scheme to ensure the overall stable operation of the system.

[0079] A conveyor line control method based on Linux qt + ARM according to the present invention solves the technical problem of distributed scheduling optimization of a multi-agent system in complex device management. The present invention realizes multi-agent collaborative scheduling, significantly improves the cooperation efficiency between multiple regions, reduces scheduling lag, and is convenient and fast to expand devices and replace functional modules, with strong adaptability.

Claims

1. A control method for a conveyor line based on Linux qt + ARM, characterized in that: It includes the following steps: Step 1: Establish edge nodes and multiple control nodes, deploy the Linux QT system in the edge nodes, establish a multi-agent module in the Linux QT system, build a multi-agent system MAS in the multi-agent module, initialize the Linux QT system and the multi-agent system MAS, and all control nodes are registered in the multi-agent module; Step 2: The Linux QT system generates an overall task according to the task requirements sent by the upper-layer scheduling system WCS, and sends the overall task to the multi-agent system MAS; the multi-agent system MAS decomposes the overall task to generate regional tasks and generates scheduling instructions; Step 3: The MAS module distributes the regional tasks and scheduling instructions to each control node respectively, and each control node executes the control tasks in its respective area; Step 4: The control node uploads the task execution data and status data generated within its own area to the Linux QT system, and the Linux QT system generates task execution feedback data for each control node according to the uploaded task execution data; The Linux QT system transfers the task execution feedback data to the multi-agent system MAS; Step 5: The multi-agent system MAS optimizes the scheduling scheme based on the task execution feedback data, generates optimized scheduling instructions, and issues the optimized scheduling instructions to the control nodes; Step 6: The Linux QT system monitors the status data and task execution data of the control nodes, and issues a fault alarm after detecting an abnormal situation.

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

3. A conveyor line control method based on Linux qt + ARM according to claim 1, characterized in that: When performing Step 2, it specifically includes the following steps: Step 2-1: The Linux QT system receives task requirements from the upper-layer scheduling system WCS, analyzes and disassembles them into specific operation plans, and generates task logic; Step 2-2: Define specific task objectives in multiple regions according to the task logic; Step 2-3: Generate an overall task according to the task objectives, send the overall task to the multi-agent system MAS, and 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. A conveyor line control method based on Linux qt + ARM according to claim 1, characterized in that: The multi-agent system MAS distributes regional tasks and scheduling instructions to each control node. Each control node executes the regional tasks in its respective area, and the sensors within the control area collect signals and the execution devices. During the execution process, each control node collects sensor data and status data of task execution in real time, and generates task execution data and status data. Each control node uploads the 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 the MAS module to optimize and use. The Linux QT system transfers the feedback data packet to the multi-agent system MAS, providing a basis for its subsequent scheduling optimization.

5. A conveyor line control method based on Linux qt + ARM according to claim 4, characterized in that: When performing step 5, it specifically includes the following steps: Step 5-1: The multi-agent system MAS analyzes the feedback data packet to obtain the completion time of each regional task. Step 5-2: According to the completion time, based on the following weight formula, generate an optimized scheduling plan: Among them, N is the number of regions, and w i is the weight of region i, and its value is assigned according to the importance of the task. t i is the time required for region i to complete the task; Step 5-3: According to the optimized scheduling plan, generate new scheduling instructions and distribute the new scheduling instructions to each control node.

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

7. The control method of a conveyor line based on Linux qt + ARM according to claim 2, wherein: 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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