Furnace tube automatic transmission control method and system based on multistage feedback array

Abstract

The invention relates to the technical field of furnace tube conveying control, in particular to a furnace tube automatic conveying control method and system based on a multistage feedback array. The method comprises the following steps: acquiring a furnace tube transmission task including task emergency degree and type; allocating different priorities to the furnace tube transmission tasks according to the emergency degrees and types of the furnace tube transmission tasks; executing a furnace tube transmission task according to the priority; wherein the priority of the task is dynamically adjusted by using an MLFQ control algorithm; scheduling according to the priority of the task by using an FCFS scheduling algorithm; and the RP scheduling algorithm is utilized to carry out time slice rotation, so that each task is ensured to be executed. Based on the improved control system, the response capability of the control system to tasks with different priorities is improved, the resource allocation is optimized, the waiting time is shortened, and the overall efficiency of a production line is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of furnace tube transmission control, and in particular to a furnace tube automatic transmission control method and system based on a multi-level feedback array. Background Art

[0002] In industrial production, the transmission and control of furnace tubes are crucial to ensure production efficiency and product quality. In the original technology, most of them rely on manual operation or simple mechanized devices, which are not only inefficient, but also prone to insufficient transmission accuracy due to human errors, and even cause safety accidents. Therefore, it is of great significance to develop a control system that can automatically, accurately and safely complete the transmission of furnace tubes. Based on this, the present invention proposes an automatic transmission system based on a multi-level feedback queue (MLFQ) algorithm, which aims to improve the automatic transmission efficiency of wafers in furnace tubes, reduce manual intervention, and ensure the safety and quality of wafers. Summary of the invention

[0003] In order to solve the problems of low efficiency, poor safety and low control accuracy of furnace tube transmission in the current technology, the present invention provides a furnace tube automatic transmission control method and system based on a multi-level feedback array. By integrating advanced sensing technology, control technology, communication technology, etc., the automatic and accurate transmission of furnace tubes is realized, and rich monitoring and alarm functions are provided to ensure the smooth progress of the production process.

[0004] In the first aspect, the present invention provides a furnace tube automatic transmission control system based on a multi-level feedback array, which adopts the following technical solution: A furnace tube automatic transmission control method based on a multi-level feedback array, comprising: Obtain furnace pipe transfer tasks, including task urgency and type; Assign different priorities to the furnace pipe transfer tasks according to their urgency and type; The furnace pipe transmission task is executed according to the priority. The MLFQ control algorithm is used to dynamically adjust the priority of the task. The FCFS scheduling algorithm is used to schedule the task according to its priority. The RP scheduling algorithm is used to rotate the time slice to ensure that each task is executed.

[0005] Further, the obtaining of the furnace tube transmission task includes obtaining the furnace tube transmission task through a system interface, parsing the furnace tube transmission task, and identifying the urgency and type of the task by extracting fields and codes in the instruction.

[0006] Furthermore, different priorities are assigned to the furnace tube transmission tasks according to the urgency and type of the furnace tube transmission tasks, including setting multiple priority arrays Q0, Q1, Q2...Qn, where Q0 has the highest priority and Qn has the lowest priority, and setting different time slice lengths for each array, the higher the priority array, the shorter the time slice, when a new furnace tube transmission task arrives, it is placed in the highest priority queue Q0, if the task in Q0 is being executed or is full, it is placed in the second highest priority queue or a lower priority queue according to the urgency of the task.

[0007] Furthermore, the MLFQ control algorithm is used to dynamically adjust the priority of the task, including removing the task from the current queue when the task is completed within the time slice; retaining the priority level and waiting for the next scheduling to continue execution when the task is not completed within the time slice but the CPU is actively released; and lowering the task to the next priority queue when the task is not completed within the time slice and the CPU is not actively released.

[0008] Furthermore, the FCFS scheduling algorithm is used to schedule tasks according to their priorities, including selecting the task that first enters the queue from the ready queue based on the first-come-first-served principle and assigning a processor to it, until the task is processed and the processor is abandoned after an event occurs and the task is blocked.

[0009] Furthermore, the RP scheduling algorithm is used to rotate time slices to ensure that each task is executed, including allocating a fixed time slice q to each task process. When a process is not completed within the time slice q, the CPU will be preempted and placed at the end of the ready queue waiting for the next scheduling.

[0010] In a second aspect, a furnace tube automatic transmission control system based on a multi-level feedback array comprises: A sensor module, a controller module, an actuator module and a human-computer interaction module, wherein the sensor module is used to monitor the position and speed parameters of the furnace tube in real time; the controller module performs data analysis and issues control instructions based on the parameters collected by the sensor module; the actuator module executes the conveying action of the furnace tube according to the control instructions; and the human-computer interaction module is used to set system parameters, display status and accept user operation instructions.

[0011] Furthermore, the sensor module uses sensors to monitor parameters such as the position and speed of the furnace tube and the load of the conveyor belt in real time, and the sensor data is transmitted to the controller module via wires.

[0012] Furthermore, the controller module receives the data transmitted by the sensor module and performs calculations and analyses according to a preset algorithm. The controller adopts a PLC programmable logic controller to receive data from the sensor. According to the analysis results, the controller module issues control instructions to adjust the speed and direction of the conveyor belt. At the same time, the controller module is also responsible for monitoring the operating status of the system. Once an abnormality is found, an alarm signal is immediately issued and corresponding protective measures are taken.

[0013] Furthermore, the actuator module includes a servo motor, a reducer and a conveyor belt, wherein the servo motor adjusts the speed and direction according to the control instructions issued by the controller, thereby driving the conveyor belt to complete the conveying movement of the furnace tube; the reducer is used to reduce the speed of the motor; the conveyor belt carries the wafer and transports it in the furnace tube.

[0015] Furthermore, the human-computer interaction module adopts a touch screen design, and the system parameters are set, the system status is checked and user instruction information is received through the touch screen.

[0016] In summary, the present invention has the following beneficial technical effects: The present invention is based on an improved control system, which improves the control system's ability to respond to tasks of different priorities, optimizes resource allocation, reduces waiting time, and improves the overall efficiency of the production line. The system can automatically adjust the scheduling strategy according to changes in production demand, adapt to different production tasks, and adopt the application of a multi-level feedback queue algorithm, so that the system can respond to changes in production demand more quickly, improve the response speed of the production line, greatly improve the transmission efficiency and the utilization rate of the transmission manipulator, and the application of the intelligent scheduling system reduces the need for manual intervention and reduces maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a schematic diagram of a multi-stage feedback queue algorithm of Embodiment 1 of the present invention; Figure 2 Schematic diagram of the queue management optimization process of embodiment 1 of the present invention; Figure 3 is a schematic diagram of dynamic adjustment of furnace tube transmission priority according to Embodiment 1 of the present invention; Figure 4 It is a schematic diagram of the automatic conveying control flow of the furnace tube in Example 1 of the present invention. DETAILED DESCRIPTION

[0018] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0019] Example 1 Reference Figure 1 , a furnace tube automatic transmission control method based on a multi-level feedback array of this embodiment includes: Obtain furnace pipe transfer tasks, including task urgency and type; Assign different priorities to the furnace pipe transfer tasks according to their urgency and type; The furnace pipe transmission task is executed according to the priority. The MLFQ control algorithm is used to dynamically adjust the priority of the task. The FCFS scheduling algorithm is used to schedule the task according to its priority. The RP scheduling algorithm is used to rotate the time slice to ensure that each task is executed.

[0020] Furthermore, the obtaining of the furnace tube transmission task includes obtaining the furnace tube transmission task from an integrated system interface, wherein the furnace tube automatic transmission control system is integrated with the enterprise's production management system, and data interaction is achieved through the system interface. The production management system generates the furnace tube transmission task according to the overall production plan and process, and encapsulates information such as the task urgency and type in the task instruction in a predetermined data format, and sends it to the furnace tube automatic transmission control system.

[0021] Real-time status monitoring: Various sensors, such as position sensors, pressure sensors, temperature sensors, etc., are installed at various key locations and equipment of furnace tube transmission. These sensors monitor the operating status and physical parameters of furnace tubes and related equipment in real time. By analyzing these real-time monitoring data, the system can determine whether a new furnace tube transmission task needs to be generated, as well as the urgency and type of the task.

[0022] Data processing and analysis: The data collected by the sensor is transmitted to the data processing center, and the data is processed and analyzed using data analysis algorithms and models. For example, based on the temperature change rate in the furnace tube, abnormal pressure conditions, and the position of the product in the furnace tube, it is determined whether an emergency occurs. For example, if the temperature in the furnace tube is too high, the product may be damaged. At this time, the generated transmission task has a high degree of urgency, and the task type may be to urgently transfer the product in the furnace tube.

[0023] Furthermore, different priorities are assigned to the furnace tube transmission tasks according to the urgency and type of the furnace tube transmission tasks, including setting multiple priority arrays Q0, Q1, Q2...Qn, where Q0 has the highest priority and Qn has the lowest priority, and setting different time slice lengths for each array, the higher the priority array, the shorter the time slice, when a new furnace tube transmission task arrives, it is placed in the highest priority queue Q0, if the task in Q0 is being executed or is full, it is placed in the second highest priority queue or a lower priority queue according to the urgency of the task.

[0024] Furthermore, the MLFQ control algorithm is used to dynamically adjust the priority of the task, including removing the task from the current queue when the task is completed within the time slice; retaining the priority level and waiting for the next scheduling to continue execution when the task is not completed within the time slice but the CPU is actively released; and lowering the task to the next priority queue when the task is not completed within the time slice and the CPU is not actively released.

[0025] Furthermore, the FCFS scheduling algorithm is used to schedule tasks according to their priorities, including selecting the task that first enters the queue from the ready queue based on the first-come-first-served principle and assigning a processor to it, until the task is processed and the processor is abandoned after an event occurs and the task is blocked.

[0026] Furthermore, the RP scheduling algorithm is used to rotate time slices to ensure that each task is executed, including allocating a fixed time slice q to each task process. When a process is not completed within the time slice q, the CPU will be preempted and placed at the end of the ready queue waiting for the next scheduling.

[0027] like Figure 1 As shown in the figure, MLFQ (Multilevel Feedback Queue) control algorithm, FCFS (First In First Out) scheduling algorithm, and RR (Round Robin) scheduling algorithm are common process scheduling algorithms in operating systems, and they each have different parameters and principles.

[0028] Among them, regarding the MLFQ control algorithm, First, set up multiple priority queues: MLFQ contains multiple independent queues, each with a different priority.

[0029] Time slices S1, S2, S3, ..., Sn: Queues of different priorities are allocated different time slices. Generally, queues with higher priorities are allocated shorter time slices.

[0030] Process classification and priority adjustment: Newly created processes are placed in the highest priority queue and assigned an initial time slice. When a process completes execution in the current time slice or is blocked because it is waiting for I / O operations, it will be moved to the next lower priority queue. If a process is still running at the end of the current time slice, it will be temporarily stopped, enter the end of the ready queue, and restart in the next time slice, but with a lower priority.

[0031] Optimizing Turnaround Time vs. Response Time: MLFQ attempts to optimize turnaround time by running shorter jobs first and responding quickly to requests from interactive users via a high-priority queue.

[0032] Avoid starvation: By dynamically adjusting priorities and queue migration, even long processes will not occupy the CPU all the time, ensuring the responsiveness of the system.

[0033] Using the FCFS scheduling algorithm, The FCFS scheduling algorithm has no specific parameters: the FCFS scheduling algorithm mainly schedules processes based on the order in which they arrive at the system.

[0034] First come first served: Each scheduling selects a process that enters the queue first from the ready queue, allocates a processor to it, and puts it into operation. The process runs until it is completed or blocked by an event before giving up the processor.

[0035] Advantages and disadvantages: FCFS scheduling algorithm is simple and fair, but it is not conducive to short jobs, because short jobs must wait for the previous long job to be completed before they can be executed. In addition, FCFS may cause starvation.

[0036] Using the RR scheduling algorithm: Set time slice q: The RR scheduling algorithm assigns a fixed time slice q to each process. If the process is not completed within the time slice, Based on time slice round robin: In the round robin scheduling algorithm, the size of the time slice has a great impact on the performance of the system. If the time slice is small, it will be beneficial for short jobs, which can be completed within this time slice. However, if the time slice is too small, it means frequent process switching, which increases the system overhead. If the time slice is too long, the RR scheduling algorithm will degenerate into the FCFS scheduling algorithm.

[0037] Fairness: The RR scheduling algorithm ensures that each process has the opportunity to obtain CPU resources through time slice rotation, thus achieving fairness.

[0038] In summary, MLFQ, FCFS, and RR are three different process scheduling algorithms, each with different parameters and principles. MLFQ optimizes turnaround time and response time through multiple priority queues and dynamic priority adjustment; FCFS schedules processes according to the order in which they arrive at the system; and RR ensures that each process has a chance to obtain CPU resources through time slice rotation. In actual applications, the appropriate scheduling algorithm can be selected according to the specific needs and load conditions of the system.

[0039] The Multilevel Feedback Queue (MLFQ) scheduling algorithm is a classic CPU scheduling algorithm that combines priority scheduling and round-robin scheduling to dynamically adjust the priority of processes. The following is a detailed formula implementation of the MLFQ control algorithm, including steps such as initialization, process insertion, process selection, and priority adjustment.

[0040] When a process enters the system, it is placed in the highest priority queue (for example, the highest priority is P=0), which has a corresponding time slice Q_0.

[0041] If a process does not complete execution within the time slice corresponding to its current priority queue, its priority will be reduced (for example, from P=0 to P=1), and then placed in the next priority queue, which may have a longer time slice (such as Q_1>Q_0).

[0042] After a certain period (such as after all queues have been scheduled once), the system may increase the priority of some processes that have been waiting for too long to avoid starvation.

[0043] For example, if the time slice of the highest priority queue is Q_0 = 8ms, a process that fails to complete execution within this time slice will be demoted to the next level queue, where the time slice may be Q_1 = 16ms. This rule is mainly implemented through queue operations and conditional judgment statements in the program, rather than a simple mathematical formula.

[0044] Data Structure 1. Queues: Each queue has a different priority. The higher the priority, the smaller the queue number.

[0045] 2. Time Quantum: The time quantum size of each queue is different. The higher the priority, the smaller the time quantum.

[0046] 3. Process: Each process has attributes such as current queue number, time slice remaining value, priority history record, etc.

[0047] initialization class Process: def__init__ (self,pid,initial_priority): self.pid = pid self.current_queue=initial_priority-1 #The queue number starts from 0 and the priority starts from 1 self.time_quantum_remaining=time_quantum[initial_priority -1] self.priority_history=[initial_priority] class MLFQScheduler: def__init__(self,num_queues,time_quantums): self.num_queues = num_queues self.time_quantum=time_quantums #List, storing the time slice size of each queue self.queues = [[]for in range(num_queues)] # Initialize the queue self.current_time=0 #global time Parameter settings num_queues=4 #Assume there are 4 priority queues time_quantums = [5, 10, 20, 40] # The time slice size of each queue scheduler =MLFQScheduler(num_queues,time_quantums) Process Insertion def add_process(scheduler,pid,initial_priority): process = Process(pid,initial_priority) scheduler.queues[process.current_queue].append(process) Process Selection def select_process(scheduler): for i in range(scheduler.num_queues): if scheduler.queues[i]: process = scheduler.queues[i].pop(0) process.time_quantum_remaining = scheduler.time_quantum[i] return process return None #All queues are empty Time slice exhaustion processing def process_time_quantum_expired(scheduler,process): if process.time_quantum _remaining<= 0: # The process is not completed, lower the priority and reinsert it into the queue new_priority = max(process.priority_ history[-1]-1,1) # The priority is not less than 1 process.current_queue =new_priority-1 process.time_quantum_remaining=scheduler.time_quantum[process.currentqueue] process.priority_history.append(new_priority) scheduler.queues[process.current queue].append(process) return False # indicates that the process is not completed else: return True # indicates that the process is completed within the time slice Main dispatch loop def schedule(scheduler): while True: process =select_process(scheduler) if not process: break # No process can be scheduled #Simulate the process execution for one time unit scheduler.current_time +=1 process.time_quantum_remaining-=1 # Check if the process is complete if process_time_quantum_expired(scheduler, process): print(f"Process{process.pid}completed at time{scheduler.current _time}") else: # The process is not completed, return to the queue and wait for the next round of scheduling scheduler.queues[process.current_queue].append(process) # Initialize the scheduler scheduler = MLFQScheduler(num_queues,time_quantums) # Add process Initial priority 1 add process(scheduler 1,1)#Process 1, # Process 2, initial priority 2C\ add process(scheduler, 2,2) add process(scheduler,3,3)#Process 3, initial priority 3 # Start scheduling schedule(scheduler) In some MLFO implementations, the system may dynamically adjust the priorities of various queues based on the current load conditions. For example, when the system load is high, the priority of processes in lower priority queues may be increased to better balance system performance.

[0048] The specific formula logic may involve the estimation of system load and the dynamic adjustment of each queue priority. For example, the following formula can be used to estimate the average load (load_avg) of the system: Load_avg = 5960x previous load_avg + 160x current ready queue length Among them, "previous load_avg" is the average load value calculated last time, and "current ready queue length" is the number of threads running or ready to run at the current moment (excluding idle threads). This formula smoothes the load changes of the system by weighted average.

[0049] Another formula related to priority adjustment may involve the thread's "recent_cpu" value, which represents the amount of CPU time the thread has received recently. This value can be updated dynamically over time and as the thread performs. For example, the following formula can be used to update the thread's "recent_cpu" value: Recent_cpu=2×load_avgload_avg+1×recent_cpu+nice In a certain case, "nice" is the thread's "nice" value, which indicates how friendly the thread is to CPU resources (the value range is -20 to 20). By adjusting the "nice" value, you can affect the thread's priority and CPU allocation. Finally, the thread priority can be calculated using the following formula: priority=PRI_MAX-recent_cpu4-2×nice Among them, "PRI_MAX" is the highest priority value (for example, 63), "recent_cpu" is the recent CPU time of the thread, and "nice" is the "nice" value of the thread. This formula takes into account factors such as the CPU usage, friendliness, and system load of the thread to dynamically adjust the priority of the thread.

[0050] MLFQ does not assign a fixed priority to each process, but adjusts its priority based on observed behavior. The priority calculation formula usually involves the following variables: priority: The priority of the process, usually an integer between a minimum value (such as PRIMIN) and a maximum value (such as PRI MAX).

[0051] recent_cpu: The amount of CPU time the process has recently received, used to measure the process's use of the CPU.

[0052] nice: The "nice" value of the process is an adjustable parameter that affects the priority of the process. A higher "nice" value will lower the priority of the process.

[0053] The specific calculation formula may be as follows: priority=PRI_MAX-(recent_cpu / 4)-(2*nice) The value of recent_cpu is updated once a second and is usually calculated as follows: recent_cpu=(2*load_avg) / (2*load_avg + 1)*recent_cpu + nice And load_avg is the average number of processes ready to run in the past minute, and its calculation formula is: Load_avg=(59 / 60)*load_avg+(1 / 60)*ready_threads Where ready_threads is the number of threads running or ready to run at the time of the update (excluding idle threads), That is, the sum of the length of the ready queue and the number of currently running threads.

[0054] Time slices S1, S2, S3, ... Sn: Different priority queues are assigned different time slices. Usually, queues with higher priority have shorter time slices. principle: Process classification and priority adjustment: Newly created processes are placed in the highest priority queue and assigned an initial time slice. If a process is still in the queue at the end of the current time slice, it will be moved to the next lower priority queue. If a thread is running, it will be temporarily stopped, enter the tail of the ready queue, and restart in the next time slice, but with a lower priority.

[0055] Optimizing Turnaround Time vs. Response Time: MLFQ attempts to optimize turnaround time by running shorter jobs first and responding quickly to interactive users through high-priority queues. User's request.

[0056] Avoid starvation: By dynamically adjusting priorities and queue migration, even long processes will not occupy the CPU all the time, ensuring the responsiveness of the system.

[0057] FCFS Scheduling Algorithm parameter: No specific parameters: The FCFS scheduling algorithm is mainly based on the order in which processes arrive at the system.

[0058] principle: First come first served: Each time the process is scheduled, it is selected from the ready queue and the processor is allocated to it to run. The process will not give up the processor until it is completed or blocked by an event.

[0059] Advantages and disadvantages: FCFS scheduling algorithm is simple and fair, but it is not conducive to short jobs, because short jobs must wait for the previous long job to be completed before they can be executed. In addition, FCFS may cause starvation.

[0060] RR Scheduling Algorithm parameter: Time slice 9: The RR scheduling algorithm assigns a fixed time slice q to each process. If a process is not completed within the time slice, it will be preempted from the CPU and placed in the ready queue. Wait for the next scheduling at the end.

[0061] principle: Time slice round robin: In the round robin scheduling algorithm, the size of the time slice has a great impact on the performance of the system. If the time slice is small, it will be beneficial for short jobs, which can be completed within this time slice. However, if the time slice is too small, it means frequent process switching, which increases the system overhead. If the time slice is too long, the RR scheduling algorithm will fall back to the FCFS scheduling algorithm.

[0062] Fairness: The RR scheduling algorithm ensures that each process has the opportunity to obtain CPU resources through time slice rotation, thus achieving fairness.

[0063] In summary, MLFQ, FCFS, and RR are three different process scheduling algorithms, each with different parameters and principles. FCFS schedules processes according to the order in which they arrive at the system; RR ensures that each process is scheduled correctly by rotating the time slice. Each process has the opportunity to obtain CPU resources. In practical applications, the appropriate scheduling algorithm can be selected according to the specific needs and load conditions of the system.

[0064] like Figure 2 As shown, Figure 2 This paper presents an optimized queue management process solution, which aims to improve the efficiency of queue operation. The process is summarized as follows: First, the system monitors the running status of the queue to determine whether preemption is likely to occur. If there is an idle tail in the queue and the remaining running time of the process is greater than 0, it attempts to preempt to improve resource utilization.

[0065] During the queue operation, the system will continuously monitor the remaining running time of each process. When the remaining running time is less than or equal to the preset time slice length, the system will evaluate whether to end the running of the process. If the tail time of the process no longer increases and the remaining running time reaches 0, the process will be popped out and stored at the end of the completed queue.

[0066] In addition, the system will dynamically adjust the queue status according to the remaining run time of the process and the length of the time slice. If the remaining run time is less than the length of the time slice, the process will be ejected and stored in the completed queue. If the remaining run time is greater than the time slice, the process continues to run in the queue.

[0067] The entire process achieves efficient resource utilization and queue priority processing by dynamically adjusting the queue status. Reasonable preemption and termination strategies ensure efficient operation of the queue and meet the priority and time requirements of different tasks.

[0068] like Figure 3 As shown, Figure 3 It shows how the priority of the furnace tube is dynamically adjusted according to the real-time status during the transmission process. The system monitors the real-time status of the furnace tube (such as waiting time, production progress, etc.) and compares it with the preset priority adjustment rules to dynamically adjust the priority of the furnace tube. For example, a furnace tube with a long waiting time can be promoted to a higher priority queue to reduce the waiting time.

[0069] like Figure 4 As shown, Figure 4 The work flow of intelligent scheduling algorithm in the furnace tube automatic transmission control system is described, including key steps such as data collection, algorithm calculation, scheduling decision and execution feedback.

[0070] Description: The system collects the real-time status data and production demand data of the furnace tubes, and uses the MLFO algorithm to calculate and obtain the optimal scheduling decision. Then, the system passes the scheduling decision to the execution module to control the transmission and positioning of the furnace tubes. At the same time, the system collects the execution results through the monitoring and feedback module for algorithm optimization and learning.

[0071] Example 2 This embodiment provides a furnace tube automatic transmission control system based on a multi-level feedback array, including: The sensor module, the controller module, the actuator module and the human-computer interaction module, wherein the sensor module is used to monitor the position and speed parameters of the furnace tube in real time; the controller module performs data analysis and issues control instructions according to the parameters collected by the sensor module; the actuator module executes the conveying action of the furnace tube according to the control instructions; the human-computer interaction module is used to set system parameters, display status and accept user operation instructions. The sensor module uses sensors to monitor the position, speed and load of the furnace tube and other parameters of the conveyor belt in real time, and the sensor data is transmitted to the controller module via wire. The controller module receives the data transmitted by the sensor module and calculates and analyzes according to the preset algorithm, wherein the controller uses a PLC programmable logic controller to receive data from the sensor. According to the analysis results, the controller module issues control instructions to adjust the speed and direction of the conveyor belt. At the same time, the controller module is also responsible for monitoring the operating status of the system. Once an abnormality is found, an alarm signal is immediately issued and corresponding protective measures are taken. The actuator module includes a servo motor, a reducer and a conveyor belt, wherein the servo motor adjusts the speed and direction according to the control instructions issued by the controller, thereby driving the conveyor belt to complete the conveying action of the furnace tube; the reducer is used to reduce the speed of the motor; the conveyor belt carries the wafer and transports it in the furnace tube. The human-computer interaction module adopts a touch screen design, through which system parameters are set, system status is checked and user instruction information is received.

[0072] Specifically, the following components need to be integrated in the overall technical solution: The sensor module, controller module, actuator module and human-computer interaction module are used to monitor the position and speed parameters of the furnace tube in real time; the controller module performs data analysis and issues control instructions based on the parameters collected by the sensor module; the actuator module performs the conveying action of the furnace tube according to the control instructions; the human-computer interaction module is used to set system parameters, display status and accept user operation instructions, as follows: Sensors are used to monitor parameters such as the position and speed of the furnace tube and the load of the conveyor belt in real time, and the sensor data is transmitted to the controller module via wired transmission.

[0073] Position sensor: used to detect the position of the furnace tube in real time.

[0074] Speed ​​sensor: used to measure the moving speed of the furnace tube.

[0075] Load sensor: used to detect the load on the conveyor belt.

[0076] The controller module selects a PLC programmable logic controller to accept data from sensors for processing and control logic.

[0077] Connection method: Wired connection, RS-485, RS-232, CAN bus protocol: suitable for long-distance transmission, ensuring stability and anti-interference ability. The sensor transmits data to the controller through these buses.

[0078] Analog signals: Position, speed and load sensors can transmit their measured values ​​to the controller via analog signals.

[0079] Parameter acquisition: Real-time position data obtained from a position sensor, usually expressed as the distance of coordinates relative to a reference point.

[0080] Speed ​​parameter: Real-time speed data obtained from the speed sensor, usually expressed in meters, seconds (m / s) or rotation speed (RPM).

[0081] Load parameter: Real-time load data obtained from the load sensor, usually expressed in kg or N.

[0082] Data processing: The controller module processes the received data (data filtering and calibration, status detection and alarm) Alarm logic: implements outlier detection and triggers an alarm when sensor data exceeds a preset value range.

[0083] Control instructions: Based on data analysis, the controller module issues instructions to adjust the conveyor belt speed and direction.

[0084] Monitor system status, use historical data and sensor input to make status judgments, and issue alarms and protective measures when necessary.

[0085] The actuator module is mainly divided into the following parts: Servo motors: The motors provide precise speed and position control, making them ideal for tasks such as automated furnace tube transfer.

[0086] Reducer: Used to reduce the speed of the motor while providing higher torque to ensure that it can bear the load of the transmission task.

[0087] Conveyor belt: carries the wafers and transports them inside the furnace tube. Usually made of materials with high heat resistance and low friction coefficient.

[0088] The human-computer interaction module adopts a touch screen design, through which system parameters are set, system status is checked and user instruction information is received.

[0089] The functions are as follows: user interface Status display: Real-time display of the furnace tube's working status, temperature, speed, load and other key parameters to help users monitor system operation.

[0090] Parameter setting: allows users to input and adjust system parameters, such as conveying speed, heating temperature, running time, etc., to meet different production needs.

[0091] Control functions Manual control: Users can manually start, stop or adjust the conveyor belt operation and perform necessary operations through the interface.

[0092] Automatic mode: The system can run automatically according to the preset program. The user only needs to check the status to reduce manual intervention.

[0093] Alarm and notification Abnormal alarm: When the system detects an abnormal situation (such as overtemperature, abnormal load, etc.), an alarm will be issued in time to ensure safety.

[0094] Maintenance reminder: Regularly remind users to perform equipment maintenance and inspection to ensure stable system operation.

[0095] composition Display devices: Touch screen: Common HIMI devices, users can operate and set through the touch screen, which is intuitive and easy to use.

[0096] LED indicator: used to display the working status of the equipment (such as running, stopping, fault, etc.).

[0097] Controller PLC (Programmable Logic Controller): As the core control unit, it is responsible for accepting user input and executing corresponding control instructions.

[0098] Communication interface: supports connection with other devices or systems (such as host computers, cloud platforms) to achieve data sharing and remote monitoring.

[0099] Serial interface: used for data exchange with other modules such as sensors and actuators.

[0100] User Experience Friendly interface design: The concise and clear interface layout allows users to quickly find the required functions.

[0101] Real-time feedback: users.

[0102] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A furnace tube automatic transmission control method based on a multi-level feedback array, characterized in that: include: Obtain furnace pipe transfer tasks, including task urgency and type; Assign different priorities to the furnace pipe transfer tasks according to their urgency and type; The furnace pipe transmission task is executed according to the priority. The MLFQ control algorithm is used to dynamically adjust the priority of the task. The FCFS scheduling algorithm is used to schedule the task according to its priority. The RP scheduling algorithm is used to rotate the time slice to ensure that each task is executed.

2. The method for automatic furnace tube transmission control based on a multi-level feedback array according to claim 1, characterized in that: The obtaining of the furnace tube transmission task includes obtaining the furnace tube transmission task through a system interface, parsing the furnace tube transmission task, and identifying the urgency and type of the task by extracting fields and codes in the instruction.

3. The method for automatic furnace tube transmission control based on a multi-level feedback array according to claim 1, characterized in that: The method allocates different priorities to the furnace tube transmission tasks according to the urgency and type of the furnace tube transmission tasks, including setting multiple priority arrays Q0, Q1, Q2...Qn, wherein Q0 has the highest priority and Qn has the lowest priority, and setting different time slice lengths for each array, the higher the priority array, the shorter the time slice, when a new furnace tube transmission task arrives, it is placed in the highest priority queue Q0, if the task in Q0 is being executed or is full, it is placed in the second highest priority queue or a lower priority queue according to the urgency of the task.

4. The method for automatic furnace tube transmission control based on a multi-level feedback array according to claim 1, characterized in that: The MLFQ control algorithm is used to dynamically adjust the priority of the task, including removing the task from the current queue when it is completed within the time slice; retaining the priority when the task is not completed within the time slice but actively releases the CPU, and waiting for the next scheduling to continue execution; when the task is not completed within the time slice and the CPU is not actively released, it is lowered to the queue of the next priority.

5. The method for automatic furnace tube transmission control based on a multi-level feedback array according to claim 1, characterized in that: The FCFS scheduling algorithm is used to schedule tasks according to their priorities, including selecting the first task to enter the queue from the ready queue based on the first-come-first-served principle and assigning a processor to it, until the task is processed and the processor is abandoned after the task is completed or an event occurs and the task is blocked.

6. The method for controlling the automatic transmission of furnace tubes based on a multi-level feedback array according to claim 1, characterized in that: The RP scheduling algorithm is used to rotate time slices to ensure that each task is executed, including allocating a fixed time slice q to each task process. When a process is not completed within the time slice q, the CPU will be preempted and placed at the end of the ready queue waiting for the next scheduling.

7. A furnace tube automatic transmission control system based on a multi-level feedback array, comprising: A sensor module, a controller module, an actuator module and a human-computer interaction module, wherein the sensor module is used to monitor the position and speed parameters of the furnace tube in real time; the controller module performs data analysis and issues control instructions based on the parameters collected by the sensor module; the actuator module executes the conveying action of the furnace tube according to the control instructions; and the human-computer interaction module is used to set system parameters, display status and accept user operation instructions.

8. The method for automatic furnace tube transmission control based on a multi-level feedback array according to claim 1, characterized in that: The sensor module uses sensors to monitor parameters such as the position and speed of the furnace tube and the load of the conveyor belt in real time, and the sensor data is transmitted to the controller module via wires.

9. The method for automatic furnace tube transmission control based on a multi-level feedback array according to claim 1, characterized in that: The controller module receives the data transmitted by the sensor module and performs calculations and analyses according to a preset algorithm. The controller adopts a PLC programmable logic controller to receive data from the sensor. According to the analysis results, the controller module issues control instructions to adjust the speed and direction of the conveyor belt. At the same time, the controller module is also responsible for monitoring the operating status of the system. Once an abnormality is found, an alarm signal is immediately issued and corresponding protective measures are taken.

10. The method for automatic furnace tube transmission control based on a multi-level feedback array according to claim 1, characterized in that: The actuator module includes a servo motor, a reducer and a conveyor belt, wherein the servo motor adjusts the speed and direction according to the control instructions issued by the controller, thereby driving the conveyor belt to complete the conveying movement of the furnace tube; the reducer is used to reduce the speed of the motor; the conveyor belt carries the wafer and transports it in the furnace tube. do.

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