Process queue dynamic arrangement and parameter issuing intelligent control system and method based on Internet of Things
By collecting real-time global flow information of the production line through the Internet of Things and constructing a virtual process queue, the problem of poor adaptability of RFID systems in mixed-line production is solved, realizing zero-wait operation and whole-line collaborative optimization, and improving the flexibility and robustness of the production line.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- XCMG EXCAVATOR MACHINERY CO LTD
- Filing Date
- 2026-02-03
- Publication Date
- 2026-05-12
AI Technical Summary
In mixed-production lines of multiple varieties and small batches, existing RFID systems cannot sense the overall dynamic flow information of the production line, resulting in poor adaptability to the production process, low system robustness, inability to achieve unmanned operation, and problems of automation process interruption due to identification failure.
By collecting real-time global flow information of the production line through IoT units, a virtual process queue that is synchronized with the physical production line in real time is constructed. The intelligent control unit dynamically adjusts the process queue and issues parameters to achieve zero-wait operation and improve the flexibility and robustness of the production line.
It significantly improved production cycle time and efficiency, enhanced the adaptability of the production line, avoided interruptions caused by single-point failures, and achieved overall line-wide collaborative optimization and precise parameter distribution.
Smart Images
Figure CN122018463A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to an intelligent control system and method for dynamic arrangement and parameter distribution of process queues based on the Internet of Things, belonging to the field of flexible manufacturing technology. Background Technology
[0002] Currently, excavator assembly is characterized by mixed-line production of multiple product types in small batches, and the assembly parameters required for each workstation vary depending on the model. In this mixed-line production mode, operators at each workstation need to confirm the model information and manually select parameters on the workstation control system to trigger the corresponding execution program. This method reduces overall production efficiency and is highly susceptible to product quality issues or even equipment malfunctions due to operator error, making true "unmanned" operation impossible.
[0003] In the existing technology: RFID tags are placed on material or tooling pallets, and RFID readers are deployed at key workstations. When the tooling carrying the material enters the identification range of the RFID reader at the workstation, the reader reads the identity information in the tag and automatically sends it to the production equipment through the group control system. The production equipment then calls the automated processing program and starts production based on the model and other information.
[0004] RFID systems cannot perceive the dynamic flow information of the entire production line and lack the ability to predict the sequence of processes about to enter the workstation. When dynamic changes occur, such as order insertions, temporary equipment failures, or buffering between processes, the system cannot proactively adjust and has poor adaptability.
[0005] Successful identification by RFID readers at fixed points determines the operation of the entire automated process. If identification fails due to radio frequency interference, tag damage, or reader malfunction, the automated process at that station will be interrupted, resulting in poor system robustness.
[0006] The workstation information in an RFID system is independent of each other, and it cannot provide forward-looking production sequence information for downstream processes, thus limiting the possibility of overall line collaborative optimization (such as precise material delivery). Summary of the Invention
[0007] The purpose of this invention is to provide an intelligent control system and method for dynamic arrangement and parameter distribution of process queues based on the Internet of Things. By collecting global flow information on the production line, the system can dynamically arrange the process queues in real time, and achieve "zero-wait" accurate distribution and execution of the parameters required by the workstations, thereby significantly improving production cycle time, flexibility and quality reliability.
[0008] To achieve the above objectives / to solve the above technical problems, the present invention is implemented using the following technical solution.
[0009] On the one hand, the present invention provides an intelligent control system for dynamic arrangement and parameter distribution of process queues based on the Internet of Things, including: a production line control unit, an Internet of Things unit, an intelligent control unit, a manufacturing execution system unit, and an equipment control unit;
[0010] The production line control unit is used to transfer workpieces to be assembled on the physical production line. According to its control and shifting characteristics, it can be divided into two categories: asynchronous shifting lines that operate independently and shift workpieces to be assembled in a discrete manner, and synchronous shifting lines that operate uniformly and shift workpieces to be assembled in a continuous synchronous manner.
[0011] The Internet of Things (IoT) unit is communicatively connected to the production line control unit and is used to collect global movement information of the hybrid production line composed of the asynchronous shift line and the synchronous shift line in real time.
[0012] The manufacturing execution system unit is used to configure and store the corresponding assembly parameters for the workpiece to be assembled.
[0013] The intelligent control unit is communicatively connected to the Internet of Things unit and the manufacturing execution system unit. It is used to receive the global flow information and dynamically construct and maintain a virtual process queue that is synchronized with the physical flow of the physical production line in real time based on the global flow information. It is also used to obtain the assembly parameters of the corresponding workpiece to be assembled from the manufacturing execution system unit according to the real-time status of the virtual process queue and actively send them to the equipment control unit.
[0014] The equipment control unit is communicatively connected to the intelligent control unit and is used to receive and execute the assembly parameters.
[0015] Furthermore, the asynchronous shift line includes one or more combinations of friction lines, rail-guided vehicles, and automated guided vehicles; the synchronous shift line is a plate chain line.
[0016] This invention clarifies the typical and widely used types of industrial conveying equipment to which it applies, giving it strong engineering versatility and clear implementation scenarios.
[0017] Furthermore, the global movement information includes: a data set reflecting the overall dynamic operating status of the hybrid production line, collected by the Internet of Things unit, including at least: independent shift trigger signals from each station of the asynchronous shift line and a unified shift trigger signal from the synchronous shift line.
[0018] Based on this global flow information, a virtual process queue is dynamically constructed and maintained in real time, which is synchronized with the physical material flow of the physical production line. The specific method is as follows: according to the arrival signal of the asynchronous or synchronous shift line, the virtual process queue is dynamically triggered to update, and a process queue that is executed in sequence and is completely synchronized with the physical material flow is mapped in the intelligent control unit.
[0019] Furthermore, the intelligent control unit includes: a communication interface module, a queue management module, a parameter management module, and a data transmission timing control module;
[0020] The communication interface module is used to interact with the IoT unit, the manufacturing execution system unit, and the equipment control unit;
[0021] The queue management module, connected to the communication interface module, is used to construct and dynamically update a virtual process queue that is synchronized with the physical production line in real time, based on the received global flow information and according to a predefined queue management model.
[0022] The parameter management module, connected to the communication interface module, is used to obtain and manage the assembly parameters of the workpiece to be assembled at each workstation from the manufacturing execution system unit.
[0023] The timing control module is connected to the queue management module and the parameter management module. It is used to determine the timing of the assembly parameter issuance based on the real-time position information of the workpieces to be assembled in the virtual process queue, and to trigger the instruction to extract the corresponding assembly parameters from the parameter management module and issue them to the equipment control unit through the communication interface module.
[0024] Furthermore, the timing control module is configured to trigger the issuance of the corresponding assembly parameters of the workpiece when the virtual process queue indicates that the workpiece to be assembled has entered the target workstation and is in place in real time.
[0025] This invention clarifies the connection relationships and functional divisions between modules, providing a clear and modular system architecture. In particular, the delivery timing control module is configured to make "real-time arrival" judgments and trigger delivery based on a virtual queue, which realizes the decoupling and coordination of perception, decision-making and execution.
[0026] Furthermore, the predefined queue management model construction method is specifically as follows:
[0027] set up Let be the material code for the i-th workstation in the virtual process queue at time t. If the material code of the workpiece to be assembled is used, then the first station must satisfy the following: ;
[0028] For an asynchronous shift line, let the total number of stations be... The asynchronous shift line movement signal at the k-th workstation at time t is: , k∈[2, ], then when When =1, perform queue update: , and place ;
[0029] For a synchronous shift line, let the total number of its stations be... If the synchronous shift line movement signal at time t is B(t), then when B(t) = 1, for all j ∈ [2, Workstation execution queue update: , and place .
[0030] This invention defines how to dynamically and accurately refresh the virtual process queue using asynchronous and synchronous shift signals through the queue management model, transforming the abstract "dynamic synchronization" process into programmable and verifiable deterministic logic. Based on an explicit event-driven queue update mechanism, this invention not only ensures high reliability and low latency synchronization between the virtual queue and the physical flow, but also makes the system state calculable and predictable, providing a solid algorithmic foundation for achieving precise timing control and fundamentally eliminating the risk of queue drift or incorrect parameter distribution caused by logical ambiguity.
[0031] Secondly, the present invention provides a method for a smart control system based on the above-mentioned IoT-based process queue dynamic arrangement and parameter distribution, comprising:
[0032] Step S1: In response to the online event of the workpiece to be assembled, load the material code of the online workpiece to be assembled into the first station of the virtual process queue;
[0033] Step S2: Collect global movement information of the production line control unit in real time through the Internet of Things (IoT) unit;
[0034] Step S3: Based on the global flow information, dynamically refresh the material code status of all workstations in the virtual process queue to form a full-line process information queue synchronized with the physical flow.
[0035] Step S4: When it is determined from the whole process information queue that a workpiece to be assembled has entered the target station, trigger and extract the assembly parameters corresponding to the workpiece to be assembled.
[0036] Step S5: Send the extracted assembly parameters to the equipment control unit to drive the equipment control unit to perform zero-wait operation;
[0037] Step S6: Repeat steps S2 to S5 to form a closed-loop control process based on global flow information and the feedback of the entire process information queue.
[0038] Furthermore, the dynamic refreshing of the virtual process queue in step S3 specifically includes:
[0039] For each station on the asynchronous shift line, based on the independent asynchronous shift line movement signal of each station... Update the material code forward station by station;
[0040] For each workstation on the synchronous shift line, the material code of all workstations on the entire line is updated synchronously according to the synchronous shift line movement signal B(t).
[0041] This invention improves the system's compatibility and processing efficiency when dealing with mixed "asynchronous" and "synchronous" production lines, ensuring that both discrete event-driven and continuous cycle-driven workstations can be uniformly and accurately mapped to the virtual queue, thus achieving true hybrid dynamic scheduling.
[0042] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:
[0043] This invention uses IoT units to collect real-time global workflow information across the entire production line and constructs and maintains a virtual process queue that is synchronized with it in real time within the intelligent control unit. This allows the system to proactively monitor the sequence, location, and flow status of all processes on the entire line in real time. When dynamic disturbances occur during production, such as order insertions, temporary equipment failures, or inter-process buffer blockages, the system can proactively and dynamically adjust the arrangement logic and parameter distribution strategies of subsequent processes based on the global virtual queue model. This overcomes the information isolation limitations of existing RFID technology and enables the production line to have a strong adaptability to cope with complex and ever-changing production scenarios.
[0044] This invention utilizes a dedicated timing control module based on a virtual process queue to intelligently determine the optimal time for parameter distribution. When it is determined that the workpiece to be assembled has entered the target station and is in place in real time, the corresponding assembly parameter distribution command is immediately triggered, allowing the equipment control unit to begin execution the moment the workpiece arrives. This mechanism completely eliminates the waiting time introduced by manual confirmation and parameter selection by operators in traditional modes, and also avoids potential process delays or failures caused by the reliance on fixed-point identification in RFID modes. This significantly shortens the operation cycle of a single station, thereby improving overall production pace and efficiency.
[0045] This invention does not rely on the successful identification of an RFID reader at a fixed point; even if an anomaly occurs at a local information collection point, the accuracy of the virtual queue can still be maintained through logical deduction based on the global flow of information, ensuring the continuous issuance of parameters. This distributed, multi-source information sensing mechanism effectively avoids the interruption of the entire workstation or production line due to a single point of failure, and the system robustness is significantly better than existing technologies.
[0046] The virtual process queue constructed by this invention, which is synchronized with the physical entity in real time, is a global data model that spans the upstream and downstream. This not only serves the automatic distribution of parameters for the current workstation, but also provides forward-looking production sequence information for advanced applications such as precise material delivery and accurate prediction of production progress upstream, laying a solid foundation for realizing the digital, intelligent, and collaborative optimization of the entire production line. Attached Figure Description
[0047] Figure 1 This is a system schematic diagram of the present invention;
[0048] Figure 2 This is a schematic diagram of the process of the present invention. Detailed Implementation
[0049] It should be noted that:
[0050] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.
[0051] The term "and / or" simply describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.
[0052] Example 1
[0053] like Figure 1 The embodiment shown provides an intelligent control system for dynamic arrangement and parameter distribution of process queues based on the Internet of Things, including: a production line control unit, an Internet of Things unit, an intelligent control unit, a manufacturing execution system unit, and an equipment control unit;
[0054] The production line control unit is used to transfer workpieces to be assembled on the physical production line. According to its control and shifting characteristics, it can be divided into two categories: asynchronous shifting lines that operate independently and shift workpieces to be assembled in a discrete manner, and synchronous shifting lines that operate uniformly and shift workpieces to be assembled in a continuous synchronous manner.
[0055] The Internet of Things (IoT) unit is communicatively connected to the production line control unit and is used to collect global movement information of the hybrid production line composed of the asynchronous shift line and the synchronous shift line in real time.
[0056] The manufacturing execution system unit is used to configure and store the corresponding assembly parameters for the workpiece to be assembled.
[0057] The intelligent control unit is communicatively connected to the Internet of Things unit and the manufacturing execution system unit. It is used to receive the global flow information and dynamically construct and maintain a virtual process queue that is synchronized with the physical flow of the physical production line in real time based on the global flow information. It is also used to obtain the assembly parameters of the corresponding workpiece to be assembled from the manufacturing execution system unit according to the real-time status of the virtual process queue and actively send them to the equipment control unit.
[0058] The equipment control unit is communicatively connected to the intelligent control unit and is used to receive and execute the assembly parameters.
[0059] The asynchronous shifting line includes one or more combinations of friction lines, rail-guided vehicles, and automated guided vehicles; the synchronous shifting line is a plate chain line.
[0060] The global movement information is a data set collected by the Internet of Things unit that reflects the overall dynamic operation status of the hybrid production line, including at least: independent shift trigger signals from each station of the asynchronous shift line and a unified shift trigger signal from the synchronous shift line; based on these signals, the real-time position sequence of all workpieces on the entire line can be logically deduced.
[0061] Based on this global flow information, a virtual process queue is dynamically constructed and maintained in real time, which is synchronized with the physical material flow of the physical production line. The specific method is as follows: according to the arrival signal of the asynchronous or synchronous shift line, the virtual process queue is dynamically triggered to update, and a process queue that is executed in sequence and is completely synchronized with the physical material flow is mapped in the intelligent control unit.
[0062] The intelligent control unit includes: a communication interface module, a queue management module, a parameter management module, and a data transmission timing control module;
[0063] The communication interface module is used to interact with the IoT unit, the manufacturing execution system unit, and the equipment control unit;
[0064] The queue management module, connected to the communication interface module, is used to construct and dynamically update a virtual process queue that is synchronized with the physical production line in real time, based on the received global flow information and according to a predefined queue management model.
[0065] The parameter management module, connected to the communication interface module, is used to obtain and manage the assembly parameters of the workpiece to be assembled at each workstation from the manufacturing execution system unit.
[0066] The timing control module is connected to the queue management module and the parameter management module. It is used to determine the timing of the assembly parameter issuance based on the real-time position information of the workpieces to be assembled in the virtual process queue, and to trigger the instruction to extract the corresponding assembly parameters from the parameter management module and issue them to the equipment control unit through the communication interface module.
[0067] The specific method for constructing the predefined queue management model is as follows:
[0068] set up Let be the material code for the i-th workstation in the virtual process queue at time t. If the material code of the workpiece to be assembled is used, then the first station must satisfy the following: ;
[0069] For an asynchronous shift line, let the total number of stations be... The asynchronous shift line movement signal at the k-th workstation at time t is: , k∈[2, ], then when When =1, perform queue update: , and place ;
[0070] For a synchronous shift line, let the total number of its stations be... If the synchronous shift line movement signal at time t is B(t), then when B(t) = 1, for all j ∈ [2, Workstation execution queue update: , and place .
[0071] The timing control module is configured to trigger the issuance of the corresponding assembly parameters of the workpiece when the virtual process queue indicates that the workpiece to be assembled has entered the target workstation and is in place in real time.
[0072] Example 2
[0073] like Figure 2 One embodiment shown provides a method for a smart control system based on the above-described IoT-based process queue dynamic arrangement and parameter distribution, comprising:
[0074] Setting scenarios for intelligent control systems:
[0075] Production line: Taking a single synchronous shifting plate chain line as an example, assume that its first station is empty and all other stations are occupied;
[0076] Internet of Things (IoT) unit: A photoelectric sensor is deployed at the end of the plate chain line to output a unified shift signal B(t);
[0077] Status Information: Current process queue status (partial examples): Station 1 - On-line; Station 2 - Slewing bearing preload; Station 3 - Slewing bearing final tightening; Station 4 - Workpiece flipping;
[0078] Initial queue: The material codes are [empty, 2154, 150G, 245C] in sequence. Different vehicle models have different slewing bearing material numbers at automated station 3, and the bolt specifications and torque values are also different.
[0079] The specific process steps include:
[0080] Step S1: In response to the online event of the workpiece to be assembled, load the material code of the online workpiece to be assembled into the first station of the virtual process queue; put workpiece 135G online and update the virtual queue to [135G, 2154, 150G, 245C].
[0081] Step S2: The IoT unit collects global movement information of the production line control unit in real time; when the conveyor belt moves, the IoT unit collects movement information and pushes it to the intelligent control unit.
[0082] Step S3: Based on the global flow information, dynamically refresh the material code status of all workstations in the virtual process queue to form a full-line process information queue synchronized with the physical flow. The virtual queue is refreshed to [empty, 135G, 2154, 150G].
[0083] Step S4: When it is determined from the whole process information queue that a workpiece to be assembled has entered the target station, the assembly parameters corresponding to the workpiece to be assembled are triggered and extracted; the intelligent control unit extracts the slewing bearing material number, bolt specifications, and torque value assembly parameters of workpiece 2154 from the manufacturing execution unit.
[0084] Step S5: Send the extracted assembly parameters to the equipment control unit to drive the equipment control unit to perform zero-wait operation; the intelligent control unit sends the assembly parameters corresponding to 2154 to the equipment control unit of workstation 3.
[0085] Step S6: Repeat steps S2 to S5 to form a closed-loop control process based on global flow information and the feedback of the entire process information queue.
[0086] The step S3 of dynamically refreshing the virtual process queue specifically includes:
[0087] For each station on the asynchronous shift line, based on the independent asynchronous shift line movement signal of each station... Update the material code forward station by station;
[0088] For each workstation on the synchronous shift line, the material code of all workstations on the entire line is updated synchronously according to the synchronous shift line movement signal B(t).
[0089] Example 3
[0090] This embodiment provides a method for a smart control system based on the above-mentioned IoT-based process queue dynamic arrangement and parameter distribution, including:
[0091] Setting scenarios for intelligent control systems:
[0092] Production line: A hybrid production line consisting of an asynchronous shifting friction line (2 stations) and a synchronous shifting plate chain line (4 stations) that intersect at 3 stations;
[0093] IoT Unit: A photoelectric sensor is deployed at the end of the plate chain line to output a unified shift signal B(t); a sensor is deployed at each station of the friction line to output independent shift signals M1(t) and M2(t).
[0094] Initial queue: The virtual process queue is [empty, 135G, 2154, 150G, 245G, 380G].
[0095] Dynamically refresh the demo (to handle order insertions), specifically including:
[0096] The original frame at workstation 4 was put into the cache, and a new 270G frame was urgently put into production at workstation 4 (interrupted order).
[0097] The IoT unit collects emergency online events and their codes;
[0098] The queue management module of the intelligent control unit receives this event and dynamically reconstructs the virtual queue; the new queue becomes: [empty, 135G, 2154, 270G, 245G, 380G]. This process demonstrates the proactive queue adjustment capability based on global events.
[0099] Parameter delivery demonstration ("zero wait"), specifically including:
[0100] The conveyor belt moves along the path, and the Internet of Things (IoT) unit collects the signal B(t)=1.
[0101] The queue management module synchronously updates the workstation codes of all workstations on the board chain line according to B(t)=1: [empty, 135G, empty, 2154, 270G, 245G];
[0102] The timing control module detected that workpiece 270G was in station 5 of the queue, and based on the continuous B(t) signal sequence, it logically determined that workpiece 270G had entered and was stably located in station 5.
[0103] The process is triggered immediately, retrieving the tightening parameters of the 270G model from the parameter management module and sending them to the equipment control unit at workstation 5.
[0104] The tightening machine calls the program the moment it receives the parameters, achieving "zero waiting" operation after the workpiece is in place at 270G, demonstrating accurate delivery based on queue logic judgment without relying on fixed-point RFID identification.
[0105] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.
Claims
1. An intelligent control system for dynamic scheduling and parameter distribution of process queues based on the Internet of Things, characterized in that, include: Production line control unit, Internet of Things unit, intelligent control unit, manufacturing execution system unit, and equipment control unit; The production line control unit is used to transfer workpieces to be assembled on the physical production line. According to its control and shifting characteristics, it can be divided into two categories: asynchronous shifting lines that operate independently and shift workpieces to be assembled in a discrete manner, and synchronous shifting lines that operate uniformly and shift workpieces to be assembled in a continuous synchronous manner. The Internet of Things (IoT) unit is communicatively connected to the production line control unit and is used to collect global movement information of the hybrid production line composed of the asynchronous shift line and the synchronous shift line in real time. The manufacturing execution system unit is used to configure and store the corresponding assembly parameters for the workpiece to be assembled. The intelligent control unit is communicatively connected to the Internet of Things unit and the manufacturing execution system unit. It is used to receive the global flow information and dynamically construct and maintain a virtual process queue that is synchronized with the physical flow of the physical production line in real time based on the global flow information. It is also used to obtain the assembly parameters of the corresponding workpiece to be assembled from the manufacturing execution system unit according to the real-time status of the virtual process queue and actively send them to the equipment control unit. The equipment control unit is communicatively connected to the intelligent control unit and is used to receive and execute the assembly parameters.
2. The IoT-based intelligent control system for dynamic arrangement and parameter distribution of process queues according to claim 1, characterized in that, The asynchronous shifting line includes one or more combinations of friction lines, rail-guided vehicles, and automated guided vehicles; the synchronous shifting line is a plate chain line.
3. The IoT-based intelligent control system for dynamic arrangement and parameter distribution of process queues according to claim 1, characterized in that, The global flow information is a data set that reflects the overall dynamic operating status of the hybrid production line, collected by the Internet of Things (IoT) unit.
4. The IoT-based intelligent control system for dynamic arrangement and parameter distribution of process queues according to claim 1, characterized in that, The intelligent control unit includes: a communication interface module, a queue management module, a parameter management module, and a data transmission timing control module; The communication interface module is used to interact with the IoT unit, the manufacturing execution system unit, and the equipment control unit; The queue management module, connected to the communication interface module, is used to construct and dynamically update a virtual process queue that is synchronized with the physical production line in real time, based on the received global flow information and according to a predefined queue management model. The parameter management module, connected to the communication interface module, is used to obtain and manage the assembly parameters of the workpiece to be assembled at each workstation from the manufacturing execution system unit. The timing control module is connected to the queue management module and the parameter management module. It is used to determine the timing of the assembly parameter issuance based on the real-time position information of the workpieces to be assembled in the virtual process queue, and to trigger the instruction to extract the corresponding assembly parameters from the parameter management module and issue them to the equipment control unit through the communication interface module.
5. The IoT-based intelligent control system for dynamic arrangement and parameter distribution of process queues according to claim 4, characterized in that, The specific method for constructing the predefined queue management model is as follows: For an asynchronous shift line body, let... Let be the material code for the i-th workstation in the virtual process queue at time t. If the material code of the workpiece to be assembled is used, then the first station must satisfy the following: ; Let the total number of workstations on the asynchronous shift line be... The movement signal of the k-th station at time t of the asynchronous shift line is: , k∈[2, ], then when When =1, perform queue update: , and place ; For a synchronous shift line, let... Let be the material code for the j-th workstation in the virtual process queue at time t. If the material code of the workpiece to be assembled is used, then the first station must satisfy the following: ; Let the total number of stations on the synchronous shift line be If the moving line signal at time t is B(t), then when B(t) = 1, for all j ∈ [2, Workstation execution queue update: , and place .
6. The IoT-based intelligent control system for dynamic arrangement and parameter distribution of process queues according to claim 4 or 5, characterized in that, The timing control module is configured to trigger the issuance of the corresponding assembly parameters of the workpiece when the virtual process queue indicates that the workpiece to be assembled has entered the target workstation and is in place in real time.
7. A method for a smart control system for dynamic arrangement and parameter distribution of process queues based on the Internet of Things as described in any one of claims 1 to 6, characterized in that, Includes the following steps: Step S1: In response to the online event of the workpiece to be assembled, load the material code of the online workpiece to be assembled into the first station of the virtual process queue; Step S2: Collect global movement information of the production line control unit in real time through the Internet of Things (IoT) unit; Step S3: Based on the global flow information, dynamically refresh the material code status of all workstations in the virtual process queue to form a full-line process information queue synchronized with the physical flow. Step S4: When it is determined from the whole process information queue that a workpiece to be assembled has entered the target station, trigger and extract the assembly parameters corresponding to the workpiece to be assembled. Step S5: Send the extracted assembly parameters to the equipment control unit to drive the equipment control unit to perform zero-wait operation; Step S6: Repeat steps S2 to S5 to form a closed-loop control process based on global flow information and the feedback of the entire process information queue.
8. The method according to claim 7, characterized in that, The step S3 of dynamically refreshing the virtual process queue specifically includes: For each station on the asynchronous shift line, based on the independent asynchronous shift line movement signal of each station... Update the material code forward station by station; For each workstation on the synchronous shift line, the material code of all workstations on the entire line is updated synchronously according to the synchronous shift line movement signal B(t).