A production line modeling method based on a multi-layer logic model
By using hierarchical modeling and event-driven simulation, the problems of long modeling cycles and poor reusability in traditional production line design are solved, enabling rapid and modular production line modeling and optimization, and improving the flexibility and operational efficiency of the production line.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SOUTHEAST UNIV
- Filing Date
- 2025-11-19
- Publication Date
- 2026-07-03
AI Technical Summary
Traditional production line design relies on the experience of domain experts, has a long modeling cycle, poor flexibility, and is difficult to respond quickly to changes in market demand. In addition, existing models have high coupling and poor reusability, and lack efficient simulation verification and optimization.
By adopting a hierarchical modeling approach, a unified logical abstraction and standardized signal interface are established at the equipment level, unit level, and production line level. Gantt charts are generated through event-driven simulation for performance evaluation and optimization, enabling rapid construction, modular combination, and cross-scenario migration.
It improves the efficiency of production line modeling, supports rapid expansion and reuse, enhances the flexibility and operational performance of the production line, and can identify bottlenecks and optimize the production process under complex operating conditions.
Smart Images
Figure CN121541595B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of intelligent manufacturing and production line design technology, and in particular to a production line modeling method based on a multi-layer logic model. Background Technology
[0002] As global manufacturing evolves towards intelligence and flexibility, production line design faces increasing challenges in terms of complexity, diversity, and dynamism. Traditional production line design relies heavily on the experience of domain experts, employing manual modeling and configuration methods, resulting in long design cycles, poor flexibility, and difficulty in modification and expansion. Especially with the trend towards small-batch, customized production, production lines require frequent reconfiguration, and traditional methods struggle to respond quickly to changes in market demand.
[0003] While some existing technologies employ simulation, optimization algorithms, or digital twin technology to model and analyze production lines, these methods mostly focus on overall performance optimization at the production line level (such as scheduling and bottleneck analysis) or modeling specific equipment. They lack a systematic and standardized description of the internal logical structure and multi-level interactions of the production line. Existing models often exhibit high coupling and poor reusability, with inconsistent interfaces and behavioral logic between different equipment and units, resulting in low model building efficiency and difficulty in supporting automated design and simulation verification. Summary of the Invention
[0004] This invention relates to the field of intelligent manufacturing and production line design, proposing a production line modeling method based on a multi-layered logical model. It aims to address problems in existing technologies such as long modeling cycles, inconsistent interfaces, poor reusability, and a lack of efficient simulation verification and optimization. By establishing unified logical abstractions and standardized signal interfaces at the equipment, unit, and production line levels, this invention achieves rapid model construction, modular combination, and cross-scenario migration, and supports automatic generation and simulation optimization of Gantt charts under complex operating conditions.
[0005] The method of this invention generally adopts a technical approach of "layered modeling + standard interface + event-driven simulation". First, at the device level, individual devices are uniformly abstracted. Then, at the unit level, multiple devices are functionally encapsulated into logical units with independent task capabilities. Finally, at the production line level, the global logic, cycle time, and bottlenecks are modeled and visualized. To ensure cross-layer consistency and low coupling, a unified interface specification and modeling framework are established, and an event-driven mechanism is used for production process simulation, automatically generating Gantt charts for performance evaluation and optimization loop closure.
[0006] At the device level, the device is modeled using a four-tuple approach, with the "input-processing-output" task chain at its core.
[0007] (1)
[0008] (Device-level logical model), Geo (geometric and spatial layout parameters), Phy (identification information, interfaces, and state sets), Beh (functional behavior and state transition logic), and Rule (event triggering and signal transmission rules). To improve project scalability, object-oriented encapsulation, inheritance, and polymorphism mechanisms are adopted: encapsulation isolates internal model data from external interfaces; inheritance extends special attributes and methods for different device types under a unified base class; and polymorphism implements differentiated behaviors under a unified interface, ensuring rapid access and reuse of different devices within the same framework.
[0009] At the unit level, several device-level models are functionally coordinated to form independent logical units. These units fall into three categories: storage units for material storage, production units for manufacturing and processing, and inspection units for product quality inspection.
[0010] (2)
[0011] in A logical model representing a production unit. This represents the complete device-level logical model within the production unit, where State represents the current state of the device. and Represents the input and output signals of the unit, while Relation represents the state-event-behavior driven mapping relationship within the logical model of the production unit.
[0012] Each unit exposes only standardized signal interfaces to the outside world, and internally describes the inter-device coordination and execution sequence in a "state-event-behavior" manner. To characterize the internal logic of the unit, a formal description using state transitions and event triggering is employed: the device state satisfies...
[0013] (3)
[0014] (4)
[0015] In the formula, S(t) represents the state of the device at time t, E(t) represents the event of the device at time t, and the state S(t+1) of the device at the next time step is determined by both S(t) and E(t), with the mapping function f. The event of the device at time t is affected by the current state, with the mapping function g. The units are divided into three categories based on their production functions: storage units, production units, and detection units. Each type of unit is encapsulated as a pluggable module with a unified interface. The internal logic of the unit is shielded from the outside, relying only on input signals to complete tasks and provide feedback, thereby achieving cross-unit decoupling and high interoperability.
[0016] At the production line level, the global logical structure of the production line is abstracted based on the unit-level model.
[0017] (5)
[0018] In the formula, Represents a production line-level logic model. Represents all production units in the production line. This represents the signal logic relationships between all units and devices. A Gantt chart representation of the simulation results of the production process.
[0019] Logical relationship diagrams are used to intuitively express the dependencies, interconnections, and path relationships between units; Gantt charts are used to express task timing, resource utilization, and waiting relationships, which are used for global cycle time coordination and bottleneck location. Bottleneck identification is based on simulation statistics of unit processing time, waiting time, and equipment utilization, and targeted optimization strategies such as parallelization, layout rearrangement, and task reallocation are proposed.
[0020] To achieve cross-level consistency and rapid assembly, a unified interface and modeling framework are established, standardizing the definitions of signal semantics, direction, load, and timing. Interface contracts ensure version compatibility and interoperability. New devices or units can be integrated into existing models simply by following the interface protocol to complete signal and parameter mapping, without modifying existing logic. This significantly improves scalability, reusability, and cross-scenario migration capabilities.
[0021] To support the closed-loop performance evaluation and optimization, this invention employs event-driven production process simulation and automatically generates Gantt charts.
[0022] First, all input information is defined based on the production line conditions. Based on the characteristics of the model constructed above, it is loaded into the equipment set to form the following representation:
[0023] (6)
[0024] In the formula, E represents all the equipment, the total number of equipment is N, the processing time of the i-th equipment is pi>0, and the equipment capacity is 1 (at most 1 event can be processed at the same time).
[0025] Next, based on the production logic relationships between the various devices, the signal transmission logic and signal connection configurations between the devices are defined. Each device has a set of allowed inbound triggers and outbound destinations:
[0026] (7)
[0027] in It means: if and only if it comes from the device The signal arrives and enters the device When the event queue is in the device You can start processing; after processing is complete, press... Specified destination forwarding ( This can be a single target or a set of targets, representing parallel output. To more realistically reflect actual processing conditions, the time for material transfer between different equipment units also needs to be defined, as shown in the following formula:
[0028] (8)
[0029] Indicates from device The signal conveyor belt sends a signal to the equipment after the transport is completed. The time delay.
[0030] This testing equipment is somewhat unique compared to other devices. Its output is transmitted to different downstream devices based on the product status. Therefore, it needs to be designed with segmented descriptions, and its judgment on the product is reflected based on a preset pass rate. This necessitates a testing / judgment unit with random flow distribution, which can be denoted as... ,for Define a set of result categories (e.g., {good, bad, rework}) and its distribution as follows:
[0031] (9)
[0032] This refers to detection devices when the generated random number is r. The corresponding signal / connection configuration, where q refers to the corresponding result category. This corresponds to the upper limit of random values for that category. It is the lower bound of random values for this category. This indicates that the generated random value r is in the range of 0 to 1. Finally, an initial signal is input, and several initial events (Event) are placed into the event queue of the initial device to drive the simulation to start running.
[0033] After the simulation begins, the changes in the state variables of each equipment model are recorded and monitored, which serve as state and event responses to jointly drive the simulation of the entire production line.
[0034] First, there's the equipment status, used to identify the current state of the equipment and thus determine which specific event-driven logic the equipment is currently using. This is the core of production line simulation.
[0035] (10)
[0036] in This represents the state of device i at time t. Initially, all device states are set to 1 (idle). Another core element is the event, which, combined with the device state, drives the device to the next state; therefore, an event queue needs to be built for the device.
[0037] (11)
[0038] This represents the signal that device i needs to process at time t (each event records the trigger source). Where to go after completion To better visualize the equipment's status changes and operational status, a Gantt chart is needed. Constructing a Gantt chart requires recording the entire processing flow. In-process records:
[0039] (12)
[0040] This indicates the signal that device i is processing at time t. It is the time when processing begins. This refers to the processing time. This refers to the source and destination of the signal. Similarly, the time loss of each semi-finished product during its transfer on the conveyor belt or AGV also needs to be recorded and displayed in the Gantt chart, showing the set of semi-finished products in transit:
[0041] (13)
[0042] This represents the semi-finished product being transported at time t. This indicates that the semi-finished product is transported from i to j via a conveyor belt or AGV. It is the time when transportation begins. It is the arrival time of the shipment. The arrival time of transportation consists of the start time plus the transportation time.
[0043] After defining all the static input parameters and dynamic monitoring parameters, it is necessary to define the simulation logic. Due to the adoption of the multi-level model construction method mentioned above, each device only needs to consider the received events and the current state of the device to make judgments on its own and complete the state transition and event processing.
[0044] Trigger the initial signal and load the event sequence of the first device. Then, it determines the static parameters of each device and the update of the dynamic state variables after the input is completed. It then determines the impact of the update of the dynamic state variables on the signal queues of the remaining devices until there are no more updates.
[0045] For two pieces of equipment that need to be transferred, it is necessary to determine the transfer time required for the semi-finished product. Let's assume the arrival time of the semi-finished product is... And the current time Then the semi-finished product arrives at the corresponding equipment j.
[0046] (14)
[0047] Find all matching signal / connection configurations for device j, complete the pairing, and load the corresponding signal / connection configurations. Add to event queue In the middle. Thus, the transmission and updating of signals after transfer are completed.
[0048] After a piece of equipment has been in the production / testing process for a period of time, its task will be completed, and its status needs to be updated and corresponding signals sent out in a timely manner. If and Take the head of the event queue. Processing begins, equipment status changes to busy, and the corresponding record in the in-process record is completed:
[0049] (15)
[0050] Current time Once the processing is complete, the equipment returns to idle status, the work-in-process record becomes empty, and the completed semi-finished product is sent to the next piece of equipment.
[0051] (16)
[0052] In summary, this invention constructs a full-stack modeling method from equipment to unit to the entire production line through a unified multi-layer logic model and standardized signal interfaces, realizing rapid, modular, and scalable production line logic modeling; through event-driven simulation and automatic Gantt chart generation, it constructs an efficient verification and optimization channel, enabling bottleneck identification and performance improvement under complex operating conditions, significantly improving the efficiency, flexibility, and operational performance of production line design.
[0053] The beneficial effects of this invention are as follows:
[0054] (1) The present invention establishes a unified logical modeling framework, realizes hierarchical logical abstraction at the equipment level, unit level and production line level, solves the problem of inconsistent modeling methods in the prior art, and improves the efficiency of production line modeling.
[0055] (2) The present invention adopts a standardized interface and a modular modeling mechanism, which enables the logical model to be quickly expanded and supports cross-scenario reuse, thus solving the problem of insufficient scalability in the prior art.
[0056] (3) The production line-level logic model of the present invention can support simulation-based verification and optimization, thereby improving the flexibility and operating efficiency of the production line. Attached Figure Description
[0057] To more clearly illustrate the implementation of the present invention or the existing technical solutions, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.
[0058] Figure 1 Content and construction process of multi-layer logical model
[0059] Figure 2 Logical model production process logic simulation and optimization. Detailed Implementation
[0060] Example 1:
[0061] To verify the method of this invention, this embodiment selects a precision electronic assembly line in the aircraft antenna manufacturing process as a case study. This highly automated production line encompasses multiple high-precision, high-performance devices, spanning the entire process from raw material storage to finished product inspection and re-storage, creating a complex production environment to evaluate the adaptability and effectiveness of this model in practical applications. The production process features diverse functions, complex interdependencies between tasks, and tightly coupled process stages, making this scenario ideal for verifying the method's ability to coordinate, optimize, and validate multi-stage workflows.
[0062] The product selected for this verification is a satellite communication antenna, mainly consisting of components such as radiating elements, feed networks, array panels, and outer housings. Micro-assembly is a critical step in the antenna manufacturing process, requiring precise integration of components such as radiators, matching networks, and support structures to ensure alignment accuracy, minimize interference, and improve overall performance. A typical flexible digital micro-assembly production line is equipped with fully automated processing units, including dispensing, component placement, reflow soldering, plasma cleaning, bonding, and final inspection. These processing units constitute the complete content of the case scenario.
[0063] In the device-level logic model, object-oriented encapsulation, inheritance, and polymorphism are employed to ensure the flexibility and scalability of the device model.
[0064] Encapsulation: By encapsulating the internal state and behavior of a device within a class, it interacts with the outside world only through a public interface. This reduces coupling between devices and ensures the independence and security of the device's internal logic.
[0065] Inheritance: By defining common attributes and methods through a base class, different types of devices inherit and extend their own unique functionalities. Inheritance simplifies the design of device classes, avoids code duplication, and improves extensibility.
[0066] Polymorphism: Different devices perform the same operation through a unified interface, with the specific behavior determined by the device type. Polymorphism enables devices to collaborate through a unified calling method, simplifying model control and management in the simulation process.
[0067] In this invention, equipment is categorized into multiple types, including but not limited to production equipment, warehousing equipment, transportation equipment, auxiliary equipment, and testing equipment. Each type of equipment has different functions, and the implementation of the equipment class varies according to these functional differences. An object-oriented modeling approach is used to complete the process. The construction of.
[0068] Storage Unit: Primarily used for the storage and management of materials, providing functions such as inventory query, material addition and removal.
[0069] Transport Unit: Used for handling materials or products, with loading and unloading functions, and capable of conveying materials on the production line.
[0070] Production Unit: Used to complete specific production and processing tasks, such as assembly, welding, and cutting, and has functions such as starting and stopping production.
[0071] Inspection Unit: Used for quality inspection of products, with functions of inspection, determining pass / fail and scrap, and providing feedback signals based on the inspection results.
[0072] By classifying devices according to their functions and inheriting from a unified base class, all types of devices can work within the same framework, achieving collaboration and interoperability between devices.
[0073] After constructing the device-level model, multiple devices are combined into functionally independent production units as shown in the equation. It includes equipment, current status, input signals, and output signals, all of which can be input or received externally. Internally, functions such as the "dispensing unit," "component placement unit," and "reflow soldering unit" are modules where multiple devices work together to complete tasks. Each unit-level model achieves collaborative work between devices through a state-event driven mechanism. For example, in the "dispensing unit," the dispensing device and the robotic arm cooperate through event-driven mechanisms. When the robotic arm completes the handling task, the dispensing device receives the task start signal through a signal interface and performs the dispensing operation. Each unit defines a standardized external signal interface, ensuring decoupling and interoperability between units.
[0074] Table 1. All constructed logical models
[0075]
[0076] During the construction process, the state of each production unit is determined by the device state and events (E). The mapping relationship between state transitions and event triggering is as follows:
[0077]
[0078]
[0079] In the formula, S(t) represents the state of the device at time t, E(t) represents the event of the device at time t, and the state S(t+1) of the device at the next time step is determined by both S(t) and E(t), with the mapping function f. The event of the device at time t is affected by the current state, with the mapping function g. The units are divided into three categories based on their production functions: storage units, production units, and detection units. Each type of unit is encapsulated as a pluggable module with a unified interface. The internal logic of the unit is shielded from the outside, relying only on input signals to complete tasks and provide feedback, thereby achieving cross-unit decoupling and high interoperability.
[0080] Building upon the unit-level model, a production line-level logical model is constructed to abstract the logical relationships of the entire production line, including task connections and resource allocation between different production units. Through logical relationship diagrams and Gantt charts, the production line can be globally modeled and visualized, helping to optimize the overall production process.
[0081]
[0082] In the formula, Represents a production line-level logic model. Represents all production units in the production line. This represents the signal logic relationships between all units and devices. A Gantt chart representation of the simulation results of the production process.
[0083] To verify and optimize the production line design, this invention employs an event-driven mechanism for production process simulation. By simulating the equipment's operational flow, a Gantt chart of task scheduling and resource utilization is generated. During the simulation, the state of each piece of equipment changes according to preset events and task execution progress.
[0084] The production process simulation employs an event-driven mechanism, with simulation time recorded and updated incrementally at time points. Each time the simulation time arrives, the current state of all equipment is evaluated based on the timestamps of events in the event queue.
[0085] First, all input information is defined based on the production line conditions. Based on the characteristics of the model constructed above, it is loaded into the equipment set to form the following representation:
[0086]
[0087] In the formula, E represents all the equipment, the total number of equipment is 16, the processing time of the i-th equipment is pi>0; the equipment capacity is 1 (at most 1 event can be processed at the same time).
[0088] Next, based on the production logic relationships between the various devices, the signal transmission logic and signal connection configurations between the devices are defined. Each device has a set of allowed inbound triggers and outbound destinations, such as device 1:
[0089]
[0090] Where 1 represents device 1, and {3, 2} indicates that device 1 can start processing if and only if a signal from device 3 arrives and enters the event queue of device 1; after processing is completed, the specified destination is forwarded to 2 (to more realistically reflect the actual processing situation, the time for material transfer between various device units must also be defined, and its transport time is as follows: "transTime": { "1-2": 30, "2-4": 30, "4-6": 30, "6-7": 30, "7-10": 30, "10-11": 30, "11-13": 30, "13-16": 30}
[0091] This testing equipment is somewhat unique compared to other devices. Its output is transmitted to different downstream devices based on the product status. Therefore, it needs to be designed with segmented descriptions, and its judgment on the product is reflected based on a preset pass rate. This necessitates a testing / judgment unit with random flow distribution, which can be denoted as... ,for Define a set of result categories (e.g., {good, bad, rework}) and its distribution as follows:
[0092]
[0093] This refers to detection devices when the generated random number is r. The corresponding signal / connection configuration indicates that the preset pass rate of the semi-finished product is 90%. If it passes, it will be sent away by the robotic arm 7 for further processing; if it fails, it will be sent to the waste table by the robotic arm 9.
[0094] After the simulation begins, the state changes of each equipment model are recorded and monitored, serving as state and event responses to jointly drive the simulation of the entire production line. First, the equipment state is used to identify the current state of the equipment, thereby determining which specific event-driven logic the equipment is currently using; this is the core of the production line simulation.
[0095]
[0096] in This represents the state of device i at time t. Initially, all device states are set to 1 (idle). Another core element is the event, which, combined with the device state, drives the device to the next state; therefore, an event queue needs to be built for the device.
[0097]
[0098] This represents the signal that device i needs to process at time t (each event records the trigger source). Where to go after completion To better visualize the equipment's status changes and operational status, a Gantt chart is needed. Constructing a Gantt chart requires recording the entire processing flow. In-process records:
[0099]
[0100] This indicates the signal that device i is processing at time t. It is the time when processing begins. This refers to the processing time. This refers to the source and destination of the signal. Similarly, the time loss of each semi-finished product during its transfer on the conveyor belt or AGV also needs to be recorded and displayed in the Gantt chart, showing the set of semi-finished products in transit:
[0101]
[0102] This represents the semi-finished product being transported at time t. This indicates that the semi-finished product is transported from i to j via a conveyor belt or AGV. It is the time when transportation begins. It is the arrival time of the shipment. The arrival time of transportation consists of the start time plus the transportation time.
[0103] After defining all the static input parameters and dynamic monitoring parameters, it is necessary to define the simulation logic. Due to the adoption of the multi-level model construction method mentioned above, each device only needs to consider the received events and the current state of the device to make judgments on its own and complete the state transition and event processing.
[0104] Trigger the initial signal and load the event sequence of the first device. Then, it determines the static parameters of each device and the update of the dynamic state variables after the input is completed. It then determines the impact of the update of the dynamic state variables on the signal queues of the remaining devices until there are no more updates.
[0105] For two pieces of equipment that need to be transferred, it is necessary to determine the transfer time required for the semi-finished product. Let's assume the arrival time of the semi-finished product is... And the current time Then the semi-finished product arrives at the corresponding equipment j.
[0106]
[0107] Find all matching signal / connection configurations for device j, complete the pairing, and load the corresponding signal / connection configurations. Add to event queue In the middle. Thus, the transmission and updating of signals after transfer are completed.
[0108] After a piece of equipment has been in the production / testing process for a period of time, its task will be completed, and its status needs to be updated and corresponding signals sent out in a timely manner. If and Take the head of the event queue. Processing begins, equipment status changes to busy, and the corresponding record in the in-process record is completed:
[0109]
[0110] Current time Once the processing is complete, the equipment returns to idle status, the work-in-process record becomes empty, and the completed semi-finished product is sent to the next piece of equipment.
[0111]
[0112] Based on this, static parameter values can be obtained by adjusting the input, and the production logic of the production line can be quickly adjusted to achieve simulation optimization of the production process, as shown in the table.
[0113]
[0114] In summary, this invention constructs a full-stack modeling method from equipment to unit to the entire production line through a unified multi-layer logic model and standardized signal interfaces, realizing rapid, modular, and scalable production line logic modeling; through event-driven simulation and automatic Gantt chart generation, it constructs an efficient verification and optimization channel, enabling bottleneck identification and performance improvement under complex operating conditions, significantly improving the efficiency, flexibility, and operational performance of production line design.
Claims
1. A production line modeling method based on a multi-layer logic model, characterized by, Includes the following steps: S1, construct a device-level logic model, abstract a single device into an "input-processing-output" logic chain, and record the device's geometric information, physical information, behavioral logic and rule definition in the form of a four-tuple; S2 constructs a unit-level logical model, combines multiple device-level logical models into a unit with independent task execution capabilities, and realizes collaborative control between devices through a state-event driven mechanism, and achieves decoupled interaction between units through standardized signal interfaces; S3 constructs a production line-level logical model, which abstracts the overall logical relationship of the production line based on the unit-level model, supports production cycle synchronization control and bottleneck identification, and is expressed through logical relationship diagrams and Gantt charts; S4 establishes a unified interface and modeling framework to enable information interaction between logical models at different levels, and supports the scalability, reusability and cross-scenario application of the model. The quadruple mentioned in S1 includes: （1） representative device-level logic model; Geo represents the device's geometric spatial location, footprint, range of motion, and configuration parameters; Phy represents the device's identification information, signal interfaces, status sets, and configuration attributes; Beh represents the functional behavior of a device, including input response, processing logic, state changes, and output behavior; Rule represents the device's event triggering mechanism, logical rules, and signal transmission path; The construction process of the unit-level logic model described in S2 includes: selecting several device-level logic models according to functional requirements and combining them into independent functional units; realizing coordination between devices through a state-event driven mechanism; and communicating through standardized signal interfaces. （2） wherein a logical model representing the production unit, State representing the current state of the equipment, and input and output signals of the unit, Relation representing the state-event-behavior driven mapping within the logical model of the production unit; In S3, the production line-level logic model includes production cycle control and bottleneck identification mechanisms to support global modeling and visualization of the production process. （5） In the formula, Represents a production line-level logic model. Represents all production units in the production line. This represents the signal logic relationships between all units and devices. A Gantt chart representation of the simulation results of the production process.
2. The production line modeling method based on a multi-layer logic model according to claim 1, characterized in that, The device-level logic model described in S1 employs object-oriented encapsulation, inheritance, and polymorphism mechanisms to support the model's extensibility and flexibility.
3. The production line modeling method based on a multi-layer logic model according to claim 2, characterized in that, The functional requirements of the production line include dividing the equipment into three categories: storage units, production units, and detection units, and constructing the internal logic based on a state-event-behavior driven mapping relationship. Simultaneously, based on the state-event-behavior driven mapping relationship, the internal logic is constructed, as shown in the following formula: （3） In the formula, S(t) represents the state of the device at time t, E(t) represents the event of the device at time t, and the state S(t+1) of the device at the next time step is determined by both S(t) and E(t), with the mapping function f. （4） The events of the device at time t are affected by the current state, and the mapping function between them is g.
4. The production line modeling method based on a multi-layer logic model according to claim 3, characterized in that, Each unit-level logic model can independently perform a specific function, relying on external signal input and providing feedback, and its internal logic is not affected by external signals.
5. The production line modeling method based on a multi-layer logic model according to claim 4, characterized in that, The Gantt chart is automatically generated through an event-driven mechanism and includes the following steps: 3.1 Objects and Input Static Data (1) Equipment set, （6） E represents all the equipment, the total number of equipment is N, and the processing time of the i-th equipment is... The equipment capacity is 1. (2) Signal / connection configuration, Each device has a set of allowed inbound triggers and outbound destinations: （7） in It means: if and only if it comes from the device The signal arrives and enters the device When the event queue is in the device You can start processing; after processing is complete, press... Forward to the designated destination. It can be a single target or a set of targets, indicating parallel output. (3) Transportation time, （8） Indicates from device The signal conveyor belt sends a signal to the equipment after the transport is completed. The time delay, (4) Testing equipment, A detection / decision unit with random flow splitting is denoted as... ,for Define a set of result categories and their distribution as follows: （9）， This refers to the signal / connection configuration for detecting device x given a generated random number r, where q refers to the corresponding result category. This corresponds to the upper limit of random values for that category. This is the lower bound of random values for that category. This indicates that the generated random value r is in the range of 0 to 1. (5) Initial signal: Several initial events are placed into the event queue of the initial device. 3.2 State variables, dynamic data that changes over time. (1) Equipment status: （10） This represents the state of device i at time t. Initially, all device states are set to 1. (2) Event queue, （11） This represents the signal that device i needs to process at time t, and records the trigger source for each event. Where to go after completion , (3) Work-in-process records, （12） This indicates the signal that device i is processing at time t. It is the time when processing begins. This refers to the processing time. This refers to the source and destination of the signal. (4) Collection of semi-finished products in transit （13） This represents the semi-finished product being transported at time t. This indicates that the semi-finished product is transported from i to j via a conveyor belt or AGV. It is the time when transportation begins. It is the arrival time of the shipment. The arrival time is composed of the start time and the transit time. 3.3 Initialization and Operation (1) Initialization and self-update, Trigger the initial signal and load the event sequence of the first device. Then, it determines whether the update of the state variable in section 3.2 for each device has been completed in relation to the object and input in section 3.
1. It then determines the impact of the update of the state variable in section 3.2 on the signal queues of the remaining devices, until no further updates are made. (2) The signal arrives. Assuming the semi-finished product is And the current time Then the semi-finished product arrives at the corresponding equipment j （14） Find all matching signal / connection configurations for device j, complete the pairing, and load the corresponding signal / connection configurations. Add to event queue middle, (3) Event complete. like and Take the head of the event queue. Processing begins, equipment status changes to busy, and the corresponding record in the in-process record is completed: （15） Current time Once the processing is complete, the equipment returns to idle status, the work-in-process record becomes empty, and the completed semi-finished product is sent to the next piece of equipment. （16）。 6. The production line modeling method based on a multi-layer logic model according to any one of claims 1 to 5, characterized in that, The unified interface and modeling framework adopts a standardized construction architecture and signal interface specifications to support the rapid construction, scalability, reusability and cross-scenario application of multi-layer logic models, and automatically generates Gantt charts under complex working conditions to support simulation optimization.