Layered and domain-divided cooperative control method and system for production line design change propagation

Abstract

The invention relates to the technical field of production lines, and provides a production line design change propagation layering and domain division cooperative control method and system, and the method comprises the steps: carrying out the layering and domain division based on the layering structure of a component layer, an equipment layer and a production line layer in a production line, and the domain division dimensions of a configuration domain, a behavior domain, a control domain and an execution domain; constructing a parameter correlation model of a component layer-equipment layer and a parameter correlation model of the equipment layer-production line layer; based on the parameter correlation model, defining a propagation path of parameter change, and setting a change blocking rule when the equipment layer parameter change causes a production line layer conflict; through linkage verification of a configuration domain and a behavior domain, system state accessibility after parameter change is verified, and through closed-loop feedback of a control domain and an execution domain, control parameter tolerance is adjusted; simulation testing is carried out on the component layer, the equipment layer and the production line layer, and the influence of the changed parameters on the production line is verified.

Description

Technical Field

[0001] The present invention relates to the technical field of production lines, and in particular to a collaborative control method and system for layered and domain-based propagation of production line design changes. Background Art

[0002] Changes are ubiquitous in production line design. Faced with factors such as enterprise technology upgrades, product replacements, and equipment changes, the production line will have to undergo design changes. Design changes will affect any information generated during the production line development process. Due to the complex relationship between different design dimensions, even minor changes to a single design item or component may cause the design change to propagate among the components of the entire production line.

[0003] Currently, faster product updates and shorter product design cycles have become key factors in corporate market competition. Flexible production lines, with their diverse, small-batch production capabilities, enable rapid adjustments to production processes and tasks to meet the flexibility and rapid changes in market demand. With the continuous updating and upgrading of products and equipment, the issue of risk management associated with flexible production lines has become increasingly prominent. In existing production lines, uncontrollable design changes can prevent the production line from completing the transformation within the specified timeframe, hindering product production and processing, and impacting order delivery dates. This can result in significant losses for the company and its stakeholders.

[0004] Risk assessment and analysis, as one of the existing control methods, suffers from being overly theoretical and difficult to adapt to the complexity and variability of actual production situations. Furthermore, establishing a good communication mechanism to ensure network coordination during the change propagation process remains limited and cannot fully address the issues and risks arising from design changes. Summary of the Invention

[0005] In response to the above-mentioned defects, the purpose of the present invention is to propose a collaborative control method and system for the hierarchical and domain-based propagation of production line design changes, aiming to achieve efficient propagation and precise regulation of parameter changes through hierarchical operations based on the component layer, equipment layer and production line layer, and collaborative operations with the configuration, behavior, control and execution domains.

[0006] To achieve this object, the present invention adopts the following technical solutions:

[0007] A collaborative control method for propagating design changes of a production line in a hierarchical and domain-specific manner, the collaborative control method comprising the following steps:

[0008] S1: Based on the hierarchical structure of the component layer, equipment layer, and production line layer in the production line, as well as the domain dimensions of configuration domain, behavior domain, control domain, and execution domain, a parameter association model between the component layer and the equipment layer and the production line layer is constructed;

[0009] S2: Based on the parameter correlation model, define the propagation path of parameter changes and set the change blocking rules when parameter changes at the equipment layer cause conflicts at the production line layer;

[0010] S3: Through the linkage verification between the configuration domain and the behavior domain, verify the reachability of the system state after parameter changes, and adjust the control parameter tolerance through the closed-loop feedback between the control domain and the execution domain;

[0011] S4: Conduct simulation tests at the component layer, equipment layer, and production line layer respectively to verify the impact of the parameters after parameter changes on the production line.

[0012] Preferably, in step S1, the construction of the component layer - equipment layer parameter correlation model includes:

[0013] Abstractly express the single actions and action sequences of the component layer based on the behavior dimension, abstractly express the actuator of the action based on the mechanical structure dimension, and abstractly express the control program of the component layer based on the behavior dimension;

[0014] Combine the action sequences of the component layer based on the behavior dimension and abstractly express the process action sequence of the equipment, construct the topological relationship between the component layers based on the configuration dimension, combine the control programs of the component layer according to the process action sequence of the equipment and abstractly express the control program sequence of the equipment, extract or abstractly characterize the performance indicators or features in the component layer from the equipment layer to obtain the component layer - equipment layer parameter correlation model.

[0015] Preferably, in step S1, the construction of the equipment layer - production line layer parameter correlation model includes:

[0016] Construct the overall topological structure of the production line layer based on the configuration dimension, combine the control programs of the equipment layer based on the control dimension and abstractly construct the total control program of the production line layer, extract or abstractly characterize the performance indicators and features in the equipment layer from the production line layer to obtain the equipment layer - production line layer parameter correlation model.

[0017] Preferably, the propagation path of the parameter change includes a longitudinal propagation path along the component layer, equipment layer, and production line layer.

[0018] Preferably, the change blocking rules set when parameter changes at the equipment layer cause conflicts at the production line layer include:

[0019] When parameter changes at the equipment layer cause parameter conflicts at the production line layer, freeze the propagation path and implement parameter updates and design changes in the order of the execution domain, control domain, behavior domain, and configuration domain.

[0020] Preferably, in step S3, it includes:

[0021] After the parameter change, load the state machine rules associated with the configuration domain parameter change from the behavior domain, and verify whether the state of the production line can reach the expected goal after the parameter change through formal methods;

[0022] Compare the deviation between the preset value and the actual value in the execution domain, and dynamically relax or tighten the control parameter tolerance according to the load in the execution domain.

[0023] Preferably, in step S4, it includes:

[0024] Establish a digital twin of the physical component, adjust the relevant data of the component, verify the failure mode of the component movement, simulate parameter mutation through a step signal or a sine wave excitation, and observe the dynamic characteristics.

[0025] Preferably, in step S4, it includes:

[0026] Establish a digital twin model of the equipment layer, and simulate the multi-axis linkage trajectory accuracy after the change of the robotic arm joint parameters;

[0027] Conduct multi-physical field coupling simulation on the processing equipment parameters to predict the change of the workpiece surface roughness.

[0028] Preferably, in step S4, it includes:

[0029] Use a simulation tool to build a discrete event model and define the interaction rules of the production line elements;

[0030] Input the changed equipment cycle time parameters, calculate the bottleneck workstations of the production line through event-driven simulation, and generate a CT distribution heat map;

[0031] Simulate the risk of in-process product accumulation after the change of material flow parameters, evaluate the range of production capacity fluctuation, and then trigger a production anomaly event to verify the emergency adjustment effect of the production line layer parameters.

[0032] A collaborative control system for hierarchical and domain-based propagation of production line design changes, the collaborative control system is applied to the collaborative control method as described above, and the collaborative control system includes:

[0033] A modeling module, which is used to build a parameter association model between the component layer and the equipment layer and between the equipment layer and the production line layer based on the hierarchical structure of the component layer, equipment layer and production line layer in the production line, and the domain dimensions of the configuration domain, behavior domain, control domain and execution domain;

[0034] A propagation control and management module, which is used to define the propagation path of parameter changes based on the parameter association model, and set the change blocking rules when conflicts occur in the production line layer due to parameter changes in the equipment layer;

[0035] The cross - domain collaborative control module is used to verify the reachability of the system state after parameter changes through the linkage verification between the configuration domain and the behavior domain, and adjust the control parameter tolerance through the closed - loop feedback between the control domain and the execution domain;

[0036] The simulation verification module is used to conduct simulation tests at the component layer, equipment layer, and production line layer respectively to verify the impact of the parameters after parameter changes on the production line.

[0037] One of the above - mentioned technical solutions has the following advantages or beneficial effects:

[0038] In the present invention, by constructing a parameter association model in layers and domains, the association relationships between each layer and domain are clearly presented, laying a foundation for effectively managing design changes; during the change propagation, based on this model, the propagation path of parameter changes is clarified, enabling the change impacts to spread in a predetermined order, facilitating early monitoring and management; at the same time, change blocking rules are set. When parameter changes at the equipment layer trigger conflicts at the production line layer, the propagation can be frozen in time and the parameters can be updated in the order of "execution domain → control domain → behavior domain → configuration domain" to prevent the conflict from expanding and ensure the stable operation of the production line; through the linkage verification between the configuration domain and the behavior domain, it is ensured that the system state after parameter changes can reach the expected state, timely discovering and adjusting the problems of incoordination between configuration and behavior, avoiding equipment failures and production chaos; the closed - loop feedback between the control domain and the execution domain optimizes the control parameter tolerance in real - time according to the feedback data of the execution domain, enabling the control system to adapt to the changes in the execution domain and maintaining stable and efficient production; finally, a complete change control system is formed, which can improve the efficiency and effect of design change management, reduce the risk cost, ensure the stable and efficient operation of the production line after changes, and ultimately enhance the market competitiveness of the enterprise. Description of the Drawings

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on the provided drawings.

[0040] Figure 1 is the flowchart of the collaborative control method for hierarchical and domain - based propagation of production line design changes provided by the embodiment of the present invention;

[0041] Figure 2 is the schematic diagram of the hierarchical and domain - based structure of the collaborative control method for hierarchical and domain - based propagation of production line design changes provided by the embodiment of the present invention;

[0042] Figure 3 is the schematic diagram of the structure of the collaborative control system for hierarchical and domain - based propagation of production line design changes provided by the embodiment of the present invention. Detailed Embodiments

[0043] Embodiments of the present invention will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are only used to explain the present invention and should not be construed as a limitation to the present invention.

[0044] In the description of the present invention, it should be understood that the orientation or positional relationships indicated by the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "axial", "radial", "circumferential", etc. are based on the orientation or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present invention.

[0045] In addition, the terms "first" and "second" are only used for descriptive purposes and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise stated, the meaning of "a plurality" is two or more.

[0046] In the description of the present invention, it should be noted that unless otherwise clearly defined and limited, the terms "installed", "connected" and "coupled" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection or an integral connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0047] A collaborative control method for hierarchical and domain-based propagation of production line design changes, as Figure 1 and Figure 2 shown, is a preferred embodiment of the present invention. The collaborative control method includes the following steps:

[0048] S1: Based on the hierarchical structure of the component layer, equipment layer and production line layer in the production line, and the domain dimensions of the configuration domain, behavior domain, control domain and execution domain, construct a parameter correlation model between the component layer - equipment layer and the equipment layer - production line layer;

[0049] In step S1, by performing hierarchical and domain-based processing on the production line, a parameter correlation model between different levels is established, based on which effective control of design changes to the production line is achieved. Since the production line is a complex system, containing numerous components, equipment, etc. with different functions and roles, and there are complex mutual relationships among them, through the hierarchical and domain-based method, it can be decomposed into more manageable parts, so as to more accurately grasp the mutual influence and correlation among various parts during design changes, providing a basis for subsequent change propagation control and coordinated regulation.

[0050] Among them, the component layer is the smallest unit of the production line, such as sensors, actuators, etc., responsible for the basic execution and feedback of parameters, and is the basic element constituting the entire production line. The changes in its state and parameters directly affect the functions and operations of the upper-level equipment; the equipment layer consists of independent devices composed of multiple components, such as robotic arms, machining centers, etc., responsible for local parameter coordination and optimization, playing a connecting role between the upper and lower levels, both managing and coordinating the component layer and providing local production capacity and functional support to the production line layer; the production line layer is the integrated system of the entire production line, responsible for formulating global parameter strategies and cross-equipment coordination, overall planning the operation and management of the entire production line, and ensuring the coordinated work of each equipment to complete production tasks. The configuration domain involves the physical scheme planning and configuration of the production line, including equipment selection, layout, connection methods, etc., which determines the overall architecture and layout of the production line; the behavior domain focuses on the action coordination and planning of each work unit and equipment, ensuring the smooth and efficient production process. Its reasonable planning can avoid waiting and conflicts in production and improve production efficiency; the control domain aims to achieve information exchange and coordinated control between equipment and workstations, enabling the production line to have the capabilities of automatic monitoring, real-time scheduling, and feedback adjustment through constructing control networks, data acquisition and analysis, decision-making instruction issuance, etc., enhancing the flexibility and response ability of production; the execution domain is the core of production line optimization, achieving the optimal operation of the production process through the optimization of the whole-line drive engine, involving the optimization of production scheduling, equipment maintenance, energy management, quality control, etc., to achieve the best balance of production efficiency, quality, and cost. Hierarchical and domain-based division can clearly define the specific content and objects of the hierarchical division and dimension division based on which the parameter correlation model is constructed, providing a basis and foundation for accurately establishing the parameter correlation between different levels, enabling the constructed model to comprehensively and accurately reflect the mutual relationships and influence mechanisms among various parts in the production line, and thus better supporting subsequent design change propagation control and coordinated regulation work.

[0051] S2: Based on the parameter correlation model, define the propagation path of parameter changes, and set the change blocking rule when a parameter change in the equipment layer causes a conflict in the production line layer;

[0052] In step S2, based on the already constructed parameter association model, define the propagation path of parameter changes between different levels of the production line, and set corresponding blocking rules for possible change conflict situations. Since design changes often spread along the association relationships between the component layer, equipment layer, and production line layer in the production line, clarifying the propagation path allows people to understand in advance the scope and order that the changes may affect. Setting change blocking rules is to be able to take timely measures when detecting conflicts caused by changes, avoid the further expansion and deterioration of conflicts, ensure the normal operation and stability of the production line, and can ensure that during the design change process, the impact of the changes can be propagated along the predetermined path and can be effectively controlled in case of problems, thereby realizing the effective management and regulation of the change propagation process.

[0053] Among them, the propagation path refers to the path through which design changes are transmitted between the component layer, equipment layer, and production line layer of the production line in a certain order and rule. It can clarify the propagation direction and sequence of changes between different levels, enabling the impact of changes to spread orderly in the production line, facilitating people to monitor and manage in advance. For example, when a parameter in the component layer changes, according to the propagation path, it can be known that this change will first affect the equipment layer where it is located, and then may further affect the operation of the entire production line layer. In this way, relevant personnel can check and adjust each level in sequence according to this path. The change blocking rule is a control strategy and measure taken when detecting that a parameter change in the equipment layer causes a parameter conflict in the production line layer. Its role is to timely prevent the further spread of the change and avoid the irreparable losses caused by the expansion of the conflict in the production line layer. For example, when a change in a parameter in the equipment layer causes incompatibility or contradiction in the production plan, process flow, etc. of the production line layer, the change blocking rule will trigger corresponding mechanisms, such as pausing the spread of the change, sending an alarm to prompt relevant personnel to handle it, etc., thereby ensuring the safety and stability of the production line during the change process. Generally speaking, it can provide specific operation bases and means for the propagation control of design changes, making the change propagation process no longer disorderly and uncontrollable, but able to proceed under the preset path and rules, thereby effectively improving the accuracy and effectiveness of change management and ensuring the smooth operation of the production line during the change process.

[0054] S3: Verify the reachability of the system state after parameter changes through the linkage verification between the configuration domain and the behavior domain, and adjust the control parameter tolerance through the closed-loop feedback between the control domain and the execution domain;

[0055] Specifically, verification is carried out based on the linkage relationship between the configuration domain and the behavior domain to verify whether the system can reach the expected operating state after parameter changes. At the same time, the closed-loop feedback mechanism between the control domain and the execution domain is used to dynamically adjust the tolerance of the control parameters. There is a close mutual relationship between the configuration domain and the behavior domain. Changes in the configuration often directly affect the execution and performance of the behavior, and vice versa. Through the linkage verification, it can be timely found whether the system state meets the expected requirements after parameter changes. If not, the problem can be quickly located and adjusted. The closed-loop feedback between the control domain and the execution domain is to evaluate the effect of the control parameters in real time based on the actual operation data fed back by the execution domain, and optimize and adjust the control parameters according to the evaluation results to ensure that the control system can always adapt to the changes in the execution domain, maintain the stable operation and efficient production of the entire production line.

[0056] The linkage verification between the configuration domain and the behavior domain refers to the process of jointly checking and verifying the configuration parameters such as equipment selection, layout, and connection method involved in the configuration domain and the behavior parameters such as equipment action sequence, task scheduling, and action timing in the behavior domain after parameter changes. Its role is to ensure that the adjustment of the configuration can correctly guide the behavior execution of the equipment, enable the equipment to accurately complete the corresponding production tasks according to the new configuration requirements, and avoid problems such as equipment failures and production chaos caused by the mismatch between the configuration and the behavior. For example, when the configuration of a certain equipment is adjusted, such as replacing some components or changing the layout of the equipment, through the linkage verification, it can be checked whether the action behavior of the equipment under the new configuration still meets the requirements of the production process and whether the coordination between actions is guaranteed. The closed-loop feedback between the control domain and the execution domain refers to the process of evaluating the effect of the control parameters based on the actual operation data (such as the position tracking error of the equipment, the temperature fluctuation range, the motor current, etc.) fed back by the execution domain after the control system issues a control command and drives the equipment in the execution domain to run, and dynamically adjusting the control parameters accordingly. Its role is to enable the control system to adapt to various changes in the execution domain in real time, such as the adjustment of production tasks and the change of equipment load. By continuously optimizing the tolerance range of the control parameters, it is ensured that the control system can always accurately control the production equipment in the best state, thereby improving the operation efficiency of the equipment and the stability of the production process. For example, when the motor current fed back by the execution domain is too large, it may mean that the equipment load is heavy. At this time, through the closed-loop feedback mechanism, the tolerance of the control parameters can be appropriately relaxed to avoid the control system frequently issuing adjustment commands and causing unstable operation of the equipment, while ensuring that the equipment can operate normally under heavy load.

[0057] S4: Conduct simulation tests at the component layer, equipment layer, and production line layer respectively to verify the impact of the parameters after parameter changes on the production line.

[0058] Specifically, by conducting simulation tests on the component layer, equipment layer, and production line layer of the production line respectively, simulating the actual operating conditions of each layer after parameter changes, the impact of the changes on the entire production line can be evaluated. Simulation tests can predict in advance various consequences that may be brought about by the changes without actually interrupting production, including changes in production efficiency, coordination problems between equipment, fluctuations in product quality, etc. By conducting simulation tests at different levels, the specific impact of the changes in each layer can be deeply understood, so as to promptly discover problems and take corresponding adjustment measures to ensure that the design changes can achieve the expected effects and will not have a serious impact on the normal operation of the production line. This helps to comprehensively evaluate and optimize the change plan before actually implementing the changes, reducing the risks and costs of change implementation.

[0059] Preferably, in step S1, the construction of the component layer-equipment layer parameter association model includes:

[0060] Abstractly express the single actions and action sequences of the component layer based on the behavior dimension, abstractly express the actuator of the action based on the mechanical structure dimension, and abstractly express the control program of the component layer according to the behavior dimension;

[0061] Combine the action sequences of the component layer based on the behavior dimension and abstractly express the process action sequence of the equipment, construct the topological relationship between the component layers based on the configuration dimension, combine the control programs of the component layer according to the process action sequence of the equipment and abstractly express the control program sequence of the equipment, extract or abstractly characterize the performance indicators or features in the component layer from the equipment layer, and obtain the parameter association model of the component layer-equipment layer.

[0062] Specifically, since the component-level parameter association model focuses on expressing the process actions, actuators of actions, and process control programs of components, first abstract the single actions and action sequences of components from the action behavior dimension to clarify the function and execution order of each action; abstract the mechanism that performs these actions from the mechanical structure dimension to determine its physical composition and motion characteristics; and abstract the control program of the component according to the behavior dimension to describe how the control program drives and manages the execution of actions. The equipment-level parameter association model focuses on expressing the process action sequence, component topology, control program, and performance indicators and features of the equipment. On this basis, combine the action sequences of multiple components to form the process action sequence at the equipment level, construct the topological relationship between components to reflect the spatial layout and connection method of components in the equipment, and then combine the control programs of components to form the control program sequence of the equipment. At the same time, extract the key performance indicators and features in the components as the representation of the equipment performance, and finally realize the association of parameters between the component layer and the equipment layer.

[0063] There are various implementation methods for constructing the component-equipment level parameter association model, which can be selected and combined according to different production scenarios, data acquisition conditions, and technical means. A common implementation method is the manual modeling method based on on-site research and data analysis. First, organize professional engineers to go deep into the production site to conduct detailed research and record the process actions, actuators, and control programs of each component, including information such as the execution sequence of actions, time parameters, kinematic and dynamic characteristics of the mechanism, and the logical flow of the control program. Then, use this data to manually construct the component-level parameter association model in a dedicated modeling software (such as MATLAB, Simulink, etc.) to abstractly express the action behavior, mechanical structure, and control logic of each component. Next, according to the assembly drawings and process documents of the equipment, determine the topological relationship between components, and integrate multiple component-level models according to the actual assembly relationship to form the equipment-level parameter association model. During the modeling process, continuously adjust the model parameters through comparison and verification with on-site production data to ensure the accuracy and reliability of the model.

[0064] Preferably, in step S1, the construction of the equipment layer-production line layer parameter association model includes:

[0065] Construct the overall topological structure of the production line layer based on the configuration dimension, combine the control programs of the equipment layer based on the control dimension and abstractly construct the overall control program of the production line layer, extract or abstractly represent the performance indicators and characteristics in the equipment layer at the production line layer to obtain the equipment layer-production line layer parameter association model.

[0066] The production line-level parameter association model focuses on expressing the overall topological structure of the production line, the overall control program, the production line performance indicators and characteristics. Therefore, first construct the overall topological structure of the production line from the configuration dimension to clarify the layout and connection relationship of each equipment in the production line; combine the control programs of the equipment from the control dimension to form the overall control program of the production line to achieve centralized control and coordinated management of the entire production line; and extract the key performance indicators and characteristics in the equipment layer at the production line level to form the multi-dimensional parameter association skeleton model of the production line. Among them, multiple equipment-level parameter association models form the equipment-production line-level parameter association model according to topological docking.

[0067] Among them, the configuration dimension involves the physical scheme planning and configuration of the production line, including equipment selection, layout, connection method, transmission path, workstation configuration, etc., which determines the overall architecture and layout of the production line; the control dimension focuses on information exchange and coordinated control between equipment and workstations, and realizes automatic monitoring, real-time scheduling, and feedback adjustment of the production line through constructing a control network, data acquisition and analysis, decision-making and instruction issuing, etc.; the performance indicators and characteristics are key parameters used to characterize the operation effect and characteristics of the equipment, such as the processing accuracy, production efficiency, failure rate of the equipment, etc. Extracting these indicators at the production line layer can reflect the overall performance and operating status of the production line.

[0068] Preferably, the propagation path of the parameter change includes a longitudinal propagation path along the component layer, the equipment layer, and the production line layer.

[0069] Specifically, by analyzing the parameter dependency relationships and mutual influence mechanisms among the component, equipment, and production line levels, the propagation path of the parameter change among the longitudinal levels is constructed. Starting from the component layer, the impact of each component parameter change on its affiliated equipment layer is clarified, and then the scope of influence of the equipment layer parameter change on the entire production line layer is further analyzed, forming a tree-like propagation structure, which can provide an intuitive basis for subsequent accurately positioning the change impact points, evaluating the change risks, and formulating corresponding control strategies, ensuring that the change can propagate within the controlled range along the predetermined path and avoiding the spread of unpredictable risks.

[0070] Preferably, the change blocking rules when the parameter change in the equipment layer triggers a conflict in the production line layer include:

[0071] When the parameter change in the equipment layer triggers a parameter conflict in the production line layer, the propagation path is frozen, and parameter updates and design changes are implemented in the order of the execution domain, the control domain, the behavior domain, and the configuration domain in sequence.

[0072] Specifically, through the predefined logic and sequence, when the parameter change in the equipment layer may trigger a parameter conflict in the production line layer, the further propagation of the change is promptly frozen to avoid the expansion of the conflict. At the same time, considering the cost differences of changes in different dimensions comprehensively, parameter updates and design changes are implemented in the priority order of "execution domain → control domain → behavior domain → configuration domain" in sequence, so as to effectively manage the change at a lower cost, reduce the impact of the change on the entire production line, and ensure the stability and reliability of the production process.

[0073] Among them, the parameter change in the equipment layer refers to the change of the parameters in the equipment layer due to reasons such as design improvement, equipment maintenance, or production requirement adjustment; the parameter conflict in the production line layer refers to the situation where, after the parameter change in the equipment layer, it is incompatible or contradictory to the existing parameter settings in the production line layer; the freezing of the propagation path means that when a parameter conflict is detected, the further transmission of the change on the longitudinal propagation path is immediately suspended to prevent the conflict from spreading to more levels; the dimension change cost refers to the resource consumption and economic cost generated by implementing parameter updates and design changes in different dimensions, and the configuration dimension involves the adjustment of physical equipment and layout changes, usually with the highest cost.

[0074] Preferably, in step S3, it includes:

[0075] After the parameter change, the state machine rules associated with the parameter change in the configuration domain are loaded from the behavior domain, and whether the state of the production line can reach the expected goal after the parameter change is verified through a formal method;

[0076] By establishing a dynamic association between the configuration domain and the behavior domain, when the basic value of a parameter changes, the state machine rules predefined in the behavior domain are automatically invoked. The formal method (such as Petri net reachability analysis) is used to verify the system state after the parameter change, ensuring that the system can transition from the current state to the expected target state, thus guaranteeing the normal operation of the production line and the smooth completion of production tasks.

[0077] Compare the deviation between the preset value and the actual value in the execution domain (such as position tracking error, temperature fluctuation range), and dynamically relax or tighten the control parameter tolerance according to the load in the execution domain (such as motor current). This closed-loop control mechanism can ensure that the control system always makes timely adjustments according to the actual operation of the execution domain to maintain the stable operation and efficient production of the production line.

[0078] Preferably, in step S4, it includes:

[0079] Establish a digital twin of the physical component, adjust the component-related data, verify the failure mode of the component movement, simulate parameter mutations through step signals or sine wave excitations, and observe the dynamic characteristics.

[0080] By constructing an accurate digital twin of the physical component and adjusting the relevant parameters in the virtual environment, the operating state of the component under different working conditions can be simulated, thereby verifying its failure mode of movement. Using step signals or sine wave excitations to simulate parameter mutations can stimulate the dynamic response characteristics of the component, and then deeply analyze its dynamic performance.

[0081] A step signal is a mutated input signal whose amplitude changes stepwise in a short time, used to simulate a sudden interference or load change received by the system; a sine wave excitation is a periodically changing input signal, used to simulate the response of the system under periodic load or vibration conditions.

[0082] Preferably, in step S4, it includes:

[0083] Establish a digital twin model of the equipment layer and simulate the multi-axis linkage trajectory accuracy after the change of the robotic arm joint parameters;

[0084] Conduct multi-physics field coupling simulation on the processing equipment parameters to predict the change of the workpiece surface roughness.

[0085] Specifically, by constructing a digital twin model of the equipment layer, the multi-axis linkage trajectory accuracy after the change of the robotic arm joint parameters can be accurately simulated, and the influence of the change of the processing equipment parameters on the workpiece surface roughness can be predicted by using the multi-physics field coupling simulation technology. The digital twin model can map the state and behavior of the physical equipment in real time, and combined with the simulation technology, the effect of the parameter change can be evaluated in the virtual environment, so as to discover potential problems in advance and optimize the equipment performance to ensure efficient operation and processing quality in actual production.

[0086] Preferably, in step S4, it includes:

[0087] Using a simulation tool to build a discrete event model and define the interaction rules of production line elements;

[0088] Input the changed equipment cycle time parameters, and calculate the production line bottleneck workstations through event-driven simulation to generate a CT distribution heat map;

[0089] Simulate the in-process inventory accumulation risk after the change of material flow parameters, evaluate the production capacity fluctuation range, and then trigger a production anomaly event to verify the emergency adjustment effect of the production line layer parameters.

[0090] The simulation tool refers to the software environment used to build and run discrete event models. These tools provide rich modeling and analysis functions, such as PlantSimulation or AnyLogic, etc., which enable users to create complex production system models; the discrete event model is a model that simulates the system behavior in an event-driven manner. It focuses on the state change events that occur in the system, such as equipment startup, stop, material arrival, etc., and is suitable for simulating production processes with randomness and discreteness; the interaction rules of production line elements define the interactions and information exchanges between various elements in the production line (such as workstations, buffer areas, AGVs, etc.), including material transfer rules, equipment startup conditions, etc. These rules determine the flow mode of materials and information in the production line; the equipment cycle time parameter refers to the time required for the equipment to complete one production cycle, which is a key factor affecting the production line efficiency; the bottleneck workstation refers to the workstation that restricts the overall production efficiency on the production line, usually the link with the longest processing time or the most prone to congestion; the CT distribution heat map is a chart that intuitively shows the production cycle time distribution of each workstation. Through visualization means such as color coding or height, it highlights the bottleneck workstations; the in-process inventory accumulation risk refers to the possibility of excessive in-process inventory accumulation on the production line due to changes in material flow parameters (conveyor speed, buffer capacity), which may cause production delays or quality problems; the production anomaly event refers to unexpected situations that occur during the production process, such as equipment failures, material shortages, etc. These events may have a significant impact on production efficiency and product quality.

[0091] A collaborative control system for hierarchical and domain-based propagation of production line design changes, as Figure 3 shown, the collaborative control system is applied to the collaborative control method as described above, and the collaborative control system includes:

[0092] A modeling module, used to build a parameter correlation model between the component layer and the equipment layer and between the equipment layer and the production line layer based on the hierarchical structure of the component layer, equipment layer, and production line layer in the production line, as well as the domain dimensions of the configuration domain, behavior domain, control domain, and execution domain;

[0093] A propagation control module, configured to define a propagation path for parameter changes based on the parameter association model, and set a change blocking rule when a parameter change at the equipment layer causes a conflict at the production line layer;

[0094] A cross-domain collaborative regulation module, configured to verify the reachability of the system state after parameter changes through the linkage verification between the configuration domain and the behavior domain, and adjust the control parameter tolerance through the closed-loop feedback between the control domain and the execution domain;

[0095] A simulation verification module, configured to perform simulation tests at the component layer, the equipment layer, and the production line layer respectively to verify the impact of the parameters after parameter changes on the production line.

[0096] In the description of this specification, the descriptions referring to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0097] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the claims and their equivalents.

Claims

1. A collaborative control method for hierarchical and domain-based propagation of production line design changes, characterized in that, The collaborative control method includes the following steps: S1: Based on the hierarchical structure of the component layer, equipment layer, and production line layer in the production line, and the domain dimensions of the configuration domain, behavior domain, control domain, and execution domain, construct a parameter association model between the component layer and the equipment layer and between the equipment layer and the production line layer; S2: Based on the parameter association model, define the propagation path of parameter changes, and set a change blocking rule when parameter changes in the equipment layer cause conflicts in the production line layer; S3: Through the linkage verification between the configuration domain and the behavior domain, verify the reachability of the system state after parameter changes, and adjust the control parameter tolerance through the closed-loop feedback between the control domain and the execution domain; S4: Conduct simulation tests on the component layer, equipment layer, and production line layer respectively to verify the impact of the parameters after parameter changes on the production line.

2. The collaborative control method according to claim 1, wherein In step S1, the construction of the parameter association model between the component layer and the equipment layer includes: Abstractly express the single actions and action sequences of the component layer based on the behavior dimension, abstractly express the actuators of the actions based on the mechanical structure dimension, and abstractly express the control programs of the component layer according to the behavior dimension; Combine the action sequences of the component layer based on the behavior dimension and abstractly express the process action sequences of the equipment, construct the topological relationship between the component layers based on the configuration dimension, combine the control programs of the component layer according to the process action sequences of the equipment and abstractly express the control program sequences of the equipment, extract or abstractly represent the performance indicators or characteristics in the component layer from the equipment layer to obtain the parameter association model between the component layer and the equipment layer.

3. The collaborative control method according to claim 1, characterized in that, In step S1, the construction of the parameter association model between the equipment layer and the production line layer includes: Construct the overall topological structure of the production line layer based on the configuration dimension, combine the control programs of the equipment layer based on the control dimension and abstractly construct the total control program of the production line layer, extract or abstractly represent the performance indicators and characteristics in the equipment layer from the production line layer to obtain the parameter association model between the equipment layer and the production line layer.

4. The collaborative control method according to claim 1, wherein The propagation path of the parameter changes includes the longitudinal propagation path along the component layer, equipment layer, and production line layer.

5. The collaborative control method according to claim 1, characterized in that, The change blocking rule set when parameter changes in the equipment layer cause conflicts in the production line layer includes: When parameter changes in the equipment layer cause parameter conflicts in the production line layer, freeze the propagation path, and implement parameter updates and design changes in the order of the execution domain, control domain, behavior domain, and configuration domain.

6. The collaborative control method according to claim 1, wherein In step S3, it includes: After parameter changes, load the state machine rules associated with the parameter changes in the configuration domain from the behavior domain, and verify whether the state of the production line after parameter changes can reach the expected goal through formal methods; Compare the deviation between the preset value and the actual value in the execution domain, and dynamically relax or tighten the control parameter tolerance according to the load in the execution domain.

7. The collaborative control method according to claim 1, wherein In step S4, it includes: Establish a digital twin of the physical component, adjust the relevant component data, verify the failure mode of the component movement, simulate parameter mutations through step signals or sine wave excitations, and observe the dynamic characteristics.

8. The collaborative control method according to claim 1, characterized in that, In step S4, it includes: Establish a digital twin model of the equipment layer, and simulate the multi-axis linkage trajectory accuracy after the change of the robotic arm joint parameters; Conduct multi-physical field coupling simulation on the processing equipment parameters to predict the change of the workpiece surface roughness.

9. The collaborative control method according to claim 1, wherein In step S4, it includes: Use a simulation tool to construct a discrete event model and define the interaction rules of the production line elements; Input the changed equipment cycle time parameters, calculate the bottleneck workstations of the production line through event-driven simulation, and generate a CT distribution heat map; Simulate the in-process inventory accumulation risk after the change of material flow parameters, evaluate the production capacity fluctuation range, and then trigger production anomaly events to verify the emergency adjustment effect of the production line layer parameters.

10. A collaborative control system for hierarchical and domain-based propagation of production line design changes, characterized in that, The collaborative control system is applied to the collaborative control method described in any one of claims 1-9, and the collaborative control system includes: A modeling module, configured to construct a parameter correlation model between the component layer and the equipment layer and between the equipment layer and the production line layer based on the hierarchical structure of the component layer, the equipment layer, and the production line layer in the production line, as well as the domain dimensions of the configuration domain, the behavior domain, the control domain, and the execution domain; A propagation control module, configured to define the propagation path of parameter changes based on the parameter correlation model and set change blocking rules when conflicts occur in the production line layer due to parameter changes in the equipment layer; A cross-domain collaborative regulation module, configured to verify the reachability of the system state after parameter changes through the linkage verification between the configuration domain and the behavior domain, and adjust the control parameter tolerance through the closed-loop feedback between the control domain and the execution domain; A simulation verification module, configured to perform simulation tests on the component layer, the equipment layer, and the production line layer respectively to verify the impact of the parameters after parameter changes on the production line.

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