A collaborative control method and system for production line design change propagation stratification and domain division

Abstract

The application relates to the technical field of production lines, and proposes a collaborative control method and system for production line design change propagation, stratification and domain division. The collaborative control method comprises the following steps: based on the stratified structure of a component layer, an equipment layer and a production line layer in a production line, and the stratified dimensions of a configuration domain, a behavior domain, a control domain and an execution domain, a parameter correlation model of the component layer-equipment layer and the equipment layer-production line layer is constructed; based on the parameter correlation model, a propagation path of parameter change is defined, and a change blocking rule when a parameter change in the equipment layer triggers a production line layer conflict is set; the system state reachability after parameter change is verified through linkage verification of the configuration domain and the behavior domain, and the control parameter tolerance is adjusted through closed-loop feedback of the control domain and the execution domain; simulation tests are respectively carried out on the component layer, the equipment layer and the production line layer to verify the influence of the changed parameters on the production line.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of production line, in particular to a collaborative control method and system for production line design change propagation layering and domain division. BACKGROUND

[0002] Changes are ubiquitous in production line design. In the face of factors such as enterprise technology upgrading, product replacement, and equipment change, the production line will have to be redesigned. Design changes will affect any information generated during the production line development process. Due to the complex relationship between different design dimensions, even a small change in a single design item or component can cause design changes to propagate among the components of the entire production line.

[0003] Currently, faster product updates and shorter product design cycles have become key elements of enterprise market competition. Flexible production lines can quickly adjust production processes and production tasks to meet market demand flexibility and rapid changes due to their multi-variety and small-batch production characteristics. With the continuous updating and upgrading of products and equipment, the change risk control problem brought about by flexible production lines is increasingly prominent. In existing production lines, uncontrolled design changes can cause the production line to fail to complete the modification within the established time, unable to complete the production and processing of products, affecting the order delivery date, which will cause significant losses to the enterprise and related stakeholders.

[0004] Risk assessment and analysis, as one of the existing control methods, has the disadvantage of being overly theoretical and difficult to meet the complexity and variability of actual production conditions. In addition, establishing a good communication mechanism to ensure the coordination of the network during the change propagation process, this method still has limitations and cannot completely address the problems and risks caused by design changes. SUMMARY

[0005] To overcome the above-mentioned defects, the present application aims to provide a collaborative control method and system for production line design change propagation layering and domain division, which aims to achieve efficient propagation and accurate control of parameter changes through collaborative operation based on the hierarchical operation of the component layer, equipment layer, and production line layer, and the division of configuration, behavior, control, and execution.

[0006] To achieve this purpose, the present application adopts the following technical solutions:

[0007] A collaborative control method for production line design change propagation layering and domain division, 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, and the domain dimension of the configuration domain, behavior domain, control domain, and execution domain, a parameter association model of the component layer-equipment layer and equipment layer-production line layer is constructed;

[0009] S2: defining a propagation path of parameter change based on the parameter correlation model, and setting a change blocking rule when parameter change at the equipment layer triggers a conflict at the production line layer;

[0010] S3: verifying the system state reachability after parameter change through linkage verification of the configuration domain and the behavior domain, and adjusting the control parameter tolerance through closed-loop feedback of the control domain and the execution domain;

[0011] S4: performing simulation testing at the component layer, the equipment layer and the production line layer respectively to verify the influence of the changed parameters on the production line.

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

[0013] abstractly expressing the single action and the action sequence of the component layer based on the behavior dimension, abstractly expressing the execution mechanism of the action based on the mechanical structure dimension, and abstractly expressing the control program of the component layer based on the behavior dimension;

[0014] combining the action sequence of the component layer based on the behavior dimension and abstractly expressing the process action sequence of the equipment, constructing the topological relationship between the component layers based on the configuration dimension, combining the control program of the component layer based on the process action sequence of the equipment and abstractly expressing the control program sequence of the equipment, extracting or abstractly representing the performance indicators or features in the component layer from the equipment layer, and obtaining 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 comprises:

[0016] constructing the overall topological structure of the production line layer based on the configuration dimension, combining the control program of the equipment layer based on the control dimension and abstractly constructing the total control program of the production line layer, extracting or abstractly representing the performance indicators and features in the equipment layer at the production line layer, and obtaining the equipment layer-production line layer parameter correlation model.

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

[0018] Preferably, the change blocking rule set when the parameter change at the equipment layer triggers a conflict at the production line layer comprises:

[0019] When the parameter change at the equipment layer triggers a parameter conflict at the production line layer, the propagation path is frozen, and parameter update and design change are implemented in the order of the execution domain, the control domain, the behavior domain and the configuration domain.

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

[0021] After the parameter change, the state machine rules associated with the configuration domain parameter change are loaded from the behavior domain, and the state of the production line after the parameter change is verified by a formal method to determine whether it can reach the expected target.

[0022] The deviation of the preset value and the actual value of the execution domain is compared, and the control parameter tolerance is dynamically relaxed or tightened according to the load of the execution domain.

[0023] Preferably, in step S4, the following steps are included:

[0024] A digital twin of the physical component is established, the component-related parameters are adjusted, the failure mode of the component motion is verified, parameter mutations are simulated through step signals or sine waves, and the dynamic characteristics are observed.

[0025] Preferably, in step S4, the following steps are included:

[0026] A digital twin model of the equipment layer is established, and the multi-axis linkage trajectory accuracy after the parameter change of the robot arm joint is simulated.

[0027] The processing equipment parameters are subjected to multi-physics field coupling simulation, and the workpiece surface roughness variation is predicted.

[0028] Preferably, in step S4, the following steps are included:

[0029] A discrete event model is constructed using a simulation tool, and interaction rules of the production line elements are defined.

[0030] The changed equipment tact parameters are input, the production line bottleneck workstations are calculated through event-driven simulation, and a CT distribution thermodynamic map is generated.

[0031] The work-in-process accumulation risk after the change of the material flow parameter is simulated, the production capacity fluctuation range is evaluated, and then a production abnormal event is triggered to verify the emergency adjustment effect of the production line layer parameters.

[0032] A production line design change propagation hierarchical and domain collaborative control system, the collaborative control system is applied to the collaborative control method as described above, and the collaborative control system comprises:

[0033] A modeling module for constructing a parameter correlation model of the component layer-equipment layer and the equipment layer-production line layer based on the hierarchical structure of the component layer, the equipment layer and the production line layer in the production line, and the dimension of the configuration domain, the behavior domain, the control domain and the execution domain.

[0034] A propagation control module for defining a propagation path of the parameter change based on the parameter correlation model, and setting a change blocking rule when the equipment layer parameter change causes a production line layer conflict.

[0035] The cross-domain cooperative regulation module is used for verifying the system state reachability after the parameter change through the linkage verification of the configuration domain and the behavior domain, and adjusting the control parameter tolerance through the closed-loop feedback of the control domain and the execution domain.

[0036] The simulation verification module is used for performing simulation tests at the component layer, the equipment layer and the production line layer respectively, and verifying the influence of the changed parameters on the production line.

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

[0038] The parameter correlation model is constructed by layering and domain partitioning, the correlation relationship of each layer and domain is clearly presented, and the foundation for effectively managing and controlling design changes is laid. When the change is propagated, the propagation path of the parameter change is determined based on the model, the change influence is diffused in the predetermined order, and the change is monitored and managed in advance. Meanwhile, the change blocking rules are set, when the equipment layer parameter change causes the production line layer conflict, the propagation is frozen in time and the parameters are updated in the order of "execution domain -> control domain -> behavior domain -> configuration domain", the conflict is prevented from expanding, and the stable operation of the production line is ensured. The linkage verification of the configuration domain and the behavior domain ensures that the system state after the parameter change reaches the expected state, the configuration and behavior incoordination problem is found and adjusted in time, and the equipment failure and production confusion are avoided. The closed-loop feedback of the control domain and the execution domain optimizes the control parameter tolerance in real time according to the feedback data of the execution domain, the control system adapts to the change of the execution domain, the stable and efficient production is maintained, and finally the complete change control system is formed. The design change management efficiency and effect are improved, the risk cost is reduced, the stable and efficient operation of the production line after the change is ensured, and the market competitiveness of the enterprise is finally enhanced. BRIEF DESCRIPTION OF DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the provided drawings.

[0040] Figure 1 is a flow chart of the production line design change propagation layering and domain partitioning cooperative control method provided by the embodiments of the present application;

[0041] Figure 2 is a hierarchical domain partitioning structure schematic diagram of the production line design change propagation layering and domain partitioning cooperative control method provided by the embodiments of the present application;

[0042] Figure 3 is a structure schematic diagram of the production line design change propagation layering and domain partitioning cooperative control system provided by the embodiments of the present application. DETAILED DESCRIPTION

[0043] Embodiments of the present application are described below in detail with reference to the accompanying drawings, wherein the same or similar components or components having the same or similar functions are denoted by the same or similar reference numerals throughout the drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application and cannot be understood as a limitation of the present application.

[0044] In the description of the present application, it should be understood that the orientation or positional relationship indicated by the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "axial", "radial", "circumferential" and the like are based on the orientation or positional relationship shown in the drawings, and are only for the purpose of facilitating the description of the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application.

[0045] In addition, the terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, unless otherwise specified, the meaning of "a plurality of" is two or more.

[0046] In the description of the present application, it should be noted that unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connection" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be directly connected, or indirectly connected through an intermediate medium, or it can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0047] A collaborative control method for production line design change propagation stratification and domain division, as shown in Figure 1 and Figure 2 A preferred embodiment of the present application, the collaborative control method comprises 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 stratification dimension of the configuration domain, behavior domain, control domain and execution domain, a parameter correlation model of the component layer-equipment layer and equipment layer-production line layer is constructed;

[0049] In step S1, by layering and domain processing the production line, a parameter association model between different levels is established, and effective control of the design change of the production line is realized on this basis. Since the production line is a complex system containing numerous components, equipment and other components with different functions and effects, and there is a complex interrelationship between them, they can be divided into more manageable parts through hierarchical and domain processing, so as to more accurately grasp the mutual influence and association of each part during design change, and provide a basis for subsequent change propagation control and collaborative control.

[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 of the entire production line, whose state and parameter change directly affects the function and operation of the upper equipment layer; the equipment layer is composed of independent devices such as mechanical arms, machining centers, etc., which are responsible for local parameter coordination and optimization, and play a role in connecting the upper and lower layers, managing and coordinating the component layer, and providing local production capacity and function support to the production line layer; the production line layer is an integrated system of the entire production line, responsible for global parameter strategy formulation and cross-equipment collaboration, overall planning of the operation and management of the entire production line, and ensuring the collaborative work between equipment to complete the production task. The configuration domain involves the physical scheme planning and configuration of the production line, including equipment selection, layout, connection mode, 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 device, ensuring smooth and efficient production process, and reasonable planning can avoid waiting and conflicts in production, improving production efficiency; the control domain aims to realize information exchange and coordinated control between devices and stations, through the construction of control network, data acquisition and analysis, decision-making instruction issuance, etc., so that the production line has the ability of automatic monitoring, real-time scheduling and feedback adjustment, and enhances the flexibility and response ability of production; the execution domain is the core of production line optimization, which realizes the optimal operation of the production process through the optimization of the whole line driving engine, involving production scheduling, equipment maintenance, energy management, quality control and other aspects of optimization, to achieve the best balance of production efficiency, quality and cost. Hierarchical and domain processing can clearly define the specific content and object of hierarchical and dimensional division when building the parameter association model, providing a basis for accurately establishing the parameter association between different levels, so that the model constructed can fully and accurately reflect the mutual relationship and influence mechanism between each part of the production line, thereby better supporting the subsequent design change propagation control and collaborative control work.

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

[0052] In step S2, the propagation path of parameter change between different levels of the production line is defined based on the parameter correlation model that has been constructed, and the corresponding blocking rules are set for possible change conflict situations. Since design changes often propagate along the correlation between the component layer, the equipment layer and the production line layer in the production line, the propagation path can help people understand the scope and order of the change that may affect in advance. By setting change blocking rules, measures can be taken in time when conflicts are detected to avoid further expansion and deterioration of conflicts, ensuring the normal operation and stability of the production line. It can ensure that the impact of changes can propagate along the predetermined path during the design change process, and can be effectively controlled in a timely manner when problems occur, thereby achieving effective management and regulation of the change propagation process.

[0053] The propagation path refers to the path of design change propagation between the component layer, the equipment layer and the production line layer of the production line according to certain order and rules. It can determine the propagation direction and sequence between different levels, so that the impact of changes can be orderly spread in the production line, facilitating early monitoring and management. For example, when a parameter in the component layer changes, according to the propagation path, it can be known that the change will first affect the equipment layer, and then may affect the operation of the entire production line. Thus, relevant personnel can check and adjust each level in sequence according to the path. The change blocking rule is a control strategy and measure taken when a parameter change in the equipment layer causes a parameter conflict in the production line layer. Its role is to prevent further propagation of changes and avoid conflicts from expanding in the production line layer, causing irreparable damage. For example, when a parameter change in the equipment layer causes incompatible or contradictory situations in the production plan and process flow of the production line layer, the change blocking rule triggers the corresponding mechanism, such as suspending the propagation of changes, issuing an alarm to prompt relevant personnel to handle, etc., thereby ensuring the safety and stability of the production line during the change process. In general, it can provide specific operation basis and means for change propagation control, so that the change propagation process is no longer disordered and uncontrollable, but can be carried out under the predetermined 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 system state reachability after parameter change through the linkage verification of configuration domain and behavior domain, and adjust the control parameter tolerance through the closed-loop feedback of control domain and execution domain;

[0055] Specifically, the linkage relationship between the configuration domain and the behavior domain is verified to verify whether the system can reach the expected running state after the parameter change, and the closed-loop feedback mechanism between the control domain and the execution domain is used to dynamically adjust the tolerance of the control parameter. There is a close relationship between the configuration domain and the behavior domain. The change of configuration will directly affect the execution and performance of behavior, and vice versa. Through linkage verification, it can be found whether the system state meets the expected requirements after parameter change. 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 parameter in real time through the actual running data fed back by the execution domain, and to optimize and adjust the control parameter according to the evaluation result, so as to ensure that the control system can always adapt to the changes in the execution domain and 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 joint inspection and verification process of the configuration parameters such as equipment selection, arrangement, connection mode in the configuration domain and the behavior parameters such as equipment action sequence, task scheduling, action timing in the behavior domain after the parameter change. Its role is to ensure that the adjustment of configuration can correctly guide the behavior execution of equipment, so that the equipment can accurately complete the corresponding production task according to the new configuration requirements, and avoid problems such as equipment failure and production confusion caused by the mismatch between configuration and behavior. For example, when the configuration of a device is adjusted, such as replacing some components or changing the layout of the device, linkage verification can check whether the action behavior of the device under the new configuration still meets the requirements of the production process, whether the coordination between actions is guaranteed, etc. The closed-loop feedback between the control domain and the execution domain refers to the process of evaluating the effect of the control parameter according to the actual running data (such as position tracking error of equipment, temperature fluctuation amplitude, motor current, etc.) fed back by the execution domain after the control system issues control instructions and drives the equipment in the execution domain to run. Its role is to make the control system adapt to various changes in the execution domain, such as adjustment of production task and change of equipment load, by continuously optimizing the tolerance range of the control parameter, so as to ensure that the control system can always accurately control the production equipment in the best state, thereby improving the running 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, the tolerance of the control parameter can be appropriately relaxed through the closed-loop feedback mechanism to avoid frequent adjustment instructions from the control system, which may cause unstable operation of the equipment, while ensuring that the equipment can normally operate under heavy load.

[0057] S4: Perform simulation testing at the component layer, equipment layer and production line layer respectively to verify the influence of the changed parameters on the production line.

[0058] Specifically, by simulating the actual operation of each level after the parameter change through simulation testing of the component layer, equipment layer and production line layer of the production line, the impact of the change on the entire production line is evaluated. Simulation testing can predict the consequences of changes in advance without actually interrupting production, including changes in production efficiency, coordination problems between equipment, fluctuations in product quality, etc. By simulating at different levels, the specific impact of changes at each level can be understood in depth to identify problems and take appropriate adjustment measures in a timely manner to ensure that design changes achieve the desired results and do not have a serious impact on the normal operation of the production line. This helps to comprehensively evaluate and optimize the change plan before actual implementation, reducing the risk and cost of change implementation.

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

[0060] The single action and action sequence of the component layer are abstractly expressed based on the behavior dimension, the execution mechanism of the action is abstractly expressed based on the mechanical structure dimension, and the control program of the component layer is abstractly expressed according to the behavior dimension;

[0061] The action sequence of the component layer is combined based on the behavior dimension and the process action sequence of the equipment is abstractly expressed, the topological relationship between the component layers is constructed based on the configuration dimension, the control program sequence of the equipment is abstractly expressed by combining the control programs of the component layers according to the process action sequence of the equipment, and the performance indicators or features in the component layer are extracted or abstractly represented from the equipment layer to obtain the component layer-equipment layer parameter correlation model.

[0062] Specifically, since the component level parameter correlation model focuses on expressing the process action, action execution mechanism and process control program of the component, the single action and action sequence of the component are first abstracted from the action behavior dimension to clarify the function and execution order of each action; the mechanism for executing these actions is abstracted from the mechanical structure dimension to determine its physical composition and motion characteristics; and the control program of the component is abstracted according to the behavior dimension to describe how the control program drives and manages the execution of the action. The equipment level parameter correlation model focuses on expressing the process action sequence, component topology, control program and performance indicators and features of the equipment. On this basis, the action sequences of multiple components are combined to form the process action sequence at the equipment level, the topological relationship between the components is constructed to reflect the spatial layout and connection method of the components in the equipment, and then the control program of the component is combined to form the control program sequence of the equipment, while the key performance indicators and features in the component are extracted as the representation of the equipment performance, finally realizing the correlation of the parameters of the component layer and the equipment layer.

[0063] The implementation of the component-equipment level parameter correlation model is various, and can be selected and combined according to different production scenes, data acquisition conditions and technical means. A common implementation is a manual modeling method based on field investigation and data analysis. First, professional engineers are organized to go deep into the production site to investigate and record the process actions, actuators and control programs of each component, including the execution sequence, time parameters, kinematics and dynamics characteristics of the mechanism, and the logic flow of the control program. Then, the component level parameter correlation model is manually constructed in the dedicated modeling software (such as MATLAB, Simulink, etc.) using these data to abstractly express the action behavior, mechanical structure and control logic of each component. Then, according to the assembly drawings and process files of the equipment, the topological relationship between the components is determined, and the component level models are integrated according to the actual assembly relationship to form the equipment level parameter correlation model. In the modeling process, the model parameters are continuously adjusted through comparison and verification with the field production data to ensure the accuracy and reliability of the model.

[0064] Preferably, in step S1, the construction of the equipment layer-line layer parameter correlation model comprises:

[0065] Based on the configuration dimension, the overall topological structure of the line layer is constructed, based on the control dimension, the control programs of the equipment layer are combined and the overall control program of the line layer is abstractly constructed, the performance indicators and characteristics in the equipment layer are extracted or abstractly characterized in the line layer, and the parameter correlation model of the equipment layer-line layer is obtained.

[0066] The line level parameter correlation model focuses on expressing the overall topological structure, the overall control program, the line performance indicators and characteristics, so the overall topological structure of the line is constructed from the configuration dimension first to clearly define the layout and connection relationship of each equipment in the line; the control programs of the equipment are combined from the control dimension to form the overall control program of the line, so as to realize centralized control and coordinated management of the entire line; and the key performance indicators and characteristics in the equipment layer are extracted at the line level to form the multi-dimensional parameter correlation skeleton model of the line. Among them, the parameter correlation models of multiple equipment levels form the equipment-line level parameter correlation model according to the topological connection.

[0067] Among them, the configuration dimension involves the physical scheme planning and configuration of the line, including the selection, arrangement, connection method, transmission path, station configuration, etc. of the equipment, which determines the overall architecture and layout of the line; the control dimension focuses on the information exchange and coordinated control between the equipment and the station, and realizes the automatic monitoring, real-time scheduling and feedback adjustment of the line through the construction of the control network, data acquisition and analysis, decision and instruction issuance, etc.; the performance indicators and characteristics are key parameters for characterizing the running effect and characteristics of the equipment, such as the machining accuracy, production efficiency, failure rate, etc. of the equipment, and extracting these indicators at the line level can reflect the overall performance and running state of the line.

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

[0069] Specifically, by analyzing the parameter dependency and mutual influence mechanism between the component layer, the equipment layer and the production line layer, the propagation path of the parameter change between the longitudinal layers is constructed. Starting from the component layer, the influence of each component parameter change on the equipment layer to which it belongs is determined, and the influence range of the equipment layer parameter change on the entire production line layer is further analyzed, forming a tree-shaped propagation structure. This can provide intuitive basis for subsequent accurate positioning of the change influence point, evaluation of the change risk and formulation of the corresponding control strategy, ensuring that the change can propagate in a controlled range according to the predetermined path, and avoiding unpredictable risk diffusion.

[0070] Preferably, the change blocking rule when the equipment layer parameter change triggers the production line layer conflict comprises:

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

[0072] Specifically, by the pre-defined logic and order, when the equipment layer parameter change may trigger the production line layer parameter conflict, the further propagation of the change is frozen in time to avoid conflict expansion. At the same time, considering the cost difference of different dimension changes, the parameter update and design change are implemented in the order of "execution domain→control domain→behavior domain→configuration domain" according to the priority order, so as to realize effective control of 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] Wherein, the equipment layer parameter change refers to the change of the parameter of the equipment layer due to design improvement, equipment maintenance or production demand adjustment; the production line layer parameter conflict refers to the incompatible or contradictory situation between the changed parameter of the equipment layer and the existing parameter setting of the production line layer; the propagation path freezing refers to immediately suspending the further transmission of the change on the longitudinal propagation path when the parameter conflict is detected, 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 update and design change in different dimensions, and the configuration dimension involves physical device adjustment and layout change, which usually has the highest cost.

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

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

[0076] By establishing a dynamic association between the configuration domain and the behavior domain, when the parameter base value changes, the pre-defined state machine rules in the behavior domain are automatically called. The system state after parameter change is verified using formal methods such as Petri net reachability analysis, ensuring that the system can transition from the current state to the desired target state, thereby ensuring the normal operation of the production line and the smooth completion of production tasks.

[0077] By comparing the deviation of the preset value and the actual value of the execution domain (such as position tracking error, temperature fluctuation amplitude), and dynamically relaxing or tightening the control parameter tolerance according to the execution domain load (such as motor current), this closed-loop control mechanism can ensure that the control system always makes timely adjustments according to the actual running situation of the execution domain to maintain the stable operation of the production line and efficient production.

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

[0079] Establishing a digital twin of the physical component, adjusting the relevant parameters of the component, verifying the failure mode of the component motion, simulating parameter mutation through step signal or sine wave excitation, and observing dynamic characteristics.

[0080] By constructing an accurate digital twin of the physical component and adjusting the relevant parameters in the virtual environment, the running state of the component under different working conditions can be simulated, thereby verifying the failure mode of its motion. Using step signal or sine wave excitation to simulate parameter mutation can stimulate the dynamic response characteristics of the component, and further analyze its dynamic performance.

[0081] Step signal is a sudden input signal whose amplitude changes in a step manner within a short time, used to simulate sudden disturbances or load changes to the system; sine wave excitation is a periodic input signal, used to simulate the response of the system under periodic load or vibration conditions.

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

[0083] Establishing a digital twin model of the equipment layer, simulating the multi-axis linkage trajectory accuracy after changing the joint parameters of the mechanical arm;

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

[0085] Specifically, by constructing a digital twin model of the equipment layer, the multi-axis linkage trajectory accuracy after changing the joint parameters of the mechanical arm can be accurately simulated, and the influence of changing the processing equipment parameters on the workpiece surface roughness can be predicted using 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 simulation technology, the effect of parameter change can be evaluated in a virtual environment, thereby discovering potential problems in advance and optimizing equipment performance, ensuring efficient operation and processing quality in actual production.

[0086] Preferably, in step S4, the following is included:

[0087] A discrete event model is constructed using a simulation tool to define the interaction rules of the production line elements;

[0088] The changed device cycle time parameters are input, the production line bottleneck stations are calculated through event-driven simulation, and a CT distribution heat map is generated;

[0089] The risk of work-in-process accumulation after changing the material flow parameters is simulated, the production capacity fluctuation range is evaluated, and then production abnormal events are triggered to verify the emergency adjustment effect of the production line layer parameters.

[0090] The simulation tool refers to the software environment used to construct and run the discrete event model, which provides rich modeling and analysis functions such as PlantSimulation or AnyLogic, enabling users to create complex production system models; the discrete event model is a model that simulates system behavior through event-driven, focusing on state change events in the system such as device start, stop, and material arrival, suitable for simulating production processes with randomness and discreteness; the interaction rules of production line elements define the interaction and information exchange between elements in the production line, such as stations, buffer zones, and AGVs, including material transfer rules and device start conditions, which determine the flow of materials and information in the production line; the device cycle time parameter refers to the time required for a device to complete a production cycle, which is a key factor affecting production line efficiency; the bottleneck station refers to the station that limits the overall production efficiency of the production line, usually the one with the longest processing time or the most prone to congestion; the CT distribution heat map is a chart that visually displays the production cycle time distribution of each station, highlighting the bottleneck station through color coding or height visualization; the work-in-process accumulation risk refers to the possibility of excessive accumulation of work-in-process on the production line due to changes in material flow parameters (transport speed, buffer zone capacity), which may cause production delays or quality problems; the production abnormal event refers to unexpected situations that occur during production, such as device failure, material shortage, etc., which may have a significant impact on production efficiency and product quality.

[0091] A production line design change propagation hierarchical and domain-based collaborative control system, as shown in Figure 3 The collaborative control system is applied to the collaborative control method described above, and the collaborative control system comprises:

[0092] The modeling module is used to construct the parameter correlation model of the component layer-equipment layer and the equipment layer-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 dimension of the configuration domain, the behavior domain, the control domain, and the execution domain;

[0093] The propagation control module is configured to define a propagation path of parameter change based on the parameter correlation model, and set a change blocking rule when a parameter change in the equipment layer triggers a conflict in the production line layer;

[0094] The cross-domain collaborative control module is configured to verify the reachability of the system state after the parameter change through linkage verification of the configuration domain and the behavior domain, and adjust the control parameter tolerance through closed-loop feedback of the control domain and the execution domain.

[0095] The simulation verification module is configured to perform simulation tests in the component layer, the equipment layer and the production line layer respectively, and verify the influence of the changed parameter on the production line.

[0096] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "exemplary embodiment", "example", "specific example", or "some examples" means 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 application. In the present specification, the exemplary description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

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

Claims

1. A collaborative control method for production line design change propagation hierarchical and zonal partitioning, characterized in that, The cooperative control method comprises the following steps: S1: based on the hierarchical structure of the component layer, the equipment layer and the production line layer in the production line, and the sub-domain dimension of the configuration domain, the behavior domain, the control domain and the execution domain, a parameter correlation model of the component layer-equipment layer and the equipment layer-production line layer is constructed; S2: based on the parameter correlation model, a propagation path of parameter change is defined, and a change blocking rule when the equipment layer parameter change causes the production line layer conflict is set; S3: through the linkage verification of the configuration domain and the behavior domain, the system state reachability after parameter change is verified, and through the closed-loop feedback of the control domain and the execution domain, the control parameter tolerance is adjusted; S4: simulation test is carried out on the component layer, the equipment layer and the production line layer respectively, and the influence of the changed parameters on the production line is verified; In step S1, the construction of the component layer-equipment layer parameter correlation model comprises: Abstractly express the single action and action sequence of the component layer based on the behavior dimension, abstractly express the execution mechanism of the action based on the mechanical structure dimension, and abstractly express the control program of the component layer according to the behavior dimension; Abstractly express the process action sequence of the equipment by combining the action sequence of the component layer based on the behavior dimension, construct the topological relationship between the component layers based on the configuration dimension, combine the control program of the component layer according to the process action sequence of the equipment, and abstractly express the control program sequence of the equipment, and extract or abstractly characterize the performance indicators or characteristics in the component layer from the equipment layer to obtain the parameter correlation model of the component layer-equipment layer; In step S1, the construction of the equipment layer-production line layer parameter correlation model comprises: Construct the overall topological structure of the production line layer based on the configuration dimension, combine the control program of the equipment layer based on the control dimension and abstractly construct the total control program of the production line layer, and extract or abstractly characterize the performance indicators and characteristics in the equipment layer in the production line layer to obtain the parameter correlation model of the equipment layer-production line layer; The change blocking rule set when the equipment layer parameter change causes the production line layer conflict comprises: When the equipment layer parameter change causes the production line layer parameter conflict, the propagation path is frozen, and parameter update and design change are implemented in the order of the execution domain, the control domain, the behavior domain and the configuration domain; In step S3, it comprises: After the parameter change, the state machine rules associated with the configuration domain parameter change are loaded from the behavior domain, and whether the state of the production line after the parameter change can reach the expected target is verified through formalized method; The deviation of the preset value and the actual value of the execution domain is compared, and the control parameter tolerance is dynamically relaxed or tightened according to the execution domain load.

2. The collaborative control method of claim 1, wherein, The propagation path of the parameter change comprises a longitudinal propagation path along the component layer, the equipment layer and the production line layer.

3. The collaborative control method of claim 1, wherein, In step S4, it comprises: The digital twin of the physical component is established, the component-related parameters are adjusted, the failure mode of the component motion is verified, the parameter mutation is simulated through step signal or sine wave excitation, and the dynamic characteristics are observed.

4. The collaborative control method of claim 1, wherein, In step S4, it comprises: The digital twin model of the equipment layer is established, and the multi-axis linkage trajectory accuracy after the joint parameter change of the mechanical arm is simulated; The processing equipment parameters are subjected to multi-physical field coupling simulation, and the workpiece surface roughness change is predicted.

5. The collaborative control method of claim 1, wherein, In step S4, it comprises: A discrete event model is constructed by using a simulation tool, and the interaction rules of the production line elements are defined; The changed device beat parameter is input, a production line bottleneck station is calculated through event-driven simulation, and a CT distribution thermal map is generated; A work-in-process accumulation risk after a change of a material flow transfer parameter is simulated, a production capacity fluctuation range is evaluated, a production abnormal event is triggered, and an emergency adjustment effect of the production line layer parameter is verified.

6. A collaborative control system for production line design change propagation hierarchical and zonal partitioning, characterized in that, The collaborative control system is applied to the collaborative control method in any one of claims 1-5, and the collaborative control system comprises: A modeling module, configured to construct a parameter correlation model of a component layer-equipment layer and an equipment layer-production line layer based on a hierarchical structure of the component layer, the equipment layer and the production line layer in the production line, and a sub-domain dimension of a configuration domain, a behavior domain, a control domain and an execution domain; A propagation control module, configured to define a propagation path of a parameter change based on the parameter correlation model, and set a change blocking rule when an equipment layer parameter change causes a production line layer conflict; A cross-domain collaborative control module, configured to verify system state reachability after a parameter change through linkage verification of the configuration domain and the behavior domain, and adjust a control parameter tolerance through closed-loop feedback of 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, and verify an influence of the changed parameter on the production line.

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