A method for modeling production execution logic in discrete manufacturing workshops

Abstract

This invention discloses a method for modeling the production execution logic (FICM) of a discrete manufacturing workshop. Specifically, it defines directed service node pairs (DSNPs) to extend the seven-element SE model, consistently expressing the dynamic execution logic of basic production activities. It defines encapsulated service units (ESCs) to enable the combination and functional, modular encapsulation of multiple DSNPs to express more complex production logic. It constructs a production execution logic model (PELM-DaE) by combining multiple DSNPs and ESCs to achieve integrated representation of FICM in the workshop, as well as effective expression and flexible reconfiguration of workshop-level production execution logic. This invention realizes the representation and integration of FICM, effectively describing dynamic production execution logic; it provides a new technical solution for improving scheduling simulation efficiency and has significant practical application value.

Description

Technical Field

[0001] This invention belongs to the field of workshop production execution logic modeling, and particularly relates to a method for modeling production execution logic in discrete manufacturing workshops. Background Technology

[0002] As market competition intensifies, discrete manufacturing workshops require greater flexibility in their production execution processes to adapt to ever-changing manufacturing demands. The application of Digital Twin (DT) technology offers a potential solution to this challenge, enabling synchronization, prediction, and decision-making in workshop production through scheduling, simulation, and other technologies. However, due to dynamic uncertainties and disturbances in the production execution process, existing technologies have the following shortcomings:

[0003] First, existing production execution logic models struggle to effectively integrate the Flows of Information, Control, and Material (FICM). Commonly used models include Gantt charts for representing scheduling plans, Petri nets for describing control flow logic, the Seven Elements (SE) model for describing material flow, and the Material Node Oriented Seven Elements (MNOSE) model. Each of these models only describes a portion of the FICM, lacking a comprehensive consideration of the relationships between the three, and thus fails to meet the need for a complete description of production execution logic in dynamic production environments.

[0004] Secondly, in practical industrial applications, when production disruptions occur due to equipment failures or order changes, adjustments to multiple aspects such as production plan updates, control program modifications, and material rescheduling need to be handled simultaneously. Existing technologies require managing and updating models for different flows separately, which not only increases response latency but may also lead to inconsistencies in the states between models, reducing system availability.

[0005] In view of this, there is an urgent need in the field for a new production execution logic model that can: (1) effectively integrate and describe FICM; and (2) support the description of dynamic production execution logic. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a method for modeling production execution logic in discrete manufacturing workshops.

[0007] The present invention provides a discrete manufacturing workshop production execution logic modeling method, which constructs a production execution logic model PELM-DaE based on directed service nodes DSNP and packaged service units ESC, specifically including the following steps:

[0008] Step 1: Define Directed Service Node Pair (DSNP) to extend the seven-element SE model and consistently express the dynamic execution logic of basic production activities.

[0009] Step 2: Define an encapsulated service cell (ESC) to enable the combination and functional, modular encapsulation of multiple directed service nodes to DSNPs, so as to express more complex production logic; at the same time, it enables the carrying of information flow, control flow and material flow FICM in workshop production, and realizes the flexible configuration and reconfiguration of production execution logic within it.

[0010] Step 3: Construct the Production Execution Logic Model (PELM-DaE) with Directed service node pairs and Encapsulated service cells. By combining multiple directed service node pairs (DSNP) and encapsulated service cells (ESC), the FICM is integrated in the shop floor, and the production execution logic at the shop floor level is effectively expressed and flexibly reconfigured.

[0011] Furthermore, step 1 specifically involves:

[0012] First, by integrating FICM, the virtual service node is extended into a service node, defined as follows:

[0013] SN = <P n A n ,Pos,T s ,S n ,F set Des in Des out > (1)

[0014] Among them, P n Service node types are categorized based on the services they perform: path nodes, processing nodes, storage nodes, and auxiliary nodes; A n The attribute elements of the service node include logistics path, processing node, and buffer zone; there is a correspondence between node type and attribute. Pos represents the coordinates of the service node, indicating the specific coordinates of the material's arrival and stop during the production process. T s Service time refers to the time a material stays at a service node and performs production activities after entering the service node; S n It refers to the state of the service node, including idle, working, and blocked; F set It is the set of stream entities currently executing services in the node; Des inThis indicates the conditions under which a flow entity enters a service node, determining whether to deny or allow the material to enter the node and initiate the service; Des out This indicates the conditions under which a flow entity can leave a service node and proceed to the next service node.

[0015] To describe the production activities at and between service nodes, a directed service node pair (DSNP) is defined as follows:

[0016] DSNP = <P p ,N1,N2,Dir,T l ,S e ,S p ,E> (2)

[0017] Among them, P p This refers to the type of DSNP, which describes different production execution activities based on the type of service nodes and their connection relationships; N1 and N2 are two defined service nodes, representing two locations in the material space where production activities will occur; Dir is the direction of the connection edge between N1 and N2, indicating the flow relationship of materials between service nodes, which can be bidirectional, forward, or reverse; T l Logistics time refers to the time required to perform material transfer activities between two service nodes; S e It refers to the state of the DSNP connection edge, including idle, active, and blocked; S p E represents the state of the DSNP, including active and inactive, reflecting the execution process of production activities; E represents the actuator of the DSNP, indicating the logistics equipment that performs material transfer activities between two service nodes.

[0018] Furthermore, step 2 specifically involves:

[0019] By combining and encapsulating multiple directed service node pairs (DSNPs), the composition structure of a modular production unit is described. Two encapsulated service units (ESCs) are proposed: Process Encapsulation Service Unit (PESC) and Buffer Encapsulation Service Unit (BESC), defined as follows:

[0020]

[0021]

[0022] Among them, C, P, and B are the control device, processing device, and cache device, respectively; It is the actuator set of ESC, used to realize the material flow within it; DSNP set It is the DSNP set in ESC, used to describe the production execution logic of a unit; LN set It is the set of internal logistics paths of ESC, used to realize the self-organization of actuators on the ESC path; and It represents the feed and discharge positions of the unit, indicating the material interaction interface between the ESC and its external elements.

[0023] For the Process Encapsulation Service Unit (PESC) and It is its private input and output buffer; for the buffer encapsulation service unit BESC, PESC set It is a collection of PESCs that can provide storage services for it.

[0024] Meanwhile, during production execution, the encapsulated directed service nodes carry and integrate FICM with DSNP to describe the dynamic execution process within the unit:

[0025] First, during the production execution process in the discrete manufacturing workshop, each ESC has a specific task queue arranged according to the production plan. This queue maps the information flow of the ESC and drives its DSNP to execute production activities. Second, due to the dynamic changes in resources and job status, the routes to complete the same production job in the ESC may differ. This logic is controlled by the in / out decision function in the DSNP, which maps the control flow. Finally, guided by the information flow and control flow, materials move forward according to the execution order of the DSNP, and their actual flow routes map the material flow.

[0026] Furthermore, step 3 specifically involves:

[0027] The flexible production execution logic model PELM-DaE, which features directed service node pairs (DSNPs) and encapsulated service units (ESCs), is defined as follows:

[0028]

[0029] Among them, PESC set and BESC set It is a collection of PESC and BESC, describing the composition of various production functional modules in the workshop; It is a workshop-level DSNP set; and It is a collection of workshop-level logistics paths and actuators, collectively describing the interactions between workshop ESCs, and is a mapping of workshop production activities; F set It is a set of fluid entities.

[0030] The beneficial technical effects of this invention are as follows:

[0031] This invention introduces the concepts of directed service node pairs and encapsulated service units, realizing the representation and integration of FICM (Fixed Component Logic Module), and effectively describing dynamic production execution logic. The PELM-DaE model provides a foundation for constructing connection graphs, enabling the characterization of job execution relationships and constraints, and pre-computation of FICM. This invention's model offers a new technical solution for improving scheduling simulation efficiency and has significant practical application value. Attached Figure Description

[0032] Figure 1 This is the overall architecture of the production execution logic model PELM-DaE of this invention.

[0033] Figure 2 For service nodes and DSNP legend.

[0034] Figure 3 This is a schematic diagram of the DSNP propulsion process.

[0035] Figure 4 The finite state machine model related to DSNP is shown (a, b, and c represent the service node, DSNP edge, and DSNP finite state machine, respectively).

[0036] Figure 5 Define encapsulated service units (a and b are the encapsulations of the processing service unit and the storage service unit, respectively).

[0037] Figure 6 This is a schematic diagram of the FICM integration process in ESC.

[0038] Figure 7 For intelligent manufacturing demonstration line

[0039] Figure 8 The image shows the graphical representation of PELM-DaE's IMDL (where a is a description of the IMDL production execution logic based on PELM-DaE, b is a screenshot of the 3D PELM-DaE modeling interface, and c is a screenshot of the 2D PELM-DaE model mapping interface).

[0040] Figure 9 The example demonstrates a comparison of production execution modeling. Detailed Implementation

[0041] The present invention will be further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0042] This invention provides a method for modeling production execution logic in discrete manufacturing workshops. It proposes a PLEM-DaE model to express the production execution logic in discrete manufacturing workshops, further promoting the effective description of the workshop production execution process. The specific steps are as follows:

[0043] Step 1: Construct the overall modeling architecture. To effectively describe the workshop production execution logic, an overall modeling architecture needs to be defined. The overall architecture of the proposed model is as follows: Figure 1 As shown. The basic elements of SE and MNOSE involved are as follows: Figure 1 As shown in the dashed box below, this invention makes improvements based on this. First, it proposes DSNP to describe production execution activities. Second, by combining and encapsulating multiple DSNPs with elements in the basic model, it extends the definition of process and buffer service units to support the dynamic processes of FICM. Finally, it proposes PELM-DaE to achieve effective connections between production resources and effectively describe their execution logic.

[0044] Step 2: Define a Directed Service Node Pair (DSNP).

[0045] SE (Service Provider) describes the forms of processing and logistics activities by connecting various elements through virtual service nodes and logistics paths. However, these activities must be further decomposed and refined to describe the underlying dynamic production execution logic, such as actuator-triggered loading and unloading, and the selection of parts for priority caching or direct processing. Therefore, this invention proposes DSNP (Dynamic Component Parts) to describe the above-mentioned execution logic. Furthermore, by combining and connecting DSNPs, more complex equipment-level, unit-level, and shop floor-level execution logic can be formed.

[0046] First, by integrating FICM, virtual service nodes are extended into service nodes. This is an important part of DSNP and is defined as follows:

[0047] SN = <P n A n ,Pos,T s ,S n ,F set Des in Des out > (1)

[0048] Among them, P n Service nodes are categorized into path nodes, processing nodes, storage nodes, and auxiliary nodes based on the services they perform. These nodes can carry both flowing entities and different types of production information such as processing and storage time. For ease of discussion, a formal definition of the service node's diagram is provided, such as... Figure 2 As shown on the left.

[0049] A nThe attribute elements for service nodes include logistics paths, processing nodes, buffers, etc. There is a correspondence between node types and attributes; for example, processing nodes correspond to processing nodes, buffers correspond to storage nodes, and auxiliary nodes may exist on processing nodes and buffers, representing consumable resources. Path nodes correspond to logistics paths, indicating the locations that materials will pass through during the logistics process.

[0050] Pos represents the coordinates of the service node, indicating the specific coordinates at which the material arrives and stops during the production process.

[0051] T s Service time refers to the time that a material (flowing entity) stays at a service node and performs production activities after entering the service node.

[0052] S n It represents the state of the service node, including idle, working, and blocked states. It is affected by FICM and can reflect FICM.

[0053] F set It is the set of stream entities currently executing services in the node.

[0054] Des in This indicates the conditions under which a flow entity enters a service node, determining whether to deny or allow material entry into the node and initiate the service. For example, for a processing node on a machine tool, the access condition might be that the machine tool is idle or capable of processing the requested operation.

[0055] Des out This indicates the conditions under which a flow entity can leave a service node and proceed to the next service node. For example, for a processing node in a machine tool, the departure condition might be that the AGV is idle and in place, or that there is space in the buffer.

[0056] Compared to the definition of virtual service nodes in SE, defining service nodes extends them from the logical space to the real space and adds dynamic constraints such as type and entry / exit decision conditions. This allows service nodes to not only describe the material flow interaction structure between devices, but also provide a more granular description and control of the production execution process.

[0057] A service node represents the specific location that a material can reach in production; it is a mapping of its static spatial characteristics. Production execution activities in a Data Management System (DMS) are viewed as the dynamic flow of materials between service nodes. This reflects the dynamic spatial characteristics of materials, making the execution logic more complex and requiring an element to describe the production activities at and between service nodes. Therefore, the concept of a Data-Driven Production Node (DSNP) is proposed, defined as follows:

[0058] DSNP = <P p ,N1,N2,Dir,T l ,S e ,S p,E> (2)

[0059] Among them, P p This refers to the type of DSNP, which describes different production execution activities based on the type of service node and its connection relationships, as shown in the appendix. Figure 2 As shown on the left.

[0060] N1 and N2 are two defined service nodes, representing two locations in the physical space where production activities will take place.

[0061] Dir is the direction of the connection edge between N1 and N2, representing the material flow relationship between service nodes. It can be bidirectional (0), forward (1, indicating N1 to N2) or reverse (2, N2 to N1).

[0062] T l Logistics time refers to the time required to perform material transfer activities between two service nodes.

[0063] S e It represents the state of the DSNP connection edge, including idle, active, and blocked, and is affected by and reflects the FICM.

[0064] S p It is the state of DSNP, including active and inactive, reflecting the execution process of production activities.

[0065] E is the actuator of DSNP, representing a logistics device that performs material transfer activities between two service nodes.

[0066] The DSNP model describes the spatial information of the production organization structure in the DMS. It also requires utilizing the FICM process to define the DSNP's progression in both time and space dimensions to evaluate and simulate production execution activities, such as... Figure 3 As shown.

[0067] Figure 3 The purple section represents production decisions in DSNP, and the green section represents production activities. First, the input decisions in DSNP are evaluated to control material input. Once materials enter, service activities begin and continue until T. l A timeout indicates the service has ended. The decision is then evaluated, and if the test passes, the material transfer from N1 to N2 begins. The process for N2 is the same as for N1, and will not be repeated here.

[0068] It is important to note that the decision-making process not only determines whether materials can leave during the DSNP process, but also establishes a logical connection with the next DSNP.

[0069] The dynamic evolution and time control of the state during the DSNP process can be represented by a finite state machine model. Figure 4 The finite state machine model of the service node, edge, and node pairs is shown.

[0070] δ represents the time when the element triggers an internal state transition. When resources arrive at a node, the entry conditions of the service node are evaluated. If the entry judgment test is passed, the resources will enter the node and begin service, and its state will change from "idle" to "working" triggered by an external state transition. t is the service time of the service node. When t = T s During the test, the node's departure conditions are evaluated. If the test passes, the resources will leave the node, and the node's status will change to "idle." If the test fails due to resource conflicts, the node's status will be "blocked," and the resources will remain on the node until the test passes.

[0071] For the connection edges between nodes in DSNP, when the resource completes the service at node one, and its Des out When the test passes, the edge's state changes from "idle" to "working," and logistics activities begin. When the edge's activity time equals T... l When, if the Des of node 2 in If the test passes, the edge's state will change to "idle". Otherwise, materials cannot enter node 2, and the edge's state will change to "blocked" until the test passes.

[0072] For DSNP, the Des of the second node in the previous node pair is... out Activated upon successful test. The node pair will remain deactivated until the second node in the current node pair reaches its Des threshold. out If the test passes, this will activate the next DSNP (if it exists).

[0073] Step 3: Define the encapsulated service cell (ESC).

[0074] The DSNP proposed in this invention represents the execution logic of basic production activities. More complex production execution logic can be achieved by combining multiple DSNPs. Furthermore, due to the pursuit of greater production flexibility, shorter logistics times, and simpler production management, shop floor production systems are transforming into cellular manufacturing systems. Cellular manufacturing improves efficiency and reduces costs by combining a series of production elements that can perform specific functions, specializing, collaborating, and modularizing these elements at the production level. This requires encapsulated production execution logic that supports flexible expansion and reconfiguration. This invention extends the process and buffer service units in the basic model, proposing encapsulated service units (ESCs), such as... Figure 5As shown. By combining and encapsulating multiple DSNPs, the extended ESC not only describes the compositional structure of the modular production unit, but also its dynamic production execution process.

[0075] The two definitions of ESC are as follows:

[0076]

[0077] C, P, and B represent the control device, processing device, and buffer device, respectively.

[0078] It is a set of actuators for ESC, used to realize the material flow within it.

[0079] DSNP set It is the DSNP set in ESC, used to describe the production execution logic of a unit.

[0080] LN set It is the set of internal logistics paths of the ESC, used to enable the self-organization of actuators on the ESC path. For example, if an automated warehouse is combined with a stacker crane as an ESC, then the stacker crane track is the internal logistics path of the cell management.

[0081] and It represents the feed and discharge positions of the unit, indicating the material interaction interface between the ESC and its external elements.

[0082] For the Process Encapsulation Service Unit (PESC) and It is its private input and output buffer.

[0083] For the Buffer Encapsulation Service Unit BESC, PESC set It is a collection of PESCs that can provide storage services for it.

[0084] Both ESCs encapsulate basic production functions. Furthermore, during production execution, they can carry and integrate FICMs based on the encapsulated DSNPs, thereby describing the dynamic execution process within the unit. Figure 6 This explains the process of achieving FICM integration through the DSNP encapsulation in ESC.

[0085] First, during the production execution process of DMS, each ESC has a specific job queue arranged according to the production plan. This queue maps the information flow of the ESC and drives its DSNP to execute production activities. Second, due to the dynamic changes in resources and job status, the routes for completing the same production job in an ESC may differ. This is mainly reflected in the different ways that the equipment in the ESC completes the same production task through different execution logics. For example, they may prioritize loading and unloading machine tools, or they may enter the buffer first and then perform these operations. This logic is controlled by the in / out decision function in the DSNP, which maps to the control flow. Finally, guided by the information flow and control flow, materials move forward according to the execution order of the DSNP, and their actual flow routes map to the material flow, such as... Figure 7 As indicated by the green arrow in the middle.

[0086] It is important to note that the above descriptions of the three flows are not fragmented. By encapsulating multiple DSNPs, it organically integrates the processes and interactions of FICM within ESC at a consistent granularity, thereby achieving the description and encapsulation of the production execution logic within the unit and reducing the workload of repeatedly constructing DSNPs.

[0087] Step 4: Construct the Production Execution Logic Model (PELM-DaE) with Directed service node pairs and Encapsulated service cells.

[0088] The DSNP and ESC proposed in this invention realize a modular representation of production execution logic, which carries and encapsulates the dynamic flow of FICM within a unit. However, it is still necessary to flexibly reconfigure the production execution logic at the shop floor level to manage the entire shop floor production process and drive production job scheduling. Therefore, this invention proposes a Flexible Production Execution Logic Model (PELM-DaE) with directed service node pairs and encapsulated service units. The model is defined as follows:

[0089]

[0090] Among them, PESC set and BESC set It is a collection of PESC and BESC, describing the composition of various production functional modules in the workshop.

[0091] It is a workshop-level DSNP set.

[0092] and It is a collection of workshop-level logistics paths and actuators that together describe the interaction between workshop ESCs and is a mapping of workshop production activities.

[0093] F set It is a set of flowing entities. It carries information such as process and production data, and drives the production organization and dynamic evolution of the workshop PELM-DaE.

[0094] PELM-DaE is a multivariable, multi-parameter model that enables modular and flexible reconfiguration of shop floor execution logic in dynamic environments by configuring combinations of DSNP and ESC. Furthermore, the encapsulation from ESC to DSNP allows for layered modeling of shop floor execution logic and unit execution logic, avoiding the repetitive construction of basic production execution activities through DSNP alone, making the construction of shop floor execution logic based on this model simpler and more practical.

[0095] Example:

[0096] 1. Actual production line analysis.

[0097] This invention developed a corresponding software platform to assist other researchers in implementing the method of this invention. Subsequently, a case study was conducted in an actual workshop. A production execution logic model of the workshop was constructed, and the improvements brought about by the proposed method were demonstrated.

[0098] This workshop, known as the Intelligent Manufacturing Demonstration Line (IMDL), specializes in processing small and medium-sized structural components. An overview of the IMDL is as follows: Figure 7 As shown, it is equipped with ten machine tools, two six-DOF robotic arms, three AGVs (one of which is equipped with a robotic arm), and two automated warehouses. It consists of five main areas: a raw material and finished product storage area, a coding and engraving area, two milling areas, and a finished product visual inspection area. All materials are coded by laser engraving machines before processing, undergo quality inspection after processing, and then enter the finished product warehouse.

[0099] 2. Shop floor production execution logic modeling based on PELM-DaE.

[0100] To construct a PELM-DaE, the first step is to identify workshop production resources and map them to a production factor model, such as... Figure 1 As shown. Secondly, it is necessary to identify the service locations of materials during production execution (including processing, logistics, warehousing, and consumption) and map them as service nodes. Then, it is necessary to analyze the flow and interaction patterns between these locations and map them as connections in DSNP. Finally, the corresponding ESCs are configured based on the service hierarchy and relationships during the actual execution of the production equipment. Figure 8The main screenshots show the IMDL graphical representation of PELM-DaE and the corresponding modeling performed in the platform.

[0101] As can be seen, IMDL contains 12 PESCs: PESC set ={ESC i |1≤i≤12}、2 BESCs: BESC set ={ESC i |13≤i≤14}、6 external logistics routes: Two internal logistics routes: 7 external actuators: Two internal actuators: 294 service nodes and 242 DSNPs. The PELM-DaE of IMDL can be constructed according to formula (5).

[0102] Furthermore, the MNOSE model has been shown to outperform existing models, such as Petri nets and complex networks, in describing production execution logic. To further demonstrate the advantages of PELM-DaE, this invention provides a case study of refined execution logic modeling using IDML. This invention compares PELM-DaE with SE and MNOSE models to determine which model exhibits best performance in modeling production execution logic. Example case study: Figure 9 As shown.

[0103] The embodiment includes two processing machines, one for laser engraving (#3) and the other for vision inspection (#4). #3 has two material positions. Position 1 is the position for laser engraving, where the material is gripped from the output conveyor (#5) by a stationary robot (#2). Then, a robot (#1) with an AGV picks up the workpiece from Position 1 and moves it for subsequent processing. Upon returning to the vision inspection process, #1 cannot directly proceed to #3 due to constraints in logistics path planning to avoid mutual interference. Therefore, Position 2 is used as a temporary storage position (without engraving). #1 transfers the material, and #2 moves the material from Position 2 to #4. After processing in #4, the material is gripped by #2 and fed onto the feed conveyor (#6). Figure 9 (bd) shows these three models.

[0104] It can be seen that the SE model cannot describe the above production scenario because all material interactions rely on logistics paths and do not define service nodes within the equipment. MNOSE defines nodes within the equipment, cascading individual material points by defining actuator nodes, thus expressing a more detailed material flow. However, this method remains equipment-centric and cannot represent situations where multiple types of services are performed within a single device. Therefore, a virtual buffer is needed to describe storage bits, but this increases the model's complexity and reduces its interpretability to actual workshop production.

[0105] Furthermore, in MNOSE, the material transfer state between points is mapped through control signals. This method only describes the form of the material flow logic between points. However, it does not fully capture the execution process and control logic of materials within a point, resulting in an inadequate representation of the FICM. For example, a material is transported from the starting point to the ending point of conveyor belt #5. However, after the current material reaches the end of #5, it cannot enter #3 and will remain waiting for other materials to be carved. MNOSE cannot express the more specific interaction process and control logic mentioned above, making it difficult for the model to represent the dynamic evolution of the FICM during production execution.

[0106] PELM-DaE constructs production logic from a service node perspective and describes the dynamic execution process based on the DSNP (Distributed Data Processing Novel) advancement process to integrate FICM (Fixed Component Management System). For information flow, the model expresses dynamic spatiotemporal production information from the material and machine dimensions through DSNP sequences and ESC (Electronic Task Control) job queues. For control flow, the model achieves more flexible control over materials through inbound and outbound decisions in DSNP. For material flow, the model is constructed from a service node perspective, providing material flow representation and tracking at the individual material granularity.

[0107] Overall, compared to other methods, PELM-DaE can achieve the unification and integration of FICM in production execution logic. It can comprehensively describe the dynamic process of information flow driving control flow, control flow generating material flow, and material flow realizing information flow, thus more effectively supporting the further application of the model in the shop floor.

Claims

1. A method for modeling production execution logic in a discrete manufacturing workshop, characterized in that, The production execution logic model PELM-DaE is constructed based on the directed service nodes DSNP and the encapsulated service unit ESC, specifically including the following steps: Step 1: Define a directed service node pair DSNP to extend the seven-element SE model and consistently express the dynamic execution logic of basic production activities; First, by integrating FICM, the virtual service node is extended into a service node, defined as follows: SN=<P n ,A n ,Pos,T s ,S n ,F set ,Des in ,Des out > (1) Among them, P n Service node types are categorized based on the services they perform: path nodes, processing nodes, storage nodes, and auxiliary nodes; A n The attribute elements of the service node include logistics path, processing node, and buffer zone; there is a correspondence between node type and attribute. Pos represents the coordinates of the service node, indicating the specific coordinates of the material's arrival and stop during the production process. T s Service time refers to the time a material stays at a service node and performs production activities after entering the service node; S n It refers to the state of the service node, including idle, working, and blocked; F set It is the set of stream entities currently executing services in the node; Des in This indicates the conditions under which a flow entity enters a service node, determining whether to deny or allow the material to enter the node and initiate the service; Des out This indicates the conditions under which a flow entity can leave a service node and proceed to the next service node; To describe the production activities at and between service nodes, a directed service node pair (DSNP) is defined as follows: DSNP=<P p ,N1,N2,Dir,T l ,S e ,S p ,E> (2) Among them, P p This refers to the type of DSNP, which describes different production execution activities based on the type of service nodes and their connection relationships; N1 and N2 are two defined service nodes, representing two locations in the material space where production activities will occur; Dir is the direction of the connection edge between N1 and N2, indicating the flow relationship of materials between service nodes, which can be bidirectional, forward, or reverse; T l Logistics time refers to the time required to perform material transfer activities between two service nodes; S e It refers to the state of the DSNP connection edge, including idle, active, and blocked; S p The state of the DSNP, including active and inactive, reflects the execution process of production activities; E is the actuator of the DSNP, representing the logistics equipment that performs material transfer activities between two service nodes; Step 2: Define an encapsulated service unit (ESC) to enable the combination and functional, modular encapsulation of multiple directed service nodes to DSNP, so as to express more complex production logic; at the same time, it enables the carrying of information flow, control flow and material flow FICM in workshop production, and realizes the flexible configuration and reconfiguration of production execution logic within it; By combining and encapsulating multiple directed service node pairs (DSNPs), the composition structure of a modular production unit is described. Two encapsulated service units (ESCs) are proposed: Process Encapsulation Service Unit (PESC) and Buffer Encapsulation Service Unit (BESC), defined as follows: Among them, C, P, and B are the control device, processing device, and cache device, respectively; It is the actuator set of ESC, used to realize the material flow within it; DSNP set It is the DSNP set in ESC, used to describe the production execution logic of a unit; LN set It is the set of internal logistics paths of ESC, used to realize the self-organization of actuators on the ESC path; and It refers to the feed and discharge positions of the unit, representing the material interaction interface between the ESC and its external elements; For the Process Encapsulation Service Unit (PESC) and It is its private input and output buffer; for the buffer encapsulation service unit BESC, PESC set It is a collection of PESCs that can provide storage services for it; Meanwhile, during production execution, the encapsulated directed service nodes carry and integrate FICM with DSNP to describe the dynamic execution process within the unit: First, during the production execution process of DMS, each ESC has a specific job queue arranged according to the production plan. This queue maps the information flow of the ESC and drives its DSNP to execute production activities. Second, due to the dynamic changes in resources and job status, the routes to complete the same production job in the ESC may be different. This logic is controlled by the in / out decision function in the DSNP, which maps the control flow. Finally, guided by the information flow and control flow, materials move forward according to the execution order of the DSNP, and their actual flow routes map the material flow. Step 3: Construct the Production Execution Logic Model (PELM-DaE). By combining multiple directed service nodes (DSNP) and encapsulated service units (ESC), the FICM is integrated in the shop floor, and the production execution logic at the shop floor level is effectively expressed and flexibly reconfigured. The flexible production execution logic model PELM-DaE, which features directed service node pairs (DSNPs) and encapsulated service units (ESCs), is defined as follows: Among them, PESC set and BESC set It is a collection of PESC and BESC, describing the composition of various production functional modules in the workshop; It is a workshop-level DSNP set; and It is a collection of workshop-level logistics paths and actuators, collectively describing the interactions between workshop ESCs, and is a mapping of workshop production activities; F set It is a set of fluid entities.

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