Discrete manufacturing workshop production execution logic modeling method

Abstract

The invention discloses a discrete manufacturing workshop production execution logic modeling method, which specifically comprises the following steps: defining a directed service node pair DSNP, expanding a seven-element SE model, and consistently expressing dynamic execution logic of basic production activities; defining a packaging service unit ESC, and realizing combination, functionalization and modular packaging of a plurality of directed service node pairs DSNP so as to express more complex production logic; a production execution logic model PELM-DaE is constructed, and integrated representation of the FICM in a workshop and effective representation and flexible reconstruction of production execution logic at a workshop level are realized by combining a plurality of directed service node pairs DSNP and a plurality of encapsulated service units ESC. According to the method, expression and integration of the FICM are realized, and dynamic production execution logic can be effectively described; a new technical scheme is provided for improving the scheduling simulation efficiency, and the method has important practical application value.

Description

Technical Field

[0001] The invention belongs to the field of workshop production execution logic modeling, and in particular relates to a discrete manufacturing workshop production execution logic modeling method. Background Art

[0002] As market competition intensifies, the production execution process of discrete manufacturing workshops requires greater flexibility to adapt to changing manufacturing needs. The application of Digital Twin (DT) technology provides a possible solution to this challenge. It achieves synchronization, prediction and decision-making of workshop production through scheduling, simulation and other technologies. However, due to dynamic uncertainty and interference in the production execution process, the existing technology has the following shortcomings:

[0003] First, it is difficult for the existing production execution logic model to effectively integrate the information flow, control flow and material flow (FICM). Currently, the commonly used models include the Gantt chart for representing the scheduling plan, the Petri net for describing the control flow logic, the Seven Elements (SE) model for describing the material flow, and the Material Node Oriented Seven Elements (MNOSE) model. These models only describe part of the content of FICM, lack comprehensive consideration of the relationship between the three, and are difficult to meet the needs of a complete description of the production execution logic in a dynamic production environment.

[0004] Second, in actual industrial applications, when production disturbances such as equipment failure or order changes occur, it is necessary to simultaneously handle multiple adjustments such as production plan updates, control program modifications, and material rescheduling. Existing technologies require the separate management and update of models for different flows, which not only increases response delays, but may also lead to inconsistent states between models, reducing system availability.

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

[0006] In order to solve the above technical problems, the present invention provides a discrete manufacturing workshop production execution logic modeling method.

[0007] A discrete manufacturing workshop production execution logic modeling method of the present invention constructs a production execution logic model PELM-DaE based on a directed service node pair DSNP and an encapsulation service unit ESC, specifically comprising the following steps:

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

[0009] Step 2: Define the encapsulated service cell (ESC) to realize the combination and functional and modular encapsulation of multiple directed service nodes to DSNP to express more complex production logic; at the same time, realize the carrying of information flow, control flow and material flow FICM in workshop production, and realize the flexible configuration and reconstruction of the production execution logic.

[0010] Step 3: Construct a production execution logic model (PELM-DaE) by combining multiple directed service node pairs DSNP and encapsulated service cells ESC to achieve the integrated representation of FICM in the workshop, as well as the effective expression and flexible reconstruction of the production execution logic at the workshop level.

[0011] Furthermore, step 1 is specifically as follows:

[0012] First, by integrating FICM, the virtual service node is extended to a service node, which is 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 A is the service node type, which is divided into path node, processing node, storage node and auxiliary node according to the different services performed by the service node; A n is the attribute element of the service node, including logistics path, processing node, buffer zone, and there is a corresponding relationship between node type and attribute; Pos is the coordinate of the service node, indicating the specific coordinate position of the material arriving and stopping in the production process; T s S is the service time, which means the time that the material stays in the service node and performs production activities after entering the service node; n is the status of the service node, including idle, working and blocked; F set It is the set of flow entities currently executing services in the node; inIndicates the conditions for the flow entity to enter the service node, and determines whether to refuse or allow the material to enter the node and start the service; out Indicates the conditions under which a flow entity can leave a service node and proceed to the next service node.

[0015] In order to describe the production activities at and between service nodes, the 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 is the type of DSNP, describing different production execution activities according to the type of service nodes and their connection relationships; N1 and N2 are two defined service nodes, representing the two locations in the material space where production activities will occur; Dir is the direction of the connecting edge between N1 and N2, indicating the flow relationship of materials between service nodes, which can be bidirectional, forward or reverse; T l is the logistics time, which means the time required to perform material transfer activities between two service nodes; S e is the state of the DSNP connection edge, including idle, working and blocked; S p It is the state of DSNP, including activation and inactivation, reflecting the execution process of production activities; E is the executor of DSNP, which represents the logistics equipment that performs material transfer activities between two service nodes.

[0018] Furthermore, step 2 is specifically as follows:

[0019] By combining and encapsulating multiple directed service node pairs DSNP, the composition structure of the modular production unit is described, and two encapsulated service units ESC are proposed, namely the process encapsulation service unit PESC and the buffer encapsulation service unit BESC, which are defined as follows:

[0020]

[0021]

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

[0023] For the process encapsulation service unit PESC, and Is its private input and output buffer; for the buffer encapsulation service unit BESC, PESC set It is a collection of PESCs for which storage services can be provided.

[0024] At the same time, during the production execution process, the encapsulated directed service node carries and integrates FICM to DSNP, thereby describing the dynamic execution process in the unit:

[0025] First, in the production execution process of the discrete manufacturing workshop, each ESC has a specific job queue arranged according to the production plan, which maps the information flow of the ESC and drives its DSNP to perform production activities; second, due to the dynamic changes in resources and job status, the route to complete the same production job in the ESC may be different. This logic is controlled by the entry / exit decision function in the DSNP, which maps the control flow; finally, under the guidance of information flow and control flow, materials advance in the execution order of the DSNP, and their actual flow routes map the material flow.

[0026] Furthermore, step 3 is specifically as follows:

[0027] The constructed flexible production execution logic model PELM-DaE with directed service node pairs DSNP and encapsulated service units ESC 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 function modules in the workshop; It is a collection of DSNPs at the workshop level; and It is a collection of workshop-level logistics paths and actuators, which together describe the interaction between workshop ESCs and are a mapping of workshop production activities; F set It is a fluid entity set.

[0030] The beneficial technical effects of the present invention are:

[0031] The model of the present invention introduces the concepts of directed service node pairs and encapsulated service units, realizes the representation and integration of FICM, and can effectively describe the dynamic production execution logic. The PELM-DaE model provides a basis for building a connection graph, which enables the characterization of job execution relationships and constraints, and pre-calculates FICM. The model of the present invention provides a new technical solution for improving the efficiency of scheduling simulation, and has important practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 The overall architecture of the PELM-DaE execution logic model is produced for the present invention.

[0033] Figure 2 Illustration of service node and DSNP.

[0034] Figure 3 Schematic diagram of the DSNP advancement process.

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

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

[0037] Figure 6 Schematic diagram of the integration process of FICM in ESC.

[0038] Figure 7 Demonstration line for intelligent manufacturing

[0039] Figure 8 It is the IMDL graphical representation of PELM-DaE (where a is the IMDL production execution logic description 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] Fig. 9 The embodiment is a production execution modeling comparison. DETAILED DESCRIPTION

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

[0042] The present invention provides a discrete manufacturing workshop production execution logic modeling method, which proposes a PLEM-DaE model to express the production execution logic in the discrete manufacturing workshop, and further promotes the effective description of the workshop production execution process. The specific steps are:

[0043] Step 1: Construct the overall modeling architecture. In order to effectively describe the production execution logic of the workshop, it is necessary to define the overall modeling architecture. The overall architecture of the proposed model is as follows: Figure 1 The basic elements of SE and MNOSE involved are as follows Figure 1 As shown in the dotted box below. On this basis, the present invention makes improvements. First, DSNP is proposed to describe production execution activities. Secondly, by combining and encapsulating multiple DSNPs with elements in the basic model, the definition of process and buffer service units is expanded to carry the dynamic process of FICM. Finally, PELM-DaE is proposed to achieve effective connection between production resources and effectively describe their execution logic.

[0044] Step 2: Define a directed service node pair (DSNP).

[0045] SE describes the form 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 triggering loading and unloading, and selecting parts to be cached or processed directly. Therefore, the present invention proposes DSNP to describe the above execution logic. In addition, by combining and connecting DSNPs, more complex equipment-level, unit-level and workshop-level execution logics can be formed.

[0046] First, by integrating FICM, the virtual service node is extended to a service node, which is an important component 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 The service node type is divided into path nodes, processing nodes, storage nodes and auxiliary nodes according to the different services performed by the service nodes. These nodes can carry both flow entities and different types of production information such as processing and storage time. For the convenience of discussion, the legend of the service node is formally defined, such as Figure 2 Shown on the left.

[0049] A nIt is the attribute element of the service node, including logistics path, processing node, buffer zone, etc. There is a corresponding relationship between node type and attribute, such as processing node corresponds to processing node, buffer zone corresponds to storage node, and there may be auxiliary nodes on processing node and buffer zone, which represent consumable resources. Path node corresponds to logistics path, which represents the location where the material will pass in the logistics process.

[0050] Pos is the coordinate of the service node, indicating the specific coordinate position where the material arrives and stops during the production process.

[0051] T s Service time refers to the time that materials (flow entities) stay in the service node and perform production activities after entering the service node.

[0052] S n It is the status of the service node, including idle, working and blocked, which 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 Indicates the conditions for a flow entity to enter a service node, determining whether to deny or allow the material to enter the node and initiate the service. For example, for a processing node on a machine tool, the access condition may be that the machine tool is idle or able to process the requested operation.

[0055] Des out 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 leaving condition may be that the AGV is idle and in position or that there is space in the buffer.

[0056] Compared with the definition of virtual service nodes in SE, defining service nodes expands them from logical space to real space, and adds dynamic constraints such as type and entry and exit decision conditions, so that service nodes can not only describe the material flow interaction structure between equipment, but also provide more fine-grained description and control of the production execution process.

[0057] The service node expresses the specific location that the material can reach in production, and is a mapping of its static spatial characteristics. The production execution activities in DMS are regarded as the dynamic flow of materials between service nodes, which reflects the dynamic spatial characteristics of materials. The execution logic is more complex, and an element is needed to describe the production activities at and between service nodes. Therefore, the concept of DSNP is proposed, which is defined as follows:

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

[0059] Among them, P p It is the type of DSNP, which describes different production execution activities according to the type of service node and its connection relationship, such as Figure 2 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 connecting edge between N1 and N2, indicating the flow relationship of materials between service nodes, which can be bidirectional (0), forward (1, indicating N1 to N2), or reverse (2, indicating N2 to N1).

[0062] T l It is the logistics time, which represents the time required to perform material transfer activities between two service nodes.

[0063] S e It is the status of the DSNP connection edge, including idle, working and blocked, which is affected by FICM and can reflect FICM.

[0064] S p It is the status of DSNP, including activated and inactivated, reflecting the execution process of production activities.

[0065] E is the executor of DSNP, which represents the logistics equipment that performs material transfer activities between two service nodes.

[0066] The DSNP model describes the spatial information of the production organization in the DMS. At the same time, it is necessary to use the FICM process in the time and space dimensions to define the DSNP advancement process in order to evaluate and simulate production execution activities, such as Figure 3 shown.

[0067] Figure 3 The purple part represents the production decision in DSNP, and the green part represents the production activity. First, the entry decision in DSNP is evaluated to control the entry of materials. After the materials enter, the service activity starts and lasts until T l The timeout indicates the end of the service. The decision is then evaluated. If the test passes, the logistics activity of transferring materials from N1 to N2 begins. The advancement of N2 is the same as N1, so it will not be repeated here.

[0068] It should be noted that the decision-making process not only determines whether the material can leave during the DSNP advancement process, but also makes a logical connection with the next DSNP.

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

[0070] δ represents the time when the element triggers the internal state transition. When the material arrives at the node, the entry condition of the service node will be evaluated. If it passes the entry judgment test, the material will enter the node and start serving. Its state is triggered by the external state transition from "idle" to "working". t is the service time of the service node. When t = T s When the node leaves, the node's leaving condition is evaluated. If the test passes, the material will leave the node and the node status will change to "Idle". If the test passes, the material will leave the service node and the node status will change to "Idle". If the test fails due to resource conflicts, the node status will change to "Blocked" and the material will stay in the node until the test passes.

[0071] For the connection edge between nodes in DSNP, when the material completes the service at node 1 and its Des out When the test passes, the edge status changes from "idle" to "working", and the logistics activity begins. l When the Des in If the test passes, the edge state will change to "Idle". Otherwise, the material cannot enter node 2 and the edge state changes to "Blocked" until the test passes.

[0072] For DSNP, the Des of the second node in the previous node pair is out Activated when the test passes. The node pair will be inactive until the Des out The test passes, which will activate the next DSNP if one exists.

[0073] Step 3: Define the Encapsulated Service Cell (ESC).

[0074] The DSNP proposed in the present invention represents the execution logic of basic production activities. By combining multiple DSNPs, more complex production execution logic can be achieved. In addition, due to the pursuit of higher production flexibility, shorter logistics time and simpler production management, the workshop production system is transforming into a unit production system. Unit production combines a series of production factors that can perform specific functions, specializes, collaborates and modularizes each production factor at the production level, thereby improving efficiency and reducing costs. This requires a production execution logic that can be encapsulated and supports flexible expansion and reconfiguration. The present invention expands the process and buffer service units in the basic model and proposes an encapsulated service unit (ESC), such as Figure 5By combining and encapsulating multiple DSNPs, the extended ESC not only describes the composition structure of the modular production unit, but also describes its dynamic production execution process.

[0075] The definitions of the two ESCs are as follows:

[0076]

[0077] Among them, C, P and B are control devices, processing devices and cache devices respectively.

[0078] It is a collection of actuators of ESC, used to realize the material flow therein.

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

[0080] LN set It is a collection of internal logistics paths of the ESC, which is used to realize the self-organization of actuators on the ESC path. For example, if an automatic warehouse is combined with a stacker as an ESC, the stacker track is the internal logistics path of the unit management.

[0081] and It is the infeed and outfeed location of the unit, representing the material interaction interface between the ESC and its external elements.

[0082] For the process encapsulation service unit PESC, and are its private input and output buffers.

[0083] For Buffered Encapsulated Service Units BESC, PESC set It is a collection of PESCs for which storage services can be provided.

[0084] The two ESCs realize the encapsulation of basic production functions. At the same time, during the production execution process, they can carry and integrate FICM based on the encapsulated DSNP to describe the dynamic execution process in the unit. Figure 6 The process of achieving FICM integration via DSNP encapsulation in ESCs is illustrated.

[0085] First, in the production execution process of DMS, each ESC has a specific job queue arranged according to the production plan, which maps the information flow of the ESC and drives its DSNP to perform production activities. Secondly, 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 is mainly reflected in the different ways in which the equipment in the ESC completes the same production task through different execution logics. For example, they can prioritize loading and unloading machine tools, or they can enter the buffer before performing these operations. This logic is controlled by the in / out decision function in the DSNP, which maps the control flow. Finally, under the guidance of the information flow and control flow, the materials advance in the execution order of the DSNP, and their actual flow routes map the material flow, such as Figure 7 Indicated by the green arrow.

[0086] It should be noted that the above description of the three flows is not fragmented. It encapsulates multiple DSNPs to organically integrate the processes and interactions of FICM within the ESC with consistent granularity, thereby achieving the description and encapsulation of the production execution logic within the unit and reducing the workload of repeatedly building DSNPs.

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

[0088] The DSNP and ESC proposed in the present invention realize the modular representation of the production execution logic, which carries and encapsulates the dynamic flow of FICM within the unit. However, it is still necessary to flexibly reconfigure the production execution logic at the workshop level to manage the production process of the entire workshop and promote the scheduling of production operations. Therefore, the present 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 function modules in the workshop.

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

[0092] and It is a collection of logistics paths and actuators at the workshop level, which together describe the interaction between ESCs in the workshop and are a mapping of the production activities in the workshop.

[0093] F set It is a flow entity set. 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 multi-variable, multi-parameter model that realizes modular and flexible reconstruction of workshop production execution logic in a dynamic environment by configuring the combination of DSNP and ESC. In addition, based on the encapsulation of ESC to DSNP, hierarchical modeling of workshop execution logic and unit execution logic can also be realized, avoiding the repeated construction of basic production execution activities through DSNP alone, making it easier and more practical to build workshop production execution logic based on this model.

[0095] Example:

[0096] 1. Actual production line analysis.

[0097] The present invention has developed a corresponding software platform to help other researchers implement the method of the present invention. Subsequently, a case study was conducted in an actual workshop. The production execution logic model of the workshop was constructed, and the improvement brought by the proposed method was demonstrated.

[0098] The workshop is called the Intelligent Manufacturing Demonstration Line (IMDL), which specializes in processing small and medium-sized structural parts. Figure 7 As shown. It is equipped with ten machine tools, two six-degree-of-freedom manipulators, three AGVs (one of which is equipped with a manipulator) and two automatic warehouses. It consists of five main areas: raw material and finished product storage area, coding and engraving area, two milling areas and finished product visual inspection area. All materials are coded by the laser engraving machine before processing, and quality inspection is carried out after processing before they can enter the finished product warehouse.

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

[0100] To construct PELM-DaE, we first need to identify the production resources in the workshop and map them to the production factor model, such as Figure 1 As shown. Secondly, the service locations of materials in production execution (including processing, logistics, warehousing, and consumption) need to be identified and mapped as service nodes. Then, the flow and interaction patterns between these locations need to be analyzed and mapped as connections in DSNP. Finally, the corresponding ESC is configured according to the service hierarchy and relationship in the actual execution process of the production equipment. Figure 8Key screenshots showing the IMDL graphical representation of PELM-DaE and the corresponding modeling performed in the platform.

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

[0102] In addition, 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, the present invention presents a case study of IDML's refined execution logic modeling. The present invention compares PELM-DaE with SE and MNOSE models to determine which model exhibits the best performance in modeling production execution logic. Example case Fig. 9 shown.

[0103] The embodiment includes two processing machines, one for laser engraving (#3) and the other for visual inspection (#4). There are two material positions in #3. Position 1 is the position for laser engraving operation, and the material is clamped to this position by a fixed robot (#2) from the outfeed conveyor (#5). Then, the robot (#1) with AGV grabs the workpiece from Position 1 and moves it for subsequent processing. When returning to the visual inspection process, #1 cannot go directly to #3 because it is restricted from avoiding mutual interference in logistics path planning. Therefore, Position 2 is used as a temporary storage location (no engraving). #1 transfers the material to, and #2 moves the material from Position 2 to #4. After the material is processed in #4, the material will be clamped to the feed conveyor (#6) by #2. Fig. 9 (bd) show the three models.

[0104] It can be seen that the SE model cannot describe the above production form because all material interactions depend on the logistics path and no service nodes are defined within the equipment. MNOSE defines nodes within the equipment and cascades single material points by defining the nodes of the actuator to express more detailed material flows. However, this method is still equipment-centric and cannot express the situation where multiple types of services are performed in a single device. Therefore, a virtual buffer is needed to describe the storage bit, but this increases the complexity of the model and reduces its interpretability for actual workshop production.

[0105] In addition, in MNOSE, the material transfer status 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 the material within the point, resulting in the inability to fully express FICM. For example, a material is transported from the starting point of conveyor belt #5 to the end point. However, after the current material reaches the end of #5, it cannot enter #3 and will stay and wait for the engraving of other materials. MNOSE cannot express the above more specific interaction process and control logic, resulting in the model being difficult to express the dynamic evolution of FICM during production execution.

[0106] PELM-DaE builds production logic from the perspective of service nodes and describes the dynamic execution process based on DSNP to integrate FICM. For information flow, the model expresses dynamic spatiotemporal production information from the material and machine dimensions through DSNP sequences and ESC job queues. For control flow, the model achieves more flexible control of materials through entry and exit decision judgments in DSNP. For material flow, the model is built from the perspective of service nodes and provides material flow representation and tracking at the granularity of a single material.

[0107] In general, compared with other methods, PELM-DaE can achieve the unification and integration of FICM in production execution logic. It can fully 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 workshop.

Claims

1. A discrete manufacturing workshop production execution logic modeling method, characterized in that: The production execution logic model PELM-DaE is constructed based on the directed service node pair DSNP and the encapsulation service unit ESC, which specifically includes the following steps: Step 1: Define the directed service node pair DSNP, expand the seven-element SE model, and consistently express the dynamic execution logic of basic production activities; Step 2: Define the encapsulated service unit ESC to realize the combination and functional and modular encapsulation of multiple directed service nodes to DSNP to express more complex production logic; at the same time, realize the carrying of information flow, control flow and material flow FICM in workshop production, and realize the flexible configuration and reconstruction of the production execution logic; Step 3: Construct the production execution logic model PELM-DaE, and realize the integrated representation of FICM in the workshop, as well as the effective expression and flexible reconstruction of the production execution logic at the workshop level by combining multiple directed service node pairs DSNP and encapsulated service units ESC.

2. A discrete manufacturing workshop production execution logic modeling method according to claim 1, characterized in that: The step 1 is specifically as follows: First, by integrating FICM, the virtual service node is extended to a service node, which is defined as follows: SN=<P n ,A n ,Pos,T s ,S n ,F set ,Des in ,Des out > (1) Among them, P n A is the service node type, which is divided into path node, processing node, storage node and auxiliary node according to the different services performed by the service node; A n is the attribute element of the service node, including logistics path, processing node, buffer zone, and there is a corresponding relationship between node type and attribute; Pos is the coordinate of the service node, indicating the specific coordinate position of the material arriving and stopping in the production process; T s S is the service time, which means the time that the material stays in the service node and performs production activities after entering the service node; n is the status of the service node, including idle, working and blocked; F set It is the set of flow entities currently executing services in the node; in Indicates the conditions for the flow entity to enter the service node, and determines whether to refuse or allow the material to enter the node and start the service; out Indicates the conditions under which the flow entity can leave the service node and go to the next service node; In order to describe the production activities at and between service nodes, the 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 is the type of DSNP, describing different production execution activities according to the type of service nodes and their connection relationships; N1 and N2 are two defined service nodes, representing the two locations in the material space where production activities will occur; Dir is the direction of the connecting edge between N1 and N2, indicating the flow relationship of materials between service nodes, which can be bidirectional, forward or reverse; T l is the logistics time, which means the time required to perform material transfer activities between two service nodes; S e is the state of the DSNP connection edge, including idle, working and blocked; S p It is the state of DSNP, including activation and inactivation, reflecting the execution process of production activities; E is the executor of DSNP, which represents the logistics equipment that performs material transfer activities between two service nodes.

3. A discrete manufacturing workshop production execution logic modeling method according to claim 2, characterized in that: The step 2 is specifically as follows: By combining and encapsulating multiple directed service node pairs DSNP, the composition structure of the modular production unit is described, and two encapsulated service units ESC are proposed, namely the process encapsulation service unit PESC and the buffer encapsulation service unit BESC, which are defined as follows: Among them, C, P and B are control device, processing device and cache device respectively; It is a set of actuators of ESC, used to realize the material flow; DSNP set It is a DSNP set in ESC, which is used to describe the production execution logic of the unit; LN set It is the set of internal logistics paths of ESC, which is used to realize the self-organization of actuators on the ESC path; and It is the feed and discharge location of the unit, representing the material interaction interface between the ESC and its external elements; For the process encapsulation service unit PESC, and Is its private input and output buffer; for the buffer encapsulation service unit BESC, PESC set It is a collection of PESCs for which storage services can be provided; At the same time, during the production execution process, the encapsulated directed service node carries and integrates FICM to DSNP, thereby describing the dynamic execution process in the unit: First, during the production execution process of DMS, each ESC has a specific job queue arranged according to the production plan, which maps the information flow of the ESC and drives its DSNP to perform production activities; second, due to the dynamic changes in resources and job status, the route to complete the same production job in the ESC may be different. This logic is controlled by the entry / exit decision function in the DSNP, which maps the control flow; finally, under the guidance of information flow and control flow, materials advance in the execution order of the DSNP, and their actual flow routes map the material flow.

4. A discrete manufacturing workshop production execution logic modeling method according to claim 3, characterized in that: The step 3 is specifically as follows: The flexible production execution logic model PELM-DaE with directed service node pairs DSNP and encapsulated service units ESC 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 function modules in the workshop; It is a collection of DSNPs at the workshop level; and It is a collection of workshop-level logistics paths and actuators, which together describe the interaction between workshop ESCs and are a mapping of workshop production activities; F set Is a fluid entity set.

Images