A digital twin simulation system based on a discrete workshop production logic model

Abstract

The application discloses a digital twin simulation system based on a discrete workshop production logic model, and comprises a production element acquisition module, which is used for acquiring workshop production related elements; a multi-dimensional workshop production logic model is constructed according to the workshop production related elements, a virtual scene three-dimensional model is established according to physical objects, a discrete workshop digital twin platform is built, an event-driven simulation logic algorithm same as an actual environment is formulated, simulation decisions are made through a contract network bidding mechanism, and discrete workshop production process simulation under the digital twin platform is realized. In combination with digital twin technology, database technology and simulation technology, the construction of a discrete workshop digital twin logic model and the simulation of a production process are realized. The application constructs an accurate, reasonable and efficient digital twin logic model for a discrete workshop, solves the problem of workshop production process simulation, enables workshop production problems to be found in advance, improves production efficiency, and guarantees the actual production.

Description

Technical Field

[0001] This invention relates to the field of digital twin technology, and in particular to a digital twin simulation system based on a discrete workshop production logic model. Background Technology

[0002] With economic development, personalized, diversified, and customized products are gradually becoming the mainstream demand of customers. How to fully utilize advanced technologies such as the Internet of Things, artificial intelligence, and big data to provide intelligent simulation support for the workshop production process, achieving timely delivery and improving customer satisfaction, has become a key issue in enhancing enterprise competitiveness. To adapt to the current needs of multi-variety, small-batch products, flexible manufacturing has become the main production method in discrete manufacturing workshops. Due to the characteristics of the products themselves, their manufacturing exhibits the characteristics of discrete production modes such as many varieties, small batches, and short cycles, making workshop production processes difficult to control, production scenarios more complex, production factors relatively independent, and lacking interaction and integration between information space and physical space data. To ensure that workshop production plans can meet production needs, simulation of the discrete workshop production process is an essential step. Using digital twin technology to build a digital twin logical model that closely resembles the actual workshop scenario, the entire process and all elements of the production process can be integrated and merged. By simulating the layout, process paths, and logistics of the actual workshop, the production process can be simulated and evaluated. This achieves a virtual-real mapping and iterative operation mechanism for all elements of the production process, enabling early detection of production problems, improving workshop production efficiency, and reducing resource waste. Summary of the Invention

[0003] To address the problems and shortcomings of existing technologies, the present invention aims to provide a digital twin simulation system based on a discrete workshop production logic model. This system combines digital twin technology, database technology, and simulation technology to construct a digital twin logic model of the discrete workshop and simulate the production process. As a digital twin simulation system for discrete workshop production processes, its purpose is to construct an accurate, reasonable, and efficient digital twin logic model for the discrete workshop, solve the simulation problems of the workshop production process, enable the early detection of workshop production problems, improve production efficiency, and ensure the smooth progress of actual production.

[0004] This application proposes a digital twin simulation system based on a discrete workshop production logic model. This system acquires various production attributes of the workshop entity through a production element acquisition module, abstracts the workshop production elements from geometric, physical, behavioral, and rule dimensions, and thus constructs a multi-dimensional workshop production logic model. By establishing a virtual scene 3D model based on physical objects, a discrete workshop digital twin platform is built. Event-driven simulation logic identical to the actual environment is formulated, and simulation decisions are made through a contract network bidding mechanism, thereby realizing the simulation of the discrete workshop production process under the digital twin platform.

[0005] The production factor acquisition module is used to collect production-related factors in the workshop. It includes a production attribute acquisition section, a data processing section, and a data transmission section. Production factors can be divided into active factors (including machine tools, logistics equipment, warehousing equipment, buffer equipment, control equipment, etc.) and passive factors (including materials, logistics paths, etc.). The production attribute acquisition section includes acquiring production attributes such as the category, quantity, processing status, and order of production factors. The data processing section includes identifying, classifying, and converting the acquired information into a unified data format. The data transmission section transmits all collected information to the database for storage.

[0006] Implementation Method: As a next-generation OPC standard, OPC UA unifies the transmission protocols and data interfaces of various IoT sensing devices, providing an effective means of information exchange between virtual and physical workshops. Using OPC UA as the primary transmission method, an OPC UA server is constructed to access multi-source data. An OPC UA address space is built according to the information model to expose interfaces for various data types, achieving unified collection and standardized transmission of multiple data types. Based on the data model, machine tool and sensor data are processed using KepOPC to form a primary OPC UA server. For enterprise system software such as ERP, MES, and SCADA, which typically have dedicated data transmission interfaces, data transmission is processed using the RabbitMQ message queue from the Spring Boot middleware, ultimately forming the OPC UA server.

[0007] The multi-dimensional workshop production logic model includes attribute modeling (including geometric parameters and physical properties) and behavioral modeling (including production behavior and simulation rules) of workshop production elements. Attribute modeling includes modeling static attributes such as layout rules, state, input / output types, capacity, and processing time of production elements, while behavioral modeling includes modeling element input / output behavior, state transitions, and time progression steps.

[0008] Implementation method: Classify physical entities that logically perform similar functions in the workshop production process, and abstract various types of manufacturing equipment in the actual production process from the dimensions of geometric parameters, physical attributes, production behavior and simulation rules, and build a multi-dimensional workshop production logic model in an object-oriented manner.

[0009] The discrete workshop digital twin platform utilizes the Unity3D engine platform to quickly build virtual scenes based on the actual workshop layout and models in the model library.

[0010] The event-driven simulation logic, through the advancement of the simulation clock, continuously adds or removes events to ensure that all events occur in the correct order during the simulation process. The workshop digital twin system simulation based on event scheduling first models the system input characteristics of the simulation object, using an abstract model to describe the actual production process. Then, based on simulation requirements and the actual workshop conditions, the generated simulation samples are selected to determine the sample input generator. Finally, the simulation event processing logic is described based on the workshop production flow, and the system simulation process is completed by triggering different event processing logics.

[0011] Implementation method: The finite state machine model formally models the behavior of elements. It describes the state queue and the transition conditions between states. Then, by defining the input behavior and output behavior, it defines the special, formal dynamic behavior or processing procedure for each state transition.

[0012] The contract network bidding mechanism simulates decision-making, whereby the contract manager issues tasks to individuals responsible for executing them; the individuals executing the tasks submit bids based on their own information and send them to the contract manager; the contract manager evaluates these bids and assigns the tasks to the most suitable individuals.

[0013] Implementation method: The workshop entity is abstracted into various member agents as bidders, and the tenderer is the workshop's decision agent. When a task needs to be tendered, the tenderer generates and broadcasts the tender information; the bidders receive the tender, calculate their bid value based on their own capabilities, generate and send their bid information; the tenderer receives all the bid information, evaluates the bids, generates and issues the winning bid information; the bidders receive the winning bid information, determine whether they have won the bid, and the winning bidder generates feedback information; the bidders receive the feedback from the winning bidder, and the tendering process for one task ends.

[0014] This invention constructs a discrete workshop digital twin simulation system based on digital twin technology and discrete production process modeling technology. Its advantages are:

[0015] 1. The system adopts a design pattern that separates the view, model, and control, which facilitates the expansion of system functions and daily maintenance.

[0016] 2. Data interaction is achieved by adopting the OPC UA information modeling method. The OPC UA protocol unifies and standardizes the protocols of various devices, establishes a standardized interface for data transmission, and presents information of different formats generated by different types of devices in a standard format, thereby achieving effective data collection and management and enabling the integration and transmission of multi-source heterogeneous data in the digital twin platform.

[0017] 3. A digital twin simulation algorithm based on the discrete workshop production logic model is proposed. The discrete workshop production logic model is established, and the model has geometric, physical, behavioral and rule attributes to realize the simulation of the discrete workshop production process.

[0018] 4. By introducing a unified logic model method, the behavior of elements is formally modeled. The constructed production logic model has a high degree of modularity, making it easy to build and test the model independently. The logic model and simulation algorithm have high reusability and flexibility. Attached Figure Description

[0019] Figure 1 This is a diagram of the architecture of a digital twin simulation system based on a discrete workshop production logic model.

[0020] Figure 2 This is a diagram of a discrete workshop information model based on OPC UA.

[0021] Figure 3 A multi-dimensional production logic model diagram for discrete workshops;

[0022] Figure 4 This is a schematic diagram of the simulation rules based on a finite state machine.

[0023] Figure 5 The flowchart is for an event-driven discrete shop floor production simulation algorithm.

[0024] Figure 6 This is a flowchart of a simulation decision-making algorithm based on a contract network bidding mechanism. Detailed Implementation

[0025] This application proposes a digital twin simulation system based on a discrete workshop production logic model. It includes a production factor acquisition module for acquiring relevant workshop production factors. Based on these factors, a multi-dimensional workshop production logic model is constructed. A three-dimensional virtual scene model is built according to physical objects, and a discrete workshop digital twin platform is established. An event-driven simulation logic algorithm identical to the actual environment is formulated. Simulation decisions are made through a contract network bidding mechanism, thereby realizing the simulation of the discrete workshop production process under the digital twin platform.

[0026] The production factor acquisition module includes a production attribute acquisition unit, a data processing unit, and a data transmission unit. The production factors are divided into active factors, including machine tools, logistics equipment, warehousing equipment, buffer equipment, and control equipment, and passive factors, including materials and logistics paths. The production attribute acquisition unit acquires production attributes such as the category, quantity, processing status, and orders of the production factors. The data processing unit identifies, classifies, and converts the acquired information into a unified data format. The data transmission unit transmits all the acquired information to the database for storage.

[0027] The multi-dimensional workshop production logic model includes attribute modeling based on workshop production-related elements, including modeling based on geometric parameters, physical attributes, and behavior, including modeling based on production behavior and simulation rules. Attribute modeling includes modeling based on the layout rules, state, input / output types, capacity, and static attributes of production elements. Behavioral modeling includes modeling the input / output behavior, state transitions, and time progression steps of elements. Physical entities that logically perform similar functions in the workshop production process are categorized. Various physical manufacturing equipment in the actual production process are abstracted from the dimensions of geometric parameters, physical attributes, production behavior, and simulation rules, and a multi-dimensional workshop production logic model is constructed in an object-oriented manner.

[0028] The production factor acquisition module accesses multi-source data by building an OPC UA server, uses KepOPC to form a first-level OPC UA server, and processes data transmission according to the RabbitMQ message queue of the Spring Boot middleware, ultimately forming the OPC UA server.

[0029] The discrete workshop digital twin platform utilizes the Unity3D engine platform to build a virtual scene based on the actual workshop layout and models in the model library.

[0030] The event-driven simulation logic algorithm adds or removes events by advancing the simulation clock; it uses a finite state machine model to formally model the behavior of elements, describes the state queue and the transition conditions between states, and then defines the dynamic behavior or processing procedure for each state transition by defining input and output behaviors.

[0031] The OPC UA server stores information models, dividing the workshop information model into five types of information concept models: Device-Inf, Person-Inf, Material-Inf, Environment-Inf, and Task-Inf. These five information concept models and the overall model are related as part-of-part to part-of-part. For a processing workshop, several Device-Inf, Person-Inf, Material-Inf, and Environment-Inf objects are first instantiated based on the collected data to represent all production elements of the workshop. Then, based on the tasks and plans in the workshop, several Task-Inf objects are instantiated to describe the tasks in the workshop.

[0032] The event-driven simulation logic algorithm is driven by discrete events. First, it reads the simulation input data from the database, initializes the workshop simulation model layout, material data, and simulation termination conditions, and sorts the logic model and material data. The second step checks whether the termination conditions are met.

[0033] For production planning simulation, the purpose of the simulation is to obtain the minimum time required to produce a limited quantity of materials. The materials stop leaving the warehouse after reaching the preset quantity. The algorithm stops when all processes of all preset quantities of materials have been processed. The termination condition is that the quantity of processed materials is equal to the preset quantity of materials to be left out of the warehouse.

[0034] For capacity simulation, the purpose of the simulation is to obtain the maximum material output within a certain time. The termination condition is that the current simulation clock equals the simulation end time. If the termination condition is not met, it indicates that there are still untriggered events in the model. The minimum time advance of all logical models can be obtained by querying.

[0035] The third step is to iterate through the list of logic models at the point when the clock is about to advance, call the output behavior function of the model. If the function returns TRUE, it means that the model can output materials. Continue to call the device selection algorithm. The device selection algorithm iterates through the downstream logic models connected to the current model, finds the logic model that can receive material output, and returns a pointer to the corresponding model instance. If the returned pointer is not null, it means that there is a logic model that can receive material input. Call the input behavior function of the returned model to input the material.

[0036] The fourth step is to advance the simulation clock. After the clock advances, if the termination condition is met, the simulation variables are calculated and the simulation result information is output. If the condition is not met, return to the second step.

[0037] The contract network bidding mechanism simulation decision-making process is as follows: The contract manager issues tasks to the individuals responsible for executing them; the individuals executing the tasks submit bids based on their own information and send them to the contract manager; the contract manager evaluates these bids and assigns the task to the most suitable individual; workshop entities are abstracted into various member agents as bidders, and the bidding entity is the workshop's decision agent. When a task needs to be bid on, the bidding entity generates and broadcasts the bidding information; bidders receive the bidding, calculate their bid value based on their capabilities, and generate and send their bid information; the bidding entity receives all the bid information, evaluates the bids, and generates and issues the winning bid information; bidders receive the winning bid information, determine whether they have won the bid, and the winning bidder generates feedback information; bidders receive the winning bidder's feedback, and the bidding for one task ends.

[0038] Example 1

[0039] like Figure 1The diagram shown is a hierarchical diagram of the simulation system architecture of this application. The system architecture is divided into four layers: The first layer is the physical workshop layer, which refers to the entities within the workshop, such as machine tools, AGVs, robotic arms, automated storage systems, sensors, and radio frequency identification (RFID), used to receive manufacturing instructions and execute manufacturing tasks, while simultaneously sensing and processing heterogeneous, multi-source, real-time data based on the Internet of Things; the second layer is the virtual workshop layer, which refers to the virtual workshop model, real-time workshop mapping, and workshop information display; the third layer is the workshop data layer, serving as the data source and transmission channel; and the fourth layer is the application service layer, which refers to the set of technologies supporting intelligent production management and control functions and target requirements. Below... Figures 2-5 The specific definitions and implementation methods of each layer are introduced separately.

[0040] like Figure 2 As shown, this is an OPC UA information model for a virtual twin workshop. Based on the concept of physical information fusion, the overall workshop information model is divided into five types of information concept models: Device-Inf, Person-Inf, Material-Inf, Environment-Inf, and Task-Inf. These five information concept models have a part-of-the-world relationship with the overall model. For a processing workshop, several Device-Inf, Person-Inf, Material-Inf, and Environment-Inf objects are first instantiated based on the collected data to represent all production elements of the workshop. Then, based on the tasks and plans in the workshop, several Task-Inf objects are instantiated to describe the tasks in the workshop.

[0041] like Figure 3 As shown, this is a multi-dimensional production logic model for discrete workshops. A multi-dimensional production logic model for discrete workshops is a digital model that realizes the attributes, methods, and behaviors of physical entities and processes in digital space. It possesses geometric properties (GP) that describe the size and position of physical entities in the production workshop, such as people, machines, materials, and the environment; physical properties (PP) that display the processing methods and states of equipment; production behaviors (PB) that characterize the sequentiality and linkage of production workshop behaviors; and simulation rules (SR) established based on the operation and evolution of the workshop. Therefore, the model is described as follows:

[0042] TLM = {GP, PP, PB, SR}

[0043] The multi-dimensional production logic model for discrete workshops is based on entities in the physical production line. It extracts production information such as the size of the physical entities, workshop layout design, and entity processing methods during the production process, which serve as the geometric parameters and physical attributes of the digital twin logic model. Then, based on the production behaviors that the physical entities may engage in during actual production, an event-driven finite state transition model is constructed, enabling the digital twin logic model to possess behavioral characteristics, response mechanisms, and the ability to perform state transitions. Finally, based on the geometric attributes, physical attributes, and production behaviors, simulation rules for the digital twin logic model of the production physical entities are established. The behavioral information generated during the simulation process is transmitted to related models through a logic pipeline, thereby enabling various model devices to evolve and operate.

[0044] like Figure 4 As shown, the simulation rules are based on a finite state machine. The initial state of the model is set to waiting, and the interval between its next events is infinite. The behavior of the workstation is δ. in This is the input of materials. When materials are input at the workstation, if:

[0045] a) If the workstation capacity is full, the status changes to "full load," and the interval between the next events is the remaining processing time T of the material at the head of the processing queue. left ;

[0046] b) If the workstation does not meet the processing quantity (i.e., the batch processing does not fill the workstation), the status will not be changed;

[0047] c) If the workstation meets the processing conditions, it will start processing materials. If the workstation is in a "waiting" state, the state will be changed to "processing". If the workstation is in a "blocked" or "working" state, the state will not be changed.

[0048] workstation δ out The action is to output materials. When a workstation outputs materials, if:

[0049] a) If the workstation is in "working" state and materials can be output, then output the materials and change the status to "waiting" or "working" according to the quantity of products.

[0050] b) If the workstation is in "working" state and the material cannot be output, the material will remain in the workstation and the workstation status will change to "blocked".

[0051] c) If the workstation is in a "full load" state and materials can be output, then output the materials, the state changes to "working," and the interval between the next events is the remaining processing time T of the material at the head of the processing queue. left ;

[0052] d) If the workstation is in a "full load" state and the material cannot be output, the material will remain in the workstation, the workstation state will change to "blocked", and the interval between its next events will be infinite.

[0053] like Figure 5 As shown, this is an event-driven workshop production simulation algorithm. Driven by discrete events, the algorithm first reads the simulation input data from the database, initializes the workshop simulation model layout, material data, simulation termination conditions, etc., and sorts the logical model and material data. The second step is to check whether the termination condition is met. This termination condition varies depending on the simulation objective: for production planning simulation, the simulation objective is to obtain the minimum time required to produce a limited quantity of materials. Material outbound stops after reaching a preset outbound quantity. The algorithm stops after all processes for all preset quantities of materials have been processed. The termination condition is that the quantity of processed materials equals the preset outbound quantity, i.e., NUM. endw =NUM w For capacity simulation, the goal is to obtain the maximum material output within a certain time period. The termination condition is that the current simulation clock equals the simulation end time, i.e., T. clock =T end If the termination condition is not met, it indicates that there are still untriggered events in the model. The minimum time advance ta for all logical models is obtained through querying. The third step is to find the time point T′ at which the clock is about to advance. clock =T olook +ta, iterates through the list of logical models and calls the model's output behavior function δ. out If the function returns TRUE, it means that the model can output materials. The device selection algorithm is then called. This algorithm iterates through the downstream logic models connected to the current model, searching for a logic model that can receive material output, and returns a pointer to the corresponding model instance. If the returned pointer is not null, it means that a logic model exists that can receive material input, and the input behavior function δ of the returned model is called. in The first step is to input the materials; the fourth step is to advance the simulation clock. After the clock advances, if the termination condition is met, the simulation variables are calculated and the simulation result information is output. If the condition is not met, the process returns to the second step.

[0054] like Figure 6As shown, the simulation decision-making algorithm based on the contract network bidding mechanism involves a decision-making agent collecting all service requirements within a time window. The workshop production logic model decomposes the tasks, breaking them down into multiple sub-tasks that each individual execution agent can complete independently. These sub-tasks are then continuously distributed to all execution agents. Each execution agent receives each sub-task sequentially, determines whether its own skills can meet the requirements of the sub-task, and then calculates its bid value for the sub-task based on its real-time state using the bid value calculation function given below, determining the sub-task's position in its local task sequence. The decision-making agent determines the task allocation scheme, plans the task execution scheme for the execution agents, and sends the command to start task execution to the relevant execution agents. The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Those skilled in the art can make several modifications and adjustments within the technical scope disclosed in this invention. Therefore, the scope of protection of this invention should be determined by the scope of the claims.

Claims

1. A digital twin simulation system based on a discrete workshop production logic model, characterized in that, The simulation system includes a production factor acquisition module, which is used to acquire workshop production-related factors; a multi-dimensional workshop production logic model is constructed based on the workshop production-related factors; a three-dimensional virtual scene model is built according to physical objects; a discrete workshop digital twin platform is built; an event-driven simulation logic algorithm identical to the actual environment is formulated; and simulation decisions are made through a contract network bidding mechanism to realize the simulation of the discrete workshop production process under the digital twin platform. The event-driven simulation logic algorithm is driven by discrete events. First, it reads the simulation input data from the database, initializes the workshop simulation model layout, material data, and simulation termination conditions, and sorts the logic model and material data. The second step checks whether the termination conditions are met. For production planning simulation, the purpose of the simulation is to obtain the minimum time required to produce a limited quantity of materials. The materials stop leaving the warehouse after reaching the preset quantity. The algorithm stops when all processes of all preset quantities of materials have been processed. The termination condition is that the quantity of processed materials is equal to the preset quantity of materials to be left out of the warehouse. For capacity simulation, the purpose of the simulation is to obtain the maximum material output within a certain time. The termination condition is that the current simulation clock equals the simulation end time. If the termination condition is not met, it indicates that there are still untriggered events in the model. The minimum time advance of all logical models can be obtained by querying. The third step is to iterate through the list of logic models at the point when the clock is about to advance, call the output behavior function of the model. If the function returns TRUE, it means that the model can output materials. Continue to call the device selection algorithm. The device selection algorithm iterates through the downstream logic models connected to the current model, finds the logic model that can receive material output, and returns a pointer to the corresponding model instance. If the returned pointer is not null, it means that there is a logic model that can receive material input. Call the input behavior function of the returned model to input the material. The fourth step is to advance the simulation clock. After the clock advances, if the termination condition is met, the simulation variables are calculated and the simulation result information is output. If the condition is not met, return to the second step. The contract network bidding mechanism simulation decision-making process is as follows: the contract manager issues tasks to the individuals responsible for executing them; the individuals executing the tasks submit bids based on their own information and send them to the contract manager; the contract manager evaluates these bids and assigns the tasks to the most suitable individuals. The workshop entity is abstracted into various member agents as bidders, and the tenderer is the workshop's decision agent. When there is a task that needs to be tendered, the tenderer will generate and broadcast the tender information; the bidders receive the tender, calculate the bid value based on their own capabilities, and generate and send the bid information. The tendering party receives all the tender information, evaluates the tenders, and generates and issues the winning bid information; Bidders receive the winning bid information, determine whether they have won the bid, and the winning bidder generates feedback information; bidders receive the feedback from the winning bidder, and the bidding process for one task ends.

2. The digital twin simulation system based on a discrete workshop production logic model according to claim 1, characterized in that, The production factor acquisition module includes a production attribute acquisition unit, a data processing unit, and a data transmission unit. The production factors are divided into active factors, including machine tools, logistics equipment, warehousing equipment, buffer equipment, and control equipment, and passive factors, including materials and logistics paths. The production attribute acquisition unit acquires the category, quantity, processing status, and order production attributes of the production factors. The data processing unit identifies, classifies, and converts the acquired information into a unified data format. The data transmission unit transmits all the acquired information to the database for storage.

3. The digital twin simulation system based on a discrete workshop production logic model according to claim 1, characterized in that, The multi-dimensional workshop production logic model includes attribute modeling based on workshop production-related elements, including modeling based on geometric parameters, physical attributes, and behavior, including modeling based on production behavior and simulation rules, including modeling based on the layout rules, state, input / output types, capacity, and static attributes of production elements, and behavior modeling including modeling of element input / output behavior, state transitions, and time progression steps. Physical entities that logically perform similar functions in the workshop production process are categorized and abstracted from the dimensions of geometric parameters, physical attributes, production behavior and simulation rules. A multi-dimensional workshop production logic model is then constructed in an object-oriented manner.

4. A digital twin simulation system based on a discrete workshop production logic model according to claim 2, characterized in that, The production factor acquisition module accesses multi-source data by building an OPC UA server, uses KepOPC to form a first-level OPC UA server, and processes data transmission according to the RabbitMQ message queue of the Spring Boot middleware, ultimately forming an OPC UA server.

5. A digital twin simulation system based on a discrete workshop production logic model according to any one of claims 1 to 4, characterized in that, The discrete workshop digital twin platform utilizes the Unity3D engine platform to build a virtual scene based on the actual workshop layout and models in the model library.

6. A digital twin simulation system based on a discrete workshop production logic model according to claim 1, characterized in that, The event-driven simulation logic algorithm adds or removes events by advancing the simulation clock. A finite state machine model is used to formally model the behavior of elements, describing the state queue and the transition conditions between states. Then, by defining the input and output behaviors, the dynamic behavior or processing procedure of each state transition is defined.

7. A digital twin simulation system based on a discrete workshop production logic model according to claim 4, characterized in that, The OPC UA server stores information models, dividing the workshop information model into five types of information concept models: Device-Inf, Person-Inf, Material-Inf, Environment-Inf, and Task-Inf. These five information concept models and the overall model are related as part-of-part. For a processing workshop, several Device-Inf, Person-Inf, Material-Inf, and Environment-Inf objects are first instantiated based on the collected data to represent all production elements of the workshop. Then, based on the tasks and plans in the workshop, several Task-Inf objects are instantiated to describe the tasks in the workshop.

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