NC-Link-based intelligent factory digital twin model construction method, device, equipment and medium

Abstract

The invention relates to an NC-Link-based intelligent factory digital twin model construction method, device, equipment and medium, and the method comprises the steps: decomposing an intelligent factory into a plurality of hierarchies including an equipment hierarchy, a unit hierarchy and a workshop hierarchy in a digital twin modeling layer, describing the geometric attributes of production line equipment and accessory equipment based on a CAD model, and constructing a digital twin model; defining an equipment motion structure based on the multi-body kinematics model, and associating a spatial position corresponding to the physical environment data collected by the environment perception sensor so as to construct a three-dimensional visual geometric model; building an event trigger logic and a state conversion rule of the virtual object, and forming an interaction model reflecting a workshop production process; and establishing a bidirectional mapping relationship among the spatial information of the geometric model, the operation logic of the interaction model and the real-time data of the data model so as to synchronously update the entity state of the equipment and the virtual object. The intelligent factory is promoted to be transformed into data-driven intelligent management and control, and the production efficiency and the management level are improved.

Description

Technical Field

[0001] This application relates to the field of data processing, and in particular to a method for constructing a digital twin model of a smart factory based on NC-Link, as well as corresponding devices, electronic equipment and computer-readable storage media. Background Technology

[0002] In recent years, digital twin technology has received increasing attention, and numerous digital twin platforms have come into focus. Siemens has developed a platform called MindSphere, which adopts the Industry 4.0 concept and connects to machines via the cloud to achieve digital twins. PTC's ThingWorx platform focuses on collecting industrial internet data and providing valuable information to users through an intuitive user interface. The core of digital twins is the integration of the physical and virtual worlds; physical entities and their corresponding virtual models, data, connections, and services are the core components of a digital twin. The three core steps in realizing digital twin technology in the industrial internet are: data acquisition, multi-source heterogeneous data cleaning and fusion, and digital twin modeling.

[0003] Regarding the fusion of multi-source heterogeneous data, to address the difficulties in data acquisition caused by the heterogeneity of factory data sources, the Advanced Manufacturing Technology Association (AMT) and the National Institute of Standards and Technology (NIST) proposed the MTConnect protocol to unify the data formats of different CNC systems and equipment, thereby enabling data acquisition and information sharing. The OPC Foundation, an international standards organization, organically integrated the data acquisition, alarm and event, historical data access, batch, and security protocol specifications in the OPC standard, developing a service-oriented industrial control interconnection protocol, OPC UA, that supports common communication and information abstraction; further, it proposed the UMATI protocol standard to unify data formats. Currently, most domestic data acquisition uses foreign interconnection protocols, leading to security risks. Furthermore, the different data formats between different protocols also cause significant challenges to data twinning.

[0004] In the area of ​​digital twin modeling, the construction of a digital twin model is a prerequisite for the practical application of digital twins. In the field of industrial internet, many scholars both domestically and internationally have conducted research on model construction, mainly covering the following aspects:

[0005] 1) Production factor modeling: Current research or applications of digital twin factor modeling mainly focus on the construction of geometric models to support the monitoring of factory status, but there is insufficient characterization of the physical, behavioral, and rule-based aspects of the workshop.

[0006] 2) Multidimensional model fusion. Current modeling research mostly focuses on single-level objects such as key components, equipment or production lines, and lacks systematic research on the fusion of multidimensional models from "unit level to system level to complex system level".

[0007] 3) Regarding production process data modeling, features were extracted from different time and space scales in the time and frequency domains to uncover the correlation between production state features and data and to establish a dynamic mapping model of production line status. However, the extracted features were numerous and redundant, and the correlation between different features and states varied greatly, making it difficult to construct an accurate production line process model.

[0008] 4) Production system simulation and modeling. Current simulations are mostly aimed at a single purpose or function, such as workshop layout simulation, workshop scheduling simulation, and workshop logistics simulation. They do not adequately consider the global simulation of the coordination of all elements, resulting in one-sided simulation results.

[0009] In summary, current research and applications of digital twin modeling for digital twin elements mainly focus on the construction of geometric models to support the monitoring of factory status. However, they lack sufficient characterization of the physical, behavioral, and rule-related aspects of the workshop. Furthermore, current simulations are mostly aimed at a single purpose or function, with insufficient consideration for global simulation of the coordination of all elements, leading to the one-sidedness of simulation results. To address these issues, the applicant has made corresponding explorations. Summary of the Invention

[0010] The purpose of this application is to solve the above problems by providing a method for constructing a digital twin model of a smart factory based on NC-Link, as well as corresponding devices, electronic devices and computer-readable storage media.

[0011] To achieve the various objectives of this application, this application adopts the following:

[0012] A method for constructing a digital twin model of a smart factory based on NC-Link, proposed to meet one of the purposes of this application, includes the following steps:

[0013] In the sensing device layer, physical environment data corresponding to environmental sensing sensors, equipment operation data corresponding to production line equipment, and equipment status signals corresponding to auxiliary equipment are collected. Based on the physical environment data, the equipment operation data, and the equipment status signals, multi-source heterogeneous data of the smart factory is constructed.

[0014] In the data perception and fusion layer, the multi-source heterogeneous data is associated with the virtual objects corresponding to the environmental perception sensors, production line equipment, and auxiliary equipment to determine the workshop level, equipment location, and real-time attribute data corresponding to the environmental perception sensors, production line equipment, and auxiliary equipment. The protocol adaptation judgment and distribution layer identifies the data transmission protocol corresponding to the multi-source heterogeneous data, and encapsulates the multi-source heterogeneous data into NC-Link DTI format to determine the standardized data in NC-Link DTI format. The data distribution layer transmits the standardized data in NC-Link DTI format to the NC-Link agent, and stores it in encrypted form according to the data type and importance.

[0015] In the digital twin modeling layer, the smart factory is decomposed into multiple levels including equipment level, unit level, and workshop level. Based on the preset CAD model, the geometric attributes of production line equipment and auxiliary equipment are described. Based on the multibody kinematics model, the motion structure of the equipment is defined, and the spatial position corresponding to the physical environment data collected by the environmental perception sensor is associated to construct a three-dimensional visualized geometric model.

[0016] Using the standardized data in the NC-Link DTI format as input, the event triggering logic and state transition rules of the virtual object are constructed to form an interactive model that reflects the workshop production process;

[0017] By real-time access to standardized data in NC-Link DTI format stored hierarchically through the data interaction layer, and by using the IDEF0 modeling method to establish a two-way mapping relationship between the spatial information of the geometric model, the operating logic of the interaction model, and the real-time data of the data model, the physical state of the equipment and the virtual object are updated synchronously to complete the construction of a digital twin model of a smart factory based on NC-Link.

[0018] Optionally, the step of associating the multi-source heterogeneous data with the virtual objects corresponding to the environmental sensing sensors, the production line equipment, and the auxiliary equipment to determine the workshop level, equipment location, and real-time attribute data corresponding to the environmental sensing sensors, the production line equipment, and the auxiliary equipment includes:

[0019] In the virtual space, virtual objects are created corresponding to the environmental sensing sensors, production line equipment, and auxiliary equipment in the smart factory. The virtual objects include hierarchical location attributes and functional attributes. The hierarchical location attributes include the workshop number, production line number, equipment body number, and core component number of the equipment entity. The functional attributes include the equipment entity type, data acquisition range, and preset sampling frequency.

[0020] A mapping system between physical devices and virtual objects is constructed. Based on the hierarchical location attributes and functional attributes, an association data structure between physical devices and virtual objects is constructed. The physical environment data, the device operation data, and the device status signals are respectively mapped to the relevant fields of their corresponding association data structures.

[0021] Optionally, the process involves identifying the data transmission protocol corresponding to the multi-source heterogeneous data through a protocol adaptation judgment and splitting layer, uniformly encapsulating the multi-source heterogeneous data into NC-Link DTI format to determine standardized data in NC-Link DTI format; and having the data distribution layer transmit the standardized data in NC-Link DTI format to the NC-Link agent, where it is encrypted and stored hierarchically according to data type and importance. This includes the following steps:

[0022] The protocol adaptation judgment layer receives multi-source heterogeneous data in the smart factory to identify the data transmission protocol corresponding to the multi-source heterogeneous data;

[0023] The system detects whether the data transmission protocol is the NC-Link protocol. If not, it parses the multi-source heterogeneous data and decomposes it into root objects, data objects, component objects, device objects, internal method objects, and sampling channel objects in the NC-Link data model. After completing the data fusion and adaptation, the multi-source heterogeneous data is uniformly encapsulated into the NC-Link DTI format to determine the standardized data of the NC-Link DTI format.

[0024] The data distribution layer transmits the standardized data in NC-Link DTI format to the NC-Link agent. The NC-Link agent performs hierarchical encryption processing on the standardized data according to the different data types of the standardized data in NC-Link DTI format and the importance level of industrial digital twin modeling, and stores the encrypted standardized data.

[0025] Optionally, the smart factory is decomposed into multiple levels, including equipment level, unit level, and workshop level. The geometric attributes of production line equipment and auxiliary equipment are described based on a preset CAD model. The motion structure of the equipment is defined based on a multi-body kinematics model, and the spatial location corresponding to the physical environment data collected by the environmental perception sensors is associated with the model. This process, to construct a three-dimensional visualized geometric model, includes the following steps:

[0026] The smart factory is decomposed into multiple levels, including equipment level, unit level, and workshop level, and the subordinate relationships and spatial associations between each level are determined.

[0027] Obtain a preset CAD model, and describe the geometric attribute information of production line equipment and auxiliary equipment at each level in the smart factory based on the CAD model. The geometric attribute information includes the shape, size, color and assembly relationship of the equipment.

[0028] A multibody kinematics model is established, and the motion structure information of each production line equipment and auxiliary equipment is defined based on the multibody kinematics model. The motion structure information includes at least the topological relationship, degrees of freedom and motion axis stroke between equipment components.

[0029] The physical environment data collected by environmental sensing sensors in the smart factory is acquired, and the physical environment data is associated with the corresponding hierarchical spatial locations after the smart factory is decomposed, so as to determine the specific corresponding location of each physical environment data in the smart factory space.

[0030] By integrating the geometric attribute information, the motion structure information, and the physical environment data of the associated spatial location, a three-dimensional visual geometric model of the smart factory is constructed.

[0031] Optionally, the step of constructing the event triggering logic and state transition rules of the virtual object, using the standardized data in the NC-Link DTI format as input, to form an interactive model reflecting the workshop production process, includes:

[0032] Based on the standardized data in the NC-Link DTI format, key event information associated with virtual objects is extracted. The virtual objects correspond to the equipment entities of environmental sensing sensors, production line equipment, and auxiliary equipment at the equipment level, unit level, and workshop level in the smart factory. The key event information includes equipment start-up and shutdown events, process switching events, material transfer events, and abnormal alarm events.

[0033] Based on the extracted key event information, the event triggering logic of the virtual object is constructed, and the triggering conditions of each key event are determined. The triggering conditions are associated with the threshold of specific data items or data change rules in the NC-Link DTI format standardized data.

[0034] Based on the event triggering logic of the virtual object and combined with the workshop production process, the state transition rules of the virtual object are defined, wherein the state transition rules include the state transition relationship of the virtual object under different event triggers;

[0035] By integrating the event triggering logic and state transition rules of the virtual objects, an interactive model is formed that reflects the interaction relationships, operating mechanisms, and dynamic changes of equipment entities in the workshop production process.

[0036] Optionally, the real-time access of standardized data in NC-Link DTI format stored hierarchically through the data interaction layer, and the establishment of a bidirectional mapping relationship between the spatial information of the geometric model, the operational logic of the interaction model, and the real-time data of the data model using the IDEF0 modeling method, to synchronize and update the physical state of the device with the virtual object, includes the following steps:

[0037] The data interaction layer obtains standardized data in NC-Link DTI format from the NC-Link agent;

[0038] The IDEF0 modeling method is used to construct a bidirectional mapping relationship. The geometric model, interaction model and data model of the digital twin modeling layer are used as the core modeling objects. The spatial information of the geometric model and the operation logic of the interaction model are used as control elements. The NC-Link DTI format standardized data are used as input elements. The virtual and real state synchronization results are used as output elements. The real-time data transmission mechanism of the data interaction layer is used as mechanism elements. The IDEF0 model diagram from the top layer to the detail layer is drawn to establish a bidirectional mapping relationship between the spatial information of the geometric model, the operation logic of the interaction model and the real-time data of the data model.

[0039] When the standardized NC-Link DTI format data accessed by the data interaction layer changes dynamically, the corresponding attributes of the virtual object are updated in real time. If the physical environment data or equipment operation data changes, the spatial state parameters of the geometric model and the operation logic triggering conditions of the interaction model are updated synchronously through the mapping relationship. If the equipment status signal changes, the status identifier of the interaction model and the visualization status of the geometric model are updated synchronously through the bidirectional mapping relationship. At the same time, based on the simulation analysis results of the interaction model, equipment control optimization instructions are generated and transmitted back to the equipment entity through the data interaction layer to synchronize the status of the equipment entity with the virtual object.

[0040] Optionally, the physical environment data includes workshop vibration data, temperature data, and distance data; the equipment operation data includes rotational speed data, displacement data, and load data; and the equipment status signals include normal signals, alarm signals, and shutdown signals.

[0041] The production line equipment includes CNC machine tools, AGV carts, industrial robots, and material storage; the auxiliary equipment includes programmable logic controllers, CNC machine tools, and protocol adapters.

[0042] A smart factory digital twin model construction device based on NC-Link, provided for another purpose of this application, includes:

[0043] The data sensing module is configured to collect physical environment data corresponding to environmental sensing sensors, equipment operation data corresponding to production line equipment, and equipment status signals corresponding to auxiliary equipment in the sensing device layer, and construct multi-source heterogeneous data of the smart factory based on the physical environment data, the equipment operation data, and the equipment status signals.

[0044] The data fusion module is configured to associate the multi-source heterogeneous data with the virtual objects corresponding to the environmental sensing sensors, production line equipment, and auxiliary equipment in the data perception and fusion layer, so as to determine the workshop level, equipment location, and real-time attribute data corresponding to the environmental sensing sensors, production line equipment, and auxiliary equipment. Through the protocol adaptation judgment and distribution layer, the module identifies the data transmission protocol corresponding to the multi-source heterogeneous data, and encapsulates the multi-source heterogeneous data into NC-Link DTI format to determine the standardized data in NC-Link DTI format. The data distribution layer transmits the standardized data in NC-Link DTI format to the NC-Link agent, and stores it in encrypted form according to the data type and importance.

[0045] The geometric model construction module is configured to decompose the smart factory into multiple levels, including equipment level, unit level, and workshop level, in the digital twin modeling layer. It describes the geometric attributes of production line equipment and auxiliary equipment based on a preset CAD model, defines the motion structure of equipment based on a multibody kinematics model, and associates the spatial position corresponding to the physical environment data collected by the environmental perception sensor to construct a three-dimensional visualized geometric model.

[0046] The interaction model construction module is configured to take the standardized data in the NC-Link DTI format as input, construct the event triggering logic and state transition rules of the virtual object, and form an interaction model that reflects the workshop production process.

[0047] The digital twin model construction module is configured to receive standardized data in NC-Link DTI format through hierarchical storage of the data interaction layer in real time. It uses the IDEF0 modeling method to establish a two-way mapping relationship between the spatial information of the geometric model, the operation logic of the interaction model, and the real-time data of the data model, so as to synchronize the status of the physical equipment with the virtual object and complete the construction of the digital twin model of the smart factory based on NC-Link.

[0048] An electronic device provided for another purpose of this application includes a central processing unit and a memory, the central processing unit being configured to invoke and run a computer program stored in the memory to perform the steps of the NC-Link-based smart factory digital twin model construction method described in this application.

[0049] A computer-readable storage medium is provided for another purpose of this application, which stores, in the form of computer-readable instructions, a computer program implemented according to the method for constructing a smart factory digital twin based on NC-Link, which, when invoked by a computer, executes the steps included in the corresponding method.

[0050] Compared to existing technologies, this application addresses the problems of existing digital twin modeling research or applications, which mainly focus on the construction of geometric models to support factory status monitoring, lacking sufficient multi-dimensional characterization of the workshop's physics, behavior, and rules, and that current simulations are mostly aimed at a single purpose or function, failing to consider the global simulation of all elements working together, leading to the one-sidedness of simulation results. This application includes, but is not limited to, the following beneficial effects:

[0051] Firstly, at the level of multi-source heterogeneous data processing, this application effectively solves the problems of existing technologies relying on foreign protocols and having chaotic data formats. By identifying mainstream industrial protocols such as MTConnect, OPC-UA, and Modbus-TCP through a protocol adaptation judgment and distribution layer, non-NC-Link protocol data is decomposed into NC-Link data model objects and encapsulated in NC-Link DTI format. This eliminates dependence on foreign protocols, avoids risks and data security vulnerabilities, and ensures the autonomy of industrial data interaction. Furthermore, it achieves unified standardization of multi-source heterogeneous data, eliminating data conflicts caused by format differences and providing a consistent data foundation for subsequent modeling and applications. Simultaneously, the data distribution layer uses hierarchical encrypted storage based on data type and modeling importance. This ensures the storage security and retrieval efficiency of high-priority data (such as equipment operating parameters and abnormal alarm signals), optimizes storage resource allocation, avoids resource waste caused by redundant storage, and provides stable and secure data source support for digital twins.

[0052] Secondly, at the modeling level, this application significantly improves the accuracy and construction efficiency of the digital twin model through structured design and multi-dimensional integration. Firstly, it adopts a hierarchical decomposition strategy of "equipment-unit-workshop," breaking down the complex smart factory into precisely controllable sub-units, clarifying the hierarchical hierarchy and spatial relationships, avoiding overall modeling chaos. Simultaneously, based on CAD models and multi-body kinematic models, it restores the geometric attributes and motion structures of the equipment, associating them with the spatial location of environmental perception data. The constructed 3D visualized geometric model can highly match the characteristics of physical equipment, providing a high-fidelity geometric foundation for subsequent simulation and monitoring. Secondly, through a one-to-one mapping between virtual objects and physical equipment, and by extracting key events based on NC-Link DTI data, constructing event triggering logic and state transition rules, the resulting interactive model can realistically reflect the operating mechanism and dynamic interaction relationships of equipment in the workshop production process, overcoming the shortcomings of existing modeling which is limited to a single dimension. Furthermore, the association between virtual objects and physical equipment does not require manual configuration one by one. By constructing a mapping system through hierarchical location and functional attributes, it significantly reduces manual operation and configuration errors, improves modeling efficiency, and shortens the digital twin implementation cycle.

[0053] Thirdly, this application constructs a deep "physical-virtual" linkage mechanism through bidirectional mapping and dynamic synchronization, significantly enhancing the control and optimization capabilities of smart factories. The data interaction layer accesses NC-Link DTI standardized data in real time, establishing a bidirectional mapping relationship between the geometric model, interaction model, and data model based on the IDEF0 modeling method. When physical equipment data changes, it can drive the virtual object attribute update in real time, ensuring consistency between the virtual model and the physical factory state. This enables precise real-time monitoring of the workshop's operating status, facilitating timely detection of equipment anomalies or production bottlenecks by maintenance personnel. More importantly, this mechanism supports reverse optimization: simulation analysis results based on the interaction model can generate equipment control optimization commands and transmit them back to the physical equipment, enabling dynamic adjustment and optimization of the production process. For example, simulation can optimize the AGV's running path to improve material transfer efficiency, or simulation can adjust processing parameters based on equipment operating data to improve product qualification rate, forming a closed loop of "data acquisition - modeling simulation - optimization feedback." This helps smart factories achieve lean production and efficient control, improving overall production efficiency and market competitiveness.

[0054] Fourth, this application significantly reduces reliance on manual operation through automated data processing and modeling mechanisms, promoting the upgrading of industrial production towards automation and intelligence. In the data association stage, a mapping system is constructed through the hierarchical location and functional attributes of virtual objects, automatically matching multi-source heterogeneous data to corresponding virtual objects. This eliminates the need for manual configuration of sensor-model relationships, avoiding configuration errors caused by human experience bias, and saving significant manpower and time costs. In the operation monitoring and optimization stage, the digital twin model can synchronize the physical factory status in real time and automatically issue early warnings of anomalies, reducing the workload and risk of missed inspections during manual inspections. The reverse optimization mechanism enables automatic adjustment of the production process, reducing the frequency of manual intervention and improving the automation level of the production process. Overall, this application, through the deep application of digital twin technology, promotes the transformation of smart factories from traditional manual control to data-driven intelligent control, improving production efficiency and management level. Attached Figure Description

[0055] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0056] Figure 1 This is an exemplary network architecture diagram used in the workshop data perception and interaction technology architecture for digital twins in the embodiments of this application;

[0057] Figure 2 This is an exemplary network architecture diagram of the multi-source twin information perception and fusion framework based on the NC-Link protocol in the embodiments of this application;

[0058] Figure 3 This is a flowchart illustrating an embodiment of this application;

[0059] Figure 4 This is a schematic diagram of the smart factory digital twin model construction device based on NC-Link in the embodiments of this application;

[0060] Figure 5 This is a schematic diagram of the structure of the computer device in the embodiments of this application. Detailed Implementation

[0061] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0062] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this application means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0063] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0064] Unless otherwise specified, all data involved in this application may be stored remotely on a server or on a local terminal device, as long as it is suitable for being accessed by this application.

[0065] Those skilled in the art will understand that although the various methods in this application are described based on the same concept and thus present commonality among them, they can be performed independently unless otherwise specified. Similarly, the various embodiments disclosed in this application are all based on the same inventive concept; therefore, concepts expressed in the same way, as well as concepts that are appropriately changed for convenience but are expressed differently, should be understood equivalently.

[0066] Unless otherwise expressly stated, the various embodiments disclosed in this application can be combined in a cross-cutting manner to flexibly construct new embodiments, as long as such combination does not depart from the inventive spirit of this application and can meet the needs of the prior art or solve a certain deficiency in the prior art. Those skilled in the art should be aware of such modifications.

[0067] Please see Figure 1The NC-Link-based intelligent factory digital twin model construction method of this application can be implemented based on a digital twin-oriented workshop data perception and interaction technology architecture. Addressing the characteristics of physical workshop data—multi-source, heterogeneous, large-scale, and time-varying—this architecture includes a sensing device layer, a data perception and fusion layer, a digital twin modeling layer, and a data interaction layer. Specifically, the sensing device layer is responsible for sensing and collecting data from multiple sources of equipment and sensors in the workshop; the data perception and fusion layer is responsible for virtual sensor data mapping and heterogeneous twin data fusion; the former maps sensor data from physical space to virtual space, while the latter converts and adapts the data from heterogeneous devices; the digital twin modeling layer maps physical workshop data to its virtual objects and establishes data connections between the underlying data and upper-layer applications, thus realizing digital twin modeling.

[0068] Digital twins are multi-dimensional, multi-scale, and multi-probabilistic simulation models. Multi-dimensional model fusion is a crucial means of achieving high-fidelity modeling. To achieve high-fidelity modeling, the physical workshop needs to be characterized from different dimensions. In traditional simulations, simulation parameters cannot be automatically obtained directly from the physical object; they are generally set as statistical values ​​or estimates based on human experience. In actual production processes, machine tool operating time is closely related to specific processes.

[0069] In summary, through analysis of the heterogeneity, dynamism, and randomness of distributed industrial internet manufacturing workshops, as well as the characteristics of digital twin multidimensional model fusion, this project proposes a digital twin modeling method based on multidimensional model fusion to address the heterogeneity, dynamism, and randomness of distributed industrial internet manufacturing workshops. This method includes the following three steps:

[0070] 1) Configurable Heterogeneous Manufacturing Shop Modeling: The manufacturing system is decomposed, its basic elements and relationships are identified and defined, and a unified graphical and formal modeling specification is established to adapt to the characteristics of various heterogeneous manufacturing shops. Then, basic modeling elements, modules, and the configuration and combination rules of modules are defined to adapt to the dynamic updates and reconstruction of shop layout and production mode.

[0071] 2) Modeling based on discrete event systems: Building upon configurable modeling, this paper leverages the advantages of discrete event system modeling methods to establish a unified formal and graphical logical representation method for discrete manufacturing workshops. A logical model of the events, states, and behaviors of the simulated entities is constructed, mapping the physical workshop's operating mechanism to achieve simulation of the workshop's operation process, analysis of random disturbances, and prediction of workshop behavior.

[0072] 3) Modeling for multi-dimensional model fusion: The physical workshop is characterized from three different dimensions: geometric model, interaction model and data model. Based on the IDEF0 modeling method, information models at different levels such as equipment, unit and workshop are established to realize the integration and encapsulation of different levels of workshop models for digital twin.

[0073] like Figure 1 As shown, the NC-Link-based intelligent factory digital twin model construction method of this application characterizes physical entities from three dimensions: geometric model, data model, and interaction model, and realizes applications at different stages through the gradual integration of the three models and the physical entity. First, a configurable heterogeneous manufacturing workshop modeling method is used to establish a geometric model, which describes the geometric attributes (e.g., shape, color, assembly relationships, etc.) and motion structures (e.g., topological relationships between parts, degrees of freedom, motion axis travel, etc.) of the physical object. Geometric attributes are described using a CAD model, while motion structures are described based on multibody kinematics and topological models, providing a three-dimensional visualization operating environment for the simulation and monitoring of the physical object. Then, a discrete event system modeling method is used to describe the logical relationships, operating mechanisms, and states and behaviors of manufacturing equipment in the workshop, establishing a workshop interaction model. Finally, enabled by the Industrial Internet and industrial big data, data perception, processing, transmission, storage, and analysis are realized to form a data model. The data model is the bridge connecting the physical entity and the digital entity. On the one hand, through model configuration and data updates, the geometric model and the interaction model can maintain consistency with the state of the physical object at all times, enabling the digital entity to have the potential for full lifecycle simulation. On the other hand, with the improvement of the accuracy and real-time performance of simulation input, physical objects / systems can be simulated and continuously optimized online. At this point, the construction of the digital twin multidimensional model is complete.

[0074] Based on the above exemplary scenario, please refer to Figure 2 In one embodiment of the method for constructing a smart factory digital twin model based on NC-Link, the present application includes the following steps:

[0075] Step S10: Collect physical environment data corresponding to environmental sensing sensors, equipment operation data corresponding to production line equipment, and equipment status signals corresponding to auxiliary equipment in the sensing device layer; and construct multi-source heterogeneous data for the smart factory based on the physical environment data, the equipment operation data, and the equipment status signals.

[0076] In the perception device layer of the workshop data perception and interaction technology architecture for digital twins, physical environment data corresponding to environmental perception sensors, equipment operation data corresponding to production line equipment, and equipment status signals corresponding to auxiliary equipment can be collected. Based on the physical environment data, the equipment operation data, and the equipment status signals, multi-source heterogeneous data of the smart factory can be constructed.

[0077] In some embodiments, the physical environment data includes workshop vibration data, temperature data, and distance data; the equipment operation data includes rotational speed data, displacement data, and load data; the equipment status signals include normal signals, alarm signals, and shutdown signals; the production line equipment includes CNC machine tools, AGVs, industrial robots, and material storage; and the auxiliary equipment includes programmable logic controllers, CNC machine tools, and protocol adapters.

[0078] Step S20: In the data perception and fusion layer, the multi-source heterogeneous data is associated with the virtual objects corresponding to the environmental perception sensors, the production line equipment, and the auxiliary equipment to determine the workshop level, equipment location, and real-time attribute data corresponding to the environmental perception sensors, production line equipment, and auxiliary equipment. The protocol adaptation judgment and distribution layer identifies the data transmission protocol corresponding to the multi-source heterogeneous data, and the multi-source heterogeneous data is uniformly encapsulated into NC-Link DTI format to determine the standardized data of NC-Link DTI format. The data distribution layer transmits the standardized data of NC-Link DTI format to the NC-Link agent and stores it in encrypted form according to the data type and importance.

[0079] In the sensing device layer, physical environment data corresponding to environmental sensing sensors, equipment operation data corresponding to production line equipment, and equipment status signals corresponding to auxiliary equipment are collected. After constructing multi-source heterogeneous data for the smart factory based on the physical environment data, equipment operation data, and equipment status signals, in the data sensing and fusion layer, the multi-source heterogeneous data is associated with the virtual objects corresponding to the environmental sensing sensors, production line equipment, and auxiliary equipment to determine the workshop level, equipment location, and real-time attribute data corresponding to the environmental sensing sensors, production line equipment, and auxiliary equipment. Through the protocol adaptation judgment and distribution layer, the data transmission protocol corresponding to the multi-source heterogeneous data is identified, and the multi-source heterogeneous data is uniformly encapsulated into NC-Link DTI format to determine the standardized data in NC-Link DTI format. The standardized data in NC-Link DTI format is transmitted to the NC-Link agent by the data distribution layer and stored in encrypted form according to the data type and importance.

[0080] In some embodiments, to map sensing sensors from physical space to virtual space, the concept of virtual sensors is introduced, linking physical devices, real-time attribute data, and virtual objects to realistically reflect the distribution and real-time status of sensors or real-time attribute data in the physical workshop. The agreed data structure format is VSDATA = <Workshop Number><Production Line Number><Equipment Number><Component Number><Monitoring Variable Name><Data Type><Data Unit><Upper Limit of Value><Lower Limit of Value><Sampling Frequency><Current Value><Timestamp>. Here, the workshop number, production line number, equipment number, and component number define the workshop level and distribution location corresponding to the virtual sensor; the monitoring variable name, data type, data unit, and sampling frequency define the basic attributes of the real-time attribute data; the upper and lower limits of the value define the normal range of the monitored value, exceeding which will trigger an alarm; and the current value and timestamp directly reflect the current status of the real-time attribute data.

[0081] In a further embodiment, a smart factory may contain various industrial devices, such as sensors, CNC machine tools, production lines, and robots. To address the challenges of data interaction caused by different devices potentially using various Industrial Internet protocols, an adapter for the NC-Link protocol is researched. Its architecture is mainly divided into four layers: a protocol adaptation and distribution layer, a data parsing layer, a data fusion and adaptation layer, and a data distribution layer.

[0082] Please see Figure 3 First, data is automatically and dynamically collected by sensing devices. The protocol judgment module of the adapter judges and distributes the protocol information, but no operation is performed on NC-Link data. Then, the parsed metadata is adapted and integrated with the NC-Link protocol, and decomposed into "root objects", "data objects", "component objects", "device objects", "internal method objects", "sampling channel objects", etc. in the NC-Link data model to realize the construction of the NC-Link industrial equipment model. When the data is forwarded or transmitted upward, it is encapsulated into NC-Link DTI using the NC-Link data transmission interface. Finally, the data distribution module distributes the data to the NC-Link agent for automatic hierarchical encryption and storage. At this point, the multi-source heterogeneous information of the industrial Internet is unified and integrated into domestic NC-Link information.

[0083] In some embodiments, the step of associating the multi-source heterogeneous data with virtual objects corresponding to the environmental sensing sensors, the production line equipment, and the auxiliary equipment to determine the workshop level, equipment location, and real-time attribute data corresponding to the environmental sensing sensors, the production line equipment, and the auxiliary equipment includes:

[0084] Step S201: Create virtual objects in the virtual space corresponding to the environmental perception sensors, production line equipment and auxiliary equipment in the smart factory. The virtual objects include hierarchical location attributes and functional attributes. The hierarchical location attributes include the workshop number, production line number, equipment body number and core component number of the equipment entity. The functional attributes include the equipment entity type, data acquisition range and preset sampling frequency.

[0085] Step S202: Construct a mapping system between device entities and virtual objects. Based on the hierarchical location attributes and functional attributes, construct an association data structure between physical devices and virtual objects, and map the physical environment data, the device operation data, and the device status signals to the relevant fields of their corresponding association data structures.

[0086] As described in steps 201 and 202 above, virtual objects corresponding one-to-one with physical devices are created in the virtual space. The spatial positioning of these virtual objects is clearly defined through hierarchical location attributes, avoiding mismatches between physical devices and virtual objects, thus laying the foundation for the "physical-virtual mirror" of the digital twin. A mapping system and associated data structure are constructed based on the device's hierarchical location and functional attributes, mapping multi-source heterogeneous data to structural fields, eliminating data format and attribute ambiguities, and providing a standardized data foundation for subsequent data fusion (such as NC-Link DTI format conversion). Through the mapping of virtual object functional attributes and data, the workshop level, location, and real-time attribute data of the physical devices are directly determined, eliminating the need for manual reverse calculation, reducing configuration errors, and improving the efficiency and accuracy of digital twin modeling.

[0087] In a further embodiment, the protocol adaptation judgment and diversion layer identifies the data transmission protocol corresponding to the multi-source heterogeneous data, and uniformly encapsulates the multi-source heterogeneous data into NC-Link DTI format to determine standardized data in NC-Link DTI format; the data distribution layer transmits the standardized data in NC-Link DTI format to the NC-Link proxy, and stores it in encrypted form according to the data type and importance hierarchy, including the following steps:

[0088] Step S2001: The protocol adaptation judgment layer receives multi-source heterogeneous data in the smart factory to identify the data transmission protocol corresponding to the multi-source heterogeneous data.

[0089] Step S2002: Detect whether the data transmission protocol is the NC-Link protocol. If not, parse the multi-source heterogeneous data and decompose it into root objects, data objects, component objects, device objects, internal method objects, and sampling channel objects in the NC-Link data model. After completing the data fusion and adaptation, encapsulate the multi-source heterogeneous data into the NC-Link DTI format to determine the standardized data of the NC-Link DTI format.

[0090] Step S2003: The data distribution layer transmits the standardized data in NC-Link DTI format to the NC-Link agent. The NC-Link agent performs hierarchical encryption processing on the standardized data according to the different data types of the standardized data in NC-Link DTI format and the importance level of industrial digital twin modeling, and stores the encrypted standardized data.

[0091] As described in steps 2001 to 2003 above, the protocol adaptation judgment and distribution layer identifies the transmission protocols of multi-source heterogeneous data, specifically parses non-NC-Link protocol data and decomposes it into NC-Link data model objects, achieving compatibility between mainstream industrial protocols such as MTConnect and OPC-UA and the domestic NC-Link protocol, ensuring data interaction security. Multi-source heterogeneous data is uniformly encapsulated into the NC-Link DTI format, eliminating format differences between different protocols and providing a standardized data foundation for subsequent digital twin modeling (such as geometric model and interaction model data access), avoiding modeling errors caused by format confusion. Hierarchical encrypted storage according to data type and modeling importance ensures both the storage security and ease of access for high-priority data (such as equipment operating parameters), while hierarchical management optimizes storage resource allocation, improves data read / write efficiency, and provides reliable support for real-time data access for digital twin models.

[0092] Step S30: In the digital twin modeling layer, the smart factory is decomposed into multiple levels including equipment level, unit level and workshop level. Based on the preset CAD model, the geometric attributes of production line equipment and auxiliary equipment are described. Based on the multibody kinematics model, the motion structure of the equipment is defined. The spatial position corresponding to the physical environment data collected by the environmental perception sensor is associated to construct a three-dimensional visualized geometric model.

[0093] The protocol adaptation judgment and diversion layer identifies the data transmission protocol corresponding to the multi-source heterogeneous data, and encapsulates the multi-source heterogeneous data into NC-Link DTI format to determine the standardized data in NC-Link DTI format. The data distribution layer transmits the standardized data in NC-Link DTI format to the NC-Link agent. After being encrypted and stored hierarchically according to the data type and importance, in the digital twin modeling layer, the smart factory is decomposed into multiple levels including equipment level, unit level, and workshop level. Based on the preset CAD model, the geometric attributes of production line equipment and auxiliary equipment are described. Based on the multibody kinematics model, the motion structure of the equipment is defined, and the spatial position corresponding to the physical environment data collected by the environmental perception sensor is associated to construct a three-dimensional visualized geometric model.

[0094] In some embodiments, the smart factory is decomposed into multiple levels including equipment level, unit level, and workshop level. The geometric attributes of production line equipment and auxiliary equipment are described based on a preset CAD model. The motion structure of the equipment is defined based on a multibody kinematics model. The spatial location corresponding to the physical environment data collected by the environmental perception sensors is associated with the model. The steps to construct a three-dimensional visualized geometric model include the following:

[0095] Step S301: Decompose the smart factory into multiple levels, including equipment level, unit level and workshop level, and determine the subordinate relationship and spatial association between each level.

[0096] The equipment hierarchy includes environmental sensing sensors, core production line equipment, and auxiliary support equipment. The environmental sensing sensors include temperature sensors, vibration sensors, etc.; the core production line equipment includes CNC machine tools, AGVs, industrial robots, etc.; and the auxiliary support equipment includes PLC controllers, air compressors, power distribution cabinets, etc. The unit hierarchy includes processing units, material handling units, or power support units, etc. The processing unit integrates CNC machine tools, tooling fixtures, cutting fluid supply equipment, vision inspection sensors, etc., and is responsible for processing parts and initial quality inspection. The material handling unit integrates AGVs, automated warehouse stacker cranes, material identification sensors, etc., and is responsible for the storage and transfer of raw materials, semi-finished products, and finished products. The power support unit integrates air compressors, power distribution cabinets, cooling water tanks, etc., and is responsible for providing power and energy to all equipment in the factory. The workshop hierarchy includes a precision machining workshop, a finished product storage unit, or a final assembly workshop. The precision machining workshop integrates raw material warehousing units, processing units, quality inspection units, finished product storage units, etc., to realize a complete processing flow from raw material entry to finished product output. The final assembly workshop integrates parts sorting units, robot assembly units, off-line inspection units, etc., to complete the final assembly and quality acceptance of the product.

[0097] Step S302: Obtain a preset CAD model, and describe the geometric attribute information of production line equipment and auxiliary equipment at each level in the smart factory based on the CAD model. The geometric attribute information includes the shape, size, color and assembly relationship of the equipment.

[0098] Step S302: Establish a multibody kinematics model, and define the motion structure information of each production line equipment and auxiliary equipment based on the multibody kinematics model. The motion structure information includes at least the topological relationship, degrees of freedom and motion axis travel between equipment components.

[0099] Step S303: Obtain the physical environment data collected by the environmental perception sensors in the smart factory, associate the physical environment data with the corresponding hierarchical spatial locations after the smart factory is decomposed, and determine the specific corresponding location of each physical environment data in the smart factory space.

[0100] Step S304: Integrate the geometric attribute information, the motion structure information, and the physical environment data of the associated spatial location to construct a three-dimensional visualized geometric model of the smart factory.

[0101] As shown in steps 301 to 303 above, the intelligent factory is divided into equipment, unit, and workshop levels through hierarchical decomposition. This clarifies the hierarchy and spatial relationships of each level, avoiding confusion in overall modeling and laying a structured foundation for subsequent accurate modeling. Based on the CAD model, the geometric attributes of the equipment are described, and the motion structure is defined using a multibody kinematics model. This fully restores key information such as the equipment's shape, size, component topology, and degrees of freedom, ensuring a high degree of consistency between the geometric model and the physical equipment. Physical environment data is associated with corresponding hierarchical spatial locations, so that the geometric model not only contains equipment information but also integrates environmental dimension data, improving model completeness. Simultaneously, the 3D visualization facilitates intuitive understanding of the factory's status, providing high-fidelity model support for subsequent digital twin simulation and monitoring.

[0102] Step S40: Using the standardized data in the NC-Link DTI format as input, construct the event triggering logic and state transition rules of the virtual object to form an interactive model that reflects the workshop production process;

[0103] In the digital twin modeling layer, the smart factory is decomposed into multiple levels, including equipment level, unit level, and workshop level. Based on the preset CAD model, the geometric attributes of production line equipment and auxiliary equipment are described. Based on the multibody kinematics model, the motion structure of the equipment is defined. The spatial position corresponding to the physical environment data collected by the environmental perception sensor is associated to construct a three-dimensional visualized geometric model. Then, with the standardized data in the NC-Link DTI format as input, the event triggering logic and state transition rules of the virtual object are constructed to form an interactive model that reflects the workshop production process.

[0104] In some embodiments, the step of constructing the event triggering logic and state transition rules of the virtual object, using standardized data in the NC-Link DTI format as input, to form an interactive model reflecting the workshop production process includes:

[0105] Step S401: Based on the standardized data in the NC-Link DTI format, extract the key event information associated with the virtual object. The virtual object corresponds to the equipment entity of environmental perception sensors, production line equipment and auxiliary equipment in the equipment level, unit level and workshop level of the smart factory. The key event information includes equipment start-up and shutdown events, process switching events, material transfer events and abnormal alarm events.

[0106] Step S402: Based on the extracted key event information, construct the event triggering logic of the virtual object and determine the triggering conditions for each key event. The triggering conditions are associated with specific data item thresholds or data change rules in the NC-Link DTI format standardized data.

[0107] Step S403: Based on the event triggering logic of the virtual object and combined with the workshop production process, define the state transition rules of the virtual object, wherein the state transition rules include the state transition relationship of the virtual object under different event triggers;

[0108] Step S404: Integrate the event triggering logic and state transition rules of the virtual object to form an interactive model that reflects the interaction relationship, operating mechanism and dynamic change process of equipment entities in the workshop production process.

[0109] As seen in steps 401 to 404 above, key events such as equipment start-up and shutdown, and process switching are extracted based on standardized data in the NC-Link DTI format. These events are directly related to the core production process in the workshop, providing precise "event anchors" for the construction of the interaction model, avoiding interference from irrelevant information, and ensuring that the model focuses on key production links. Event triggering conditions are bound to specific data item thresholds or change rules in the standardized data, enabling dynamic association between virtual object event triggering and physical equipment data (e.g., equipment operating parameters exceeding thresholds triggering alarm events), ensuring synchronization between virtual events and physical equipment states, and achieving "dynamic response" of the interaction model. Combined with the workshop production process flow, virtual object state transition rules are defined, clarifying the state transition relationship under event triggering (e.g., process switching events triggering equipment to transition from "standby" to "running" state), enabling the interaction model to truly reflect the operating logic of physical workshop equipment and the connection with the production process, laying a logical foundation for the subsequent construction of a complete interaction model.

[0110] Step S50: Real-time access of standardized data in NC-Link DTI format stored hierarchically through the data interaction layer; establishment of a two-way mapping relationship between the spatial information of the geometric model, the operation logic of the interaction model, and the real-time data of the data model using the IDEF0 modeling method, so as to synchronize the status of the physical equipment with the virtual object, thereby completing the construction of a digital twin model of a smart factory based on NC-Link.

[0111] Using the standardized data in the NC-Link DTI format as input, the event triggering logic and state transition rules of the virtual object are constructed to form an interactive model reflecting the workshop production process. Then, the standardized data in the NC-Link DTI format, which is stored hierarchically in the data interaction layer, is accessed in real time. The IDEF0 modeling method is used to establish a two-way mapping relationship between the spatial information of the geometric model, the operation logic of the interactive model, and the real-time data of the data model, so as to synchronize the status of the equipment entity with the virtual object and complete the construction of the digital twin model of the smart factory based on NC-Link.

[0112] In some embodiments, the step of real-time access to standardized data in NC-Link DTI format stored hierarchically through the data interaction layer, and establishing a bidirectional mapping relationship between the spatial information of the geometric model, the operational logic of the interaction model, and the real-time data of the data model using the IDEF0 modeling method to synchronize and update the device entity state with the virtual object includes the following steps:

[0113] Step S501: The data interaction layer obtains the standardized data in NC-Link DTI format from the NC-Link agent;

[0114] Step S502: Construct a bidirectional mapping relationship using the IDEF0 modeling method. Take the geometric model, interaction model, and data model of the digital twin modeling layer as the core modeling objects. Use the spatial information of the geometric model and the operation logic of the interaction model as control elements, the NC-Link DTI format standardized data as input elements, the virtual-real state synchronization results as output elements, and the real-time data transmission mechanism of the data interaction layer as mechanism elements. Draw an IDEF0 model diagram from the top layer to the detail layer to establish a bidirectional mapping relationship between the spatial information of the geometric model, the operation logic of the interaction model, and the real-time data of the data model.

[0115] Step S503: When the standardized data in NC-Link DTI format accessed by the data interaction layer changes dynamically, the corresponding attributes of the virtual object are updated in real time. If the change is in physical environment data or equipment operation data, the spatial state parameters of the geometric model and the operation logic triggering conditions of the interaction model are updated synchronously through the mapping relationship. If the change is in equipment status signal, the status identifier of the interaction model and the visualization status of the geometric model are updated synchronously through the bidirectional mapping relationship. At the same time, based on the simulation analysis results of the interaction model, equipment control optimization instructions are generated and transmitted back to the equipment entity through the data interaction layer to synchronize the status of the equipment entity with the virtual object.

[0116] As shown in steps 501 to 503 above, the data interaction layer obtains standardized data from the NC-Link agent, ensuring a unified data source and standardized format. This provides a real-time and reliable data source for subsequent virtual-physical synchronization, avoiding the impact of data delays or format inconsistencies on the synchronization effect. The IDEF0 modeling method is adopted to clarify the spatial information of the geometric model, the operational logic of the interaction model, and the input, control, mechanism, and output elements of the real-time data of the data model. A structured bidirectional mapping relationship is established, making the data interaction logic between multiple models clear and reducing the complexity of virtual-physical association. Data changes drive the real-time update of virtual object attributes, ensuring consistency between the geometric model, interaction model, and physical equipment states. Simultaneously, optimization instructions are generated based on simulation results to control the equipment in reverse, forming a closed loop of "data access - state synchronization - optimization feedback," enhancing the dynamic control and optimization capabilities of the digital twin for the smart factory.

[0117] As can be seen from the above embodiments, compared with the prior art, the research or application of digital twin modeling in the prior art mainly focuses on the construction of geometric models to support the monitoring of factory status. It is insufficient in the multi-dimensional characterization of the workshop's physics, behavior, rules, etc., and the current simulations are mostly aimed at a single purpose or function, with insufficient consideration for the global simulation of the coordination of all elements, resulting in the one-sidedness of simulation results. The present application has the following beneficial effects, including but not limited to:

[0118] Firstly, at the level of multi-source heterogeneous data processing, this application effectively solves the problems of existing technologies relying on foreign protocols and having chaotic data formats. By identifying mainstream industrial protocols such as MTConnect, OPC-UA, and Modbus-TCP through a protocol adaptation judgment and distribution layer, non-NC-Link protocol data is decomposed into NC-Link data model objects and encapsulated in NC-Link DTI format. This eliminates dependence on foreign protocols, avoids risks and data security vulnerabilities, and ensures the autonomy of industrial data interaction. Furthermore, it achieves unified standardization of multi-source heterogeneous data, eliminating data conflicts caused by format differences and providing a consistent data foundation for subsequent modeling and applications. Simultaneously, the data distribution layer uses hierarchical encrypted storage based on data type and modeling importance. This ensures the storage security and retrieval efficiency of high-priority data (such as equipment operating parameters and abnormal alarm signals), optimizes storage resource allocation, avoids resource waste caused by redundant storage, and provides stable and secure data source support for digital twins.

[0119] Secondly, at the modeling level, this application significantly improves the accuracy and construction efficiency of the digital twin model through structured design and multi-dimensional integration. Firstly, it adopts a hierarchical decomposition strategy of "equipment-unit-workshop," breaking down the complex smart factory into precisely controllable sub-units, clarifying the hierarchical hierarchy and spatial relationships, avoiding overall modeling chaos. Simultaneously, based on CAD models and multi-body kinematic models, it restores the geometric attributes and motion structures of the equipment, associating them with the spatial location of environmental perception data. The constructed 3D visualized geometric model can highly match the characteristics of physical equipment, providing a high-fidelity geometric foundation for subsequent simulation and monitoring. Secondly, through a one-to-one mapping between virtual objects and physical equipment, and by extracting key events based on NC-Link DTI data, constructing event triggering logic and state transition rules, the resulting interactive model can realistically reflect the operating mechanism and dynamic interaction relationships of equipment in the workshop production process, overcoming the shortcomings of existing modeling which is limited to a single dimension. Furthermore, the association between virtual objects and physical equipment does not require manual configuration one by one. By constructing a mapping system through hierarchical location and functional attributes, it significantly reduces manual operation and configuration errors, improves modeling efficiency, and shortens the digital twin implementation cycle.

[0120] Thirdly, this application constructs a deep "physical-virtual" linkage mechanism through bidirectional mapping and dynamic synchronization, significantly enhancing the control and optimization capabilities of smart factories. The data interaction layer accesses NC-Link DTI standardized data in real time, establishing a bidirectional mapping relationship between the geometric model, interaction model, and data model based on the IDEF0 modeling method. When physical equipment data changes, it can drive the virtual object attribute update in real time, ensuring consistency between the virtual model and the physical factory state. This enables precise real-time monitoring of the workshop's operating status, facilitating timely detection of equipment anomalies or production bottlenecks by maintenance personnel. More importantly, this mechanism supports reverse optimization: simulation analysis results based on the interaction model can generate equipment control optimization commands and transmit them back to the physical equipment, enabling dynamic adjustment and optimization of the production process. For example, simulation can optimize the AGV's running path to improve material transfer efficiency, or simulation can adjust processing parameters based on equipment operating data to improve product qualification rate, forming a closed loop of "data acquisition - modeling simulation - optimization feedback." This helps smart factories achieve lean production and efficient control, improving overall production efficiency and market competitiveness.

[0121] Fourth, this application significantly reduces reliance on manual operation through automated data processing and modeling mechanisms, promoting the upgrading of industrial production towards automation and intelligence. In the data association stage, a mapping system is constructed through the hierarchical location and functional attributes of virtual objects, automatically matching multi-source heterogeneous data to corresponding virtual objects. This eliminates the need for manual configuration of sensor-model relationships, avoiding configuration errors caused by human experience bias, and saving significant manpower and time costs. In the operation monitoring and optimization stage, the digital twin model can synchronize the physical factory status in real time and automatically issue early warnings of anomalies, reducing the workload and risk of missed inspections during manual inspections. The reverse optimization mechanism enables automatic adjustment of the production process, reducing the frequency of manual intervention and improving the automation level of the production process. Overall, this application, through the deep application of digital twin technology, promotes the transformation of smart factories from traditional manual control to data-driven intelligent control, improving production efficiency and management level.

[0122] Please see Figure 4A smart factory digital twin model construction device based on NC-Link, provided for one of the purposes of this application, includes a data sensing module 1100, a data fusion module 1200, a geometric model construction module 1300, an interactive model construction module 1400, and a digital twin model construction module 1500. The data sensing module 1100 is configured to collect physical environment data corresponding to environmental sensing sensors, equipment operation data corresponding to production line equipment, and equipment status signals corresponding to auxiliary equipment in the sensing device layer. Based on the physical environment data, equipment operation data, and equipment status signals, it constructs multi-source heterogeneous data for the smart factory. The data fusion module 1200 is configured to associate the multi-source heterogeneous data with virtual objects corresponding to the environmental sensing sensors, production line equipment, and auxiliary equipment in the data sensing and fusion layer. This determines the workshop level, equipment location, and real-time attribute data corresponding to the environmental sensing sensors, production line equipment, and auxiliary equipment. Through a protocol adaptation judgment and distribution layer, it identifies the data transmission protocol corresponding to the multi-source heterogeneous data, uniformly encapsulates the multi-source heterogeneous data into NC-Link DTI format to determine standardized data in NC-Link DTI format, and the data distribution layer distributes the NC-Link... Standardized DTI format data is transmitted to the NC-Link agent and stored in encrypted form according to data type and importance. The geometric model construction module 1300 is configured to decompose the smart factory into multiple levels, including equipment, unit, and workshop levels, within the digital twin modeling layer. It describes the geometric attributes of production line equipment and auxiliary equipment based on a preset CAD model, defines the equipment motion structure based on a multi-body kinematics model, and associates the spatial location corresponding to the physical environment data collected by the environmental perception sensors to construct a three-dimensional visualized geometric model. The interaction model construction module 1400 is configured to use the standardized NC-Link DTI format data as input to construct the event triggering logic and state transition rules of the virtual object, forming an interaction model reflecting the workshop production process. The digital twin model construction module 1500 is configured to receive the standardized NC-Link DTI format data stored hierarchically in the data interaction layer in real time, and use the IDEF0 modeling method to establish a two-way mapping relationship between the spatial information of the geometric model, the operating logic of the interaction model, and the real-time data of the data model, so as to synchronously update the physical state of the equipment and the virtual object, thereby completing the construction of the NC-Link-based smart factory digital twin model.

[0123] Based on any embodiment of this application, please refer to Figure 5 Another embodiment of this application also provides an electronic device, which can be implemented by a computer device, such as... Figure 5The diagram shows the internal structure of a computer device. The computer device includes a processor, a computer-readable storage medium, a memory, and a network interface connected via a system bus. The computer-readable storage medium stores an operating system, a database, and computer-readable instructions. The database may store control information sequences. When the computer-readable instructions are executed by the processor, the processor can implement a method for constructing a smart factory digital twin model based on NC-Link. The processor of the computer device provides computing and control capabilities to support the operation of the entire computer device. The memory of the computer device may store computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor can execute the smart factory digital twin model construction method based on NC-Link of this application. The network interface of the computer device is used for communication with a terminal. Those skilled in the art will understand that… Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0124] In this embodiment, the processor is used to execute... Figure 4 The specific functions of each module are defined within the device, and the memory stores the program code and various data required to execute these modules or sub-modules. The network interface is used for data transmission between the user terminal and the server. In this embodiment, the memory stores the program code and data required to execute all modules in the NC-Link-based smart factory digital twin model construction device of this application. The server can call the server's program code and data to execute the functions of all modules.

[0125] This application also provides a storage medium storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to perform the steps of the NC-Link-based smart factory digital twin model construction method described in any embodiment of this application.

[0126] This application also provides a computer program product, including a computer program / instructions that, when executed by one or more processors, implement the steps of the method for constructing a smart factory digital twin model based on NC-Link as described in any embodiment of this application.

[0127] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. This computer program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0128] The above description is only a partial embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for constructing a digital twin model of a smart factory based on NC-Link, characterized in that, Includes the following steps: In the sensing device layer, physical environment data corresponding to environmental sensing sensors, equipment operation data corresponding to production line equipment, and equipment status signals corresponding to auxiliary equipment are collected. Based on the physical environment data, the equipment operation data, and the equipment status signals, multi-source heterogeneous data of the smart factory is constructed. In the data perception and fusion layer, the multi-source heterogeneous data is associated with the virtual objects corresponding to the environmental perception sensors, production line equipment, and auxiliary equipment to determine the workshop level, equipment location, and real-time attribute data corresponding to the environmental perception sensors, production line equipment, and auxiliary equipment. The protocol adaptation judgment and distribution layer identifies the data transmission protocol corresponding to the multi-source heterogeneous data, and encapsulates the multi-source heterogeneous data into NC-Link DTI format to determine the standardized data in NC-Link DTI format. The data distribution layer transmits the standardized data in NC-Link DTI format to the NC-Link agent, and stores it in encrypted form according to the data type and importance. In the digital twin modeling layer, the smart factory is decomposed into multiple levels including equipment level, unit level, and workshop level. Based on the preset CAD model, the geometric attributes of production line equipment and auxiliary equipment are described. Based on the multibody kinematics model, the motion structure of the equipment is defined, and the spatial position corresponding to the physical environment data collected by the environmental perception sensor is associated to construct a three-dimensional visualized geometric model. Using the standardized data in the NC-Link DTI format as input, the event triggering logic and state transition rules of the virtual object are constructed to form an interactive model that reflects the workshop production process; By real-time access to standardized data in NC-Link DTI format stored hierarchically through the data interaction layer, and by using the IDEF0 modeling method to establish a two-way mapping relationship between the spatial information of the geometric model, the operating logic of the interaction model, and the real-time data of the data model, the physical state of the equipment and the virtual object are updated synchronously to complete the construction of a digital twin model of a smart factory based on NC-Link.

2. The method for constructing a smart factory digital twin model based on NC-Link according to claim 1, characterized in that, The step of associating the multi-source heterogeneous data with virtual objects corresponding to the environmental sensing sensors, production line equipment, and auxiliary equipment to determine the workshop level, equipment location, and real-time attribute data corresponding to the environmental sensing sensors, production line equipment, and auxiliary equipment includes: In the virtual space, virtual objects are created corresponding to the environmental sensing sensors, production line equipment, and auxiliary equipment in the smart factory. The virtual objects include hierarchical location attributes and functional attributes. The hierarchical location attributes include the workshop number, production line number, equipment body number, and core component number of the equipment entity. The functional attributes include the equipment entity type, data acquisition range, and preset sampling frequency. A mapping system between physical devices and virtual objects is constructed. Based on the hierarchical location attributes and functional attributes, an association data structure between physical devices and virtual objects is constructed. The physical environment data, the device operation data, and the device status signals are respectively mapped to the relevant fields of their corresponding association data structures.

3. The method for constructing a smart factory digital twin model based on NC-Link according to claim 1, characterized in that, The process involves identifying the data transmission protocol corresponding to the multi-source heterogeneous data through a protocol adaptation judgment and distribution layer, uniformly encapsulating the multi-source heterogeneous data into NC-Link DTI format to determine standardized data in NC-Link DTI format, and then having the standardized data in NC-Link DTI format transmitted to the NC-Link agent by the data distribution layer, and then encrypting and storing the data according to its data type and importance. This includes the following steps: The protocol adaptation judgment layer receives multi-source heterogeneous data in the smart factory to identify the data transmission protocol corresponding to the multi-source heterogeneous data; The system detects whether the data transmission protocol is the NC-Link protocol. If not, it parses the multi-source heterogeneous data and decomposes it into root objects, data objects, component objects, device objects, internal method objects, and sampling channel objects in the NC-Link data model. After completing the data fusion and adaptation, the multi-source heterogeneous data is uniformly encapsulated into the NC-Link DTI format to determine the standardized data of the NC-Link DTI format. The data distribution layer transmits the standardized data in NC-Link DTI format to the NC-Link agent. The NC-Link agent performs hierarchical encryption processing on the standardized data according to the different data types of the standardized data in NC-Link DTI format and the importance level of industrial digital twin modeling, and stores the encrypted standardized data.

4. The method for constructing a smart factory digital twin model based on NC-Link according to claim 2, characterized in that, The steps of decomposing the smart factory into multiple levels, including equipment, unit, and workshop levels, describing the geometric attributes of production line equipment and auxiliary equipment based on a preset CAD model, defining the motion structure of the equipment based on a multibody kinematics model, and associating the spatial position corresponding to the physical environment data collected by the environmental perception sensors to construct a three-dimensional visualized geometric model include the following steps: The smart factory is decomposed into multiple levels, including equipment level, unit level, and workshop level, and the subordinate relationships and spatial associations between each level are determined. Obtain a preset CAD model, and describe the geometric attribute information of production line equipment and auxiliary equipment at each level in the smart factory based on the CAD model. The geometric attribute information includes the shape, size, color and assembly relationship of the equipment. A multibody kinematics model is established, and the motion structure information of each production line equipment and auxiliary equipment is defined based on the multibody kinematics model. The motion structure information includes at least the topological relationship, degrees of freedom and motion axis stroke between equipment components. The physical environment data collected by environmental sensing sensors in the smart factory is acquired, and the physical environment data is associated with the corresponding hierarchical spatial locations after the smart factory is decomposed, so as to determine the specific corresponding location of each physical environment data in the smart factory space. By integrating the geometric attribute information, the motion structure information, and the physical environment data of the associated spatial location, a three-dimensional visual geometric model of the smart factory is constructed.

5. The method for constructing a smart factory digital twin model based on NC-Link according to claim 1, characterized in that, The steps of constructing the event triggering logic and state transition rules of the virtual object, using the standardized data in the NC-Link DTI format as input, to form an interactive model reflecting the workshop production process, include: Based on the standardized data in the NC-Link DTI format, key event information associated with virtual objects is extracted. The virtual objects correspond to the equipment entities of environmental sensing sensors, production line equipment, and auxiliary equipment at the equipment level, unit level, and workshop level in the smart factory. The key event information includes equipment start-up and shutdown events, process switching events, material transfer events, and abnormal alarm events. Based on the extracted key event information, the event triggering logic of the virtual object is constructed, and the triggering conditions of each key event are determined. The triggering conditions are associated with the threshold of specific data items or data change rules in the NC-Link DTI format standardized data. Based on the event triggering logic of the virtual object and combined with the workshop production process, the state transition rules of the virtual object are defined, wherein the state transition rules include the state transition relationship of the virtual object under different event triggers; By integrating the event triggering logic and state transition rules of the virtual objects, an interactive model is formed that reflects the interaction relationships, operating mechanisms, and dynamic changes of equipment entities in the workshop production process.

6. The method for constructing a digital twin model of a smart factory based on NC-Link according to claim 1, characterized in that, The steps for synchronizing and updating the physical device state with virtual objects include the following: Real-time access to standardized data in NC-Link DTI format stored hierarchically through a data interaction layer; establishing a bidirectional mapping relationship between the spatial information of the geometric model, the operational logic of the interaction model, and the real-time data of the data model using the IDEF0 modeling method; and using this mapping to synchronize the physical device state with virtual objects. The data interaction layer obtains standardized data in NC-Link DTI format from the NC-Link agent; The IDEF0 modeling method is used to construct a bidirectional mapping relationship. The geometric model, interaction model and data model of the digital twin modeling layer are used as the core modeling objects. The spatial information of the geometric model and the operation logic of the interaction model are used as control elements. The NC-Link DTI format standardized data are used as input elements. The virtual and real state synchronization results are used as output elements. The real-time data transmission mechanism of the data interaction layer is used as mechanism elements. The IDEF0 model diagram from the top layer to the detail layer is drawn to establish a bidirectional mapping relationship between the spatial information of the geometric model, the operation logic of the interaction model and the real-time data of the data model. When the standardized data in NC-Link DTI format accessed by the data interaction layer changes dynamically, the corresponding attributes of the virtual object are updated in real time. If the physical environment data or equipment operation data changes, the spatial state parameters of the geometric model and the operation logic triggering conditions of the interaction model are updated synchronously through the mapping relationship. If the device status signal changes, the status identifier of the interaction model and the visualization status of the geometric model are updated synchronously through the bidirectional mapping relationship. At the same time, the device control optimization command is generated based on the simulation analysis results of the interaction model and transmitted back to the device entity through the data interaction layer so as to synchronize the status of the device entity with the virtual object.

7. The method for constructing a smart factory digital twin model based on NC-Link according to any one of claims 1 to 6, characterized in that, The physical environment data includes workshop vibration data, temperature data, and distance data; the equipment operation data includes speed data, displacement data, and load data; and the equipment status signals include normal signals, alarm signals, and shutdown signals. The production line equipment includes CNC machine tools, AGV carts, industrial robots, and material storage; the auxiliary equipment includes programmable logic controllers, CNC machine tools, and protocol adapters.

8. A device for constructing a digital twin model of a smart factory based on NC-Link, characterized in that, include: The data sensing module is configured to collect physical environment data corresponding to environmental sensing sensors, equipment operation data corresponding to production line equipment, and equipment status signals corresponding to auxiliary equipment in the sensing device layer, and construct multi-source heterogeneous data of the smart factory based on the physical environment data, the equipment operation data, and the equipment status signals. The data fusion module is configured to associate the multi-source heterogeneous data with the virtual objects corresponding to the environmental sensing sensors, production line equipment, and auxiliary equipment in the data perception and fusion layer, so as to determine the workshop level, equipment location, and real-time attribute data corresponding to the environmental sensing sensors, production line equipment, and auxiliary equipment. Through the protocol adaptation judgment and distribution layer, the module identifies the data transmission protocol corresponding to the multi-source heterogeneous data, and encapsulates the multi-source heterogeneous data into NC-Link DTI format to determine the standardized data in NC-Link DTI format. The data distribution layer transmits the standardized data in NC-Link DTI format to the NC-Link agent, and stores it in encrypted form according to the data type and importance. The geometric model construction module is configured to decompose the smart factory into multiple levels, including equipment level, unit level, and workshop level, in the digital twin modeling layer. It describes the geometric attributes of production line equipment and auxiliary equipment based on a preset CAD model, defines the motion structure of equipment based on a multibody kinematics model, and associates the spatial position corresponding to the physical environment data collected by the environmental perception sensor to construct a three-dimensional visualized geometric model. The interaction model construction module is configured to take the standardized data in the NC-Link DTI format as input, construct the event triggering logic and state transition rules of the virtual object, and form an interaction model that reflects the workshop production process. The digital twin model construction module is configured to receive standardized data in NC-Link DTI format through hierarchical storage of the data interaction layer in real time. It uses the IDEF0 modeling method to establish a two-way mapping relationship between the spatial information of the geometric model, the operation logic of the interaction model, and the real-time data of the data model, so as to synchronize the status of the physical equipment with the virtual object and complete the construction of the digital twin model of the smart factory based on NC-Link.

9. An electronic device comprising a central processing unit and a memory, characterized in that, The central processing unit is used to invoke and run a computer program stored in the memory to perform the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores, in the form of computer-readable instructions, a computer program implemented according to any one of claims 1 to 7, which, when invoked by a computer, executes the steps included in the corresponding method.

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