Digital twin processing method and system, and cloud platform

Abstract

The present invention relates to a digital twin processing method and system, and a cloud platform. The method comprises: acquiring production system elements, carrying out abstraction definition and parameterization description on the production system elements by means of digital-to-analog knowledge fusion of a mathematical model and a mechanistic model, so as to construct a digital twin ontology model; analyzing and reconstructing model data to obtain a mapping model of which object variables can be directly accessed and operated by a collective motion control method, so that the model is visualized at the cloud; and using an external data source to drive parameter update and operation matching of the model by means of a motion control method, so as to complete cooperative deployment and synchronous evolution of an actual physical device and the model in the production process on a cloud server. According to the present invention, a model is constructed by means of digital-to-analog knowledge fusion of a mathematical model and a mechanistic model, the model is mapped to achieve motion visualization, model parameter update and operation matching on the cloud are achieved, and then cooperative deployment and synchronous evolution of a physical device and the model are completed.

Description

Digital twin processing method, system and cloud platform Technical Field

[0001] The present invention relates to a digital twin processing method, system and cloud platform, and belongs to the technical field of digital twin processing. Background Art

[0002] In digital manufacturing, digital twin technology can simulate production conditions in real time, manage and monitor resources, and provide transparent guidance and reporting, driving digital transformation. Driven by both data and models, it enables simulation, real-time monitoring, predictive evaluation, model optimization and updating, and control. This can address issues such as unclear internal equipment mechanisms and insufficient data. While digital twin technology can collect real-time data and dynamically track the operating status of physical entities, and the accuracy of its digital twin models includes both explicit and implicit factors such as manufacturing element attributes and operational characteristics, achieving operational coordination between actual physical equipment and digital twin models during production still requires exploration.

[0003] Specifically, in the case of intelligent production lines, due to the variability of actual production conditions in production systems, if the existing serial virtual simulation model of "design - modeling - design modification - re-modeling - re-analysis" is applied, re-modeling and re-analysis will be required when on-site conditions change. This leads to problems such as large simulation modeling workload, high error rate, and difficulty in ensuring collaborative real-time performance during operation. Furthermore, the heterogeneity of the various production factors in the production system requires frequent iterative updates and redeployment of software and hardware when new production factors are connected to the system. The existing lengthy resource deployment process cannot achieve unified collection of interface data for each factor, and cannot respond to rapidly changing production needs in a timely manner. Furthermore, in traditional manufacturing systems, communication between heterogeneous production factors usually requires the development of specific interfaces for each new factor within the system. Therefore, how to seamlessly connect heterogeneous production factors is the key to achieving interoperability of production factors and data integration of digital twin services. Therefore, the digital twin technology in the existing technology faces problems such as deployment limitations, complex modeling, low accuracy, and poor real-time interaction. The interaction between physical entities and digital twin models is insufficient, the integration between data and mechanism rules is insufficient, and production collaboration is difficult to implement. It is necessary to explore methods to achieve operational collaboration between actual physical equipment and digital twin models in the production process.

[0004] At the same time, the current data visualization solutions for digital twin models in cloud scenarios are also limited. Their visual expression in cloud environments is relatively clumsy. Usually, after modeling is completed, the kinematic model and physical model need to be defined in dedicated software. In addition, the various types of data of the digital twin are independent of each other and the data volume is large, which does not support deployment and control in cloud scenarios. At the same time, the expression of the kinematic data of the digital twin model is also pre-set, and there is no way to achieve real-time synchronization with the actual equipment. It lacks scalability and precise digital control during runtime.

[0005] In summary, the application of digital-model knowledge fusion in the field of digital twins requires solving the problems of insufficient interaction between physical entities and models, and insufficient fusion between data and mechanism rules. It lacks the analysis of the above models in cloud scenarios and subsequent complex applications. How to provide the information analysis in the model to digital twin services for driving, updating and operation and maintenance collaboration will be the problem that existing technologies need to focus on solving. Summary of the Invention

[0006] The present invention provides a digital twin processing method, system and cloud platform, aiming to solve at least one of the technical problems existing in the prior art.

[0007] The technical solution of the present invention relates to a digital twin processing method, and the method according to the present invention comprises the following steps:

[0008] S100. Acquire production system elements and generate a mathematical model. By integrating the mathematical model with the mechanism model, abstractly define and parametrically describe the production system elements and construct a digital twin ontology model. The production system elements include the mechanical structure, electrical control, motion rules, dynamic system, and physical system of the actual physical equipment. The data content of the digital twin ontology model includes visual scenes, geometric structures, model effects, model materials, kinematic models, dynamic systems, kinematic scenes, physical systems, and physical scenes.

[0009] S200, parsing and reconstructing the data of the digital twin ontology model to obtain a mapping model of object variables that can be directly accessed and operated by the collective motion control method, thereby visualizing the model on the cloud;

[0010] S300, driven by external data sources, updates model parameters and performs operation matching through motion control methods, and then completes the collaborative deployment and synchronous evolution of actual physical equipment and models in the production process on the cloud server.

[0011] Furthermore, in step S100, the construction of the digital twin ontology model based on the coupling framework includes: using XML format to integrate digital model knowledge with various element data for hierarchical classification description, parameterized definition, and driving rule abstraction and expression, encapsulating each element data, and using labels and identifiers to instantiate and associate them to form a coupling framework.

[0012] Furthermore, in step S100, various types of data of the physical entity are abstractly defined as an information library, and parameterized descriptions are adopted for the data attributes in the information library to form a high-polygon low-polygon digital twin ontology model.

[0013] Furthermore, in step S100, the model, parameters, services, controls, workflows and security management of the actual physical device are digitally processed in an element-labeled manner and divided into various information libraries according to different granularity perspectives.

[0014] Furthermore, in step S100, the information library includes a visual scene information library, a geometric structure information library, a model effect information library, a model material information library, a kinematic model information library, a kinematic system information library, a kinematic scene information library, a physical scene information library and a physical model information library.

[0015] Furthermore, in step S100, the visual scene is used to instantiate information about geometry, nodes, light sources, controllers, cameras, and materials; the model material is used to instantiate model effect information; the dynamic system is used to instantiate the target kinematic model; the kinematic scene is used to instantiate the kinematic model and the dynamic system; and the physical scene is used to instantiate the physical system and the force environment.

[0016] Furthermore, in step S200, the parsing algorithm includes:

[0017] During the execution process, the XML source file of the ontology knowledge base is obtained; after obtaining the XML source file, the scenario, information base and ID identifier of the digital-analog fusion knowledge base are parsed respectively, wherein the parsed scenario includes parsing the trigger conditions of all elements of the information base contained in the digital-analog fusion knowledge base; the information of all element labels of the ontology knowledge base is obtained, and combined with all the ID identifiers obtained above, data parsing of various information bases is performed; the parsed data is used as the reconstructed data source, and the parsed data is reconstructed in the form of object variables to obtain the mapping model of the digital-analog fusion knowledge base.

[0018] Furthermore, in step S200, the visualization operation of the parsed digital twin ontology model includes:

[0019] S211. Pull the model's download address and basic information from the digital twin ontology model's warehouse to read the actual physical model information of the digital twin ontology model and determine whether it needs to be loaded and rendered in the cloud scene; define the model's initial pose in the scene, and update the model's pose in the scene;

[0020] S212: Encapsulate all variables under the kinematic model data and dynamic system data of the model into the kinematic variables of the model, and then register and bind the kinematic variables of the model with the data of the actual physical device of the model stored in the server;

[0021] S213. After binding, a long connection is established to enable the model to display the actual physical device's operating status in real time, or to reversely control the actual physical device through user data on the browser side.

[0022] Furthermore, the step S211 includes: implementing the parsing of the ontology model and the reconstruction process of the mapping model through the loading method and the parsing algorithm, preprocessing the mapping model after reconstruction, and traversing the subclass variables of all object variables of the reconstructed mapping model during the preprocessing process to determine whether the above subclass variables are grid data; wherein, if it is grid data, setting the renderer working flag to true, and preparing for the rendering work of the model; if it is non-grid data, returning an exception and stopping rendering.

[0023] Furthermore, in step S300, the data source for driving includes real-time data of the production system collected on-site, simulated motion data in the database and / or optimal motion trajectory data obtained through machine learning.

[0024] Furthermore, in step S300, the motion control method includes: passing the new motion data to all joint participating joint objects in the model kinematic variables to update the data of the joint variables in the mapping model, combining the interpolation animation method, calculating the current variables and interpolation animation parameters through posture transformation, and obtaining a new mapping model point vector set, and then executing the renderer rendering and GPU drawing to obtain the new expression of the model in the scene, and looping the execution process to enable the operation and maintenance coordination of the digital twin ontology model in the cloud environment.

[0025] The technical solution of the present invention also relates to a digital twin processing cloud platform, which is characterized by comprising: a digital twin ontology model based on a production system, and a digital twin processing method of the digital twin ontology model comprising:

[0026] S100. Acquire production system elements and generate a mathematical model. By integrating the mathematical model with the mechanism model, abstractly define and parametrically describe the production system elements and construct a digital twin ontology model. The production system elements include the mechanical structure, electrical control, motion rules, dynamic system, and physical system of the actual physical equipment. The data content of the digital twin ontology model includes visual scenes, geometric structures, model effects, model materials, kinematic models, dynamic systems, kinematic scenes, physical systems, and physical scenes.

[0027] S200, parsing and reconstructing the data of the digital twin ontology model to obtain a mapping model of object variables that can be directly accessed and operated by the collective motion control method, thereby visualizing the model on the cloud;

[0028] S300, driven by external data sources, updates model parameters and performs operation matching through motion control methods, and then completes the collaborative deployment and synchronous evolution of actual physical equipment and models in the production process on the cloud server.

[0029] Furthermore, the cloud platform interacts with the intelligent production edge through the production edge adapter.

[0030] Furthermore, the production edge adapter is used to connect the heterogeneous elements of the production system to the cloud platform through service encapsulation, and is used to interact with the cloud and perform adaptive negotiation based on the relationship between the elements.

[0031] Furthermore, the heterogeneous elements of the production system include robots, CNC equipment, sensors, RFID, UWB and HMI in the manufacturing unit.

[0032] Furthermore, the cloud platform includes a business server and a function server for interacting with the intelligent generation edge for data.

[0033] Furthermore, the business server adopts a business server of the SpringBoot framework, and the function server uses a web browser as a client.

[0034] Furthermore, the digital twin processing method adopts gRPC bidirectional streaming service, which includes: the business server adopts RPC, deploys gRPC server in IPEA, deploys gRPC client in the service background, and collects motion control real-time data between the business server and IPEA through gRPC protocol.

[0035] Furthermore, in the digital twin processing method, data collection of production system elements includes:

[0036] The OPCUA server is deployed through IPEA, and method nodes and variable nodes are established for each heterogeneous resource through protocol mapping. Adaptive negotiation is performed based on the relationship between resources. During system operation, IPEA will upload the process data extracted from the bottom layer to the database and gRPC client in real time; the middleware Consul server will upload the relevant data about each factor to the database; remote process control services represented by gRPC are deployed to convert the bottom-level controller data of each manufacturing production equipment into concrete data of the rotation or displacement of each equipment joint; the gRPC client obtains real-time control information of each production factor by sending gRPC service requests.

[0037] The technical solution of the present invention also relates to a digital twin processing system, characterized in that it includes: a computer device, wherein the computer device executes a digital twin processing method based on a production system, the method including:

[0038] S100. Obtain production system elements and generate mathematical models. Through the fusion of mathematical and model knowledge of the mathematical model and the mechanism model, abstractly define and parametrically describe the production system elements and construct a digital twin ontology model.

[0039] The production system elements include the mechanical structure, electrical control, motion rules, dynamic system and physical system of the actual physical equipment; the data content of the digital twin ontology model includes visual scene, geometric structure, model effect, model material, kinematic model, dynamic system, kinematic scene, physical system and physical scene;

[0040] S200, parsing and reconstructing the data of the digital twin ontology model to obtain a mapping model of object variables that can be directly accessed and operated by the collective motion control method, thereby visualizing the model on the cloud;

[0041] S300, driven by external data sources, updates model parameters and performs operation matching through motion control methods, and then completes the collaborative deployment and synchronous evolution of actual physical equipment and models in the production process on the cloud server.

[0042] The beneficial effects of the present invention are as follows:

[0043] The digital twin processing method, system and cloud platform of the present invention, and the operation coordination method of actual physical equipment and digital twin models in the production process, realize the digital twin synchronous evolution service based on edge-cloud collaboration, and the analysis of the digital twin ontology model with digital and analog knowledge fusion in the cloud scene.

[0044] The present invention realizes the parameter update of the digital twin ontology model and matches it with the production operation status by collecting real-time production data and parameter mapping; realizes the collection of real-time data by building a gRPC bidirectional streaming service, further drives the posture transformation of the digital twin ontology model of the production system, and executes the process cyclically as the data source is constantly changed and updated, thereby realizing the operation and maintenance collaboration of the ontology model in the cloud environment, solving the technical problems of poor accuracy and high delay rate in the synchronous evolution process of the existing technology.

[0045] The present invention adopts a cloud-native approach to build a digital twin synchronous evolution service and deploys it in the cloud to solve the limitations of local deployment. At the same time, the service is provided to digital users in a containerized manner, so that the digital twin service has sufficient flexibility, including flexibility in software operation and maintenance collaboration, flexibility in specific service functions, flexibility in user interaction, flexibility in data management, etc.

[0046] Based on the analysis of production factors and their correlations, the present invention adopts a computer structured language based on the fusion of data models and mechanism models to perform multi-dimensional and multi-level abstract definitions and parameterized descriptions of the entities of production factors, thereby improving the accuracy, agility and real-time performance of digital twin modeling. At the same time, the digital twin model of the present invention supports development and deployment in the cloud and accepts scheduling and analysis by cloud servers.

[0047] The present invention provides a method for importing, parsing, and reconstructing a digital twin ontology model, dividing different granularity perspectives into various information libraries, namely, parsing the start and end tags, descriptions, and enclosed information blocks, mapping and reconstructing them into binary grid data that can be rendered in the cloud scene and object variables that can be called by external applications, thereby realizing the reconstruction of the digital twin ontology model in the cloud scene, solving the problems of insufficient interaction between physical entities and models, and insufficient fusion between data and mechanism rules, and realizing a digital twin ontology model construction method based on the fusion of digital and analog knowledge.

[0048] This method maps time-varying operating condition information into effective model mechanism parameters. This mechanism parameter drives the update of the digital twin model, enabling online collaboration during the production process and improving real-time performance and accuracy during runtime. A matching relationship is established between on-site production drive data and twin model parameters, enabling automatic matching and rapid updates of the digital twin model based on on-site production data. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] FIG1 is a schematic diagram of a framework of a digital twin processing system according to an embodiment of the present invention.

[0050] FIG2 is a schematic diagram of the basic architecture of the digital twin technology according to an embodiment of the present invention.

[0051] FIG3 is a schematic diagram of the architecture of a digital twin processing cloud platform according to an embodiment of the present invention.

[0052] FIG4 is a schematic diagram of a digital twin ontology model information library for digital-analog knowledge fusion according to an embodiment of the present invention.

[0053] FIG5 is a schematic diagram of a digital twin ontology model coupling framework according to an embodiment of the present invention.

[0054] FIG6 is a logic diagram of the parsing algorithm of the digital twin ontology model according to an embodiment of the present invention.

[0055] FIG7 is a schematic diagram of the digital twin ontology model visualization process according to an embodiment of the present invention.

[0056] FIG8 is a schematic diagram of the IPEA structure of the digital twin ontology model according to an embodiment of the present invention.

[0057] FIG9 is a schematic structural diagram of IPEA and IPE in a dual-arm robot according to an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The following will provide a clear and complete description of the concept, specific structure and technical effects of the present invention in conjunction with the embodiments and drawings to fully understand the purpose, scheme and effects of the present invention.

[0059] It should be noted that, unless otherwise specified, when a feature is referred to as being "fixed" or "connected" to another feature, it may be directly fixed or connected to the other feature, or it may be indirectly fixed or connected to the other feature. The singular forms of "," "said" and "the" used herein are also intended to include the plural forms, unless the context clearly indicates otherwise. In addition, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art. The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the invention. The term "and / or" used herein includes any combination of one or more of the related listed items.

[0060] 1 to 9 , in some embodiments, the digital twin processing method according to the present invention includes at least the following steps:

[0061] S100. Acquire production system elements and generate a mathematical model. By integrating the mathematical model with the mechanism model, abstractly define and parametrically describe the production system elements and construct a digital twin ontology model. The production system elements include the mechanical structure, electrical control, motion rules, dynamic system, and physical system of the actual physical equipment. The data content of the digital twin ontology model includes visual scenes, geometric structures, model effects, model materials, kinematic models, dynamic systems, kinematic scenes, physical systems, and physical scenes.

[0062] S200, parsing and reconstructing the data of the digital twin ontology model to obtain a mapping model of object variables that can be directly accessed and operated by the collective motion control method, thereby visualizing the model on the cloud;

[0063] S300, driven by external data sources, updates model parameters and performs operation matching through motion control methods, and then completes the collaborative deployment and synchronous evolution of actual physical equipment and models in the production process on the cloud server.

[0064] The digital twin ontology model based on the production system constructed by the present invention is a digital mapping system that uses digital technology or computer structured language to express data information in a virtual environment that is completely consistent with the actual object. The high-fidelity digital twin ontology model constructed by it can generate data close to the performance of the real system, realize the real-time fusion and interaction between the digital twin model and the physical entity, and co-evolution throughout the entire production life cycle.

[0065] Referring to Figures 1 and 2 , the core components of the digital twin system of the present invention include the physical entity of the production system, the digital twin ontology model, the digital twin service software platform, data, and the interaction between its entity, model, and service. Specifically, the present invention uses a unified adapter (IPEA) to connect the communication interfaces and data sources of different elements as a buffer between the actual physical entity (IPE) and the service (software platform), solving the data interaction and control of model matching and updating based on real-time data, intelligent production decision-making, and synchronous evolution of the production process during operation.

[0066] Referring to Figure 1, the physical entities of the production system form an intelligent production edge (IPE). For the entire IPE, there are many manufacturing elements and the relationships are complex. Heterogeneous elements from different manufacturers cannot interact with the cloud for information, and provide unified data services for digital twin services. The present invention builds an intelligent production edge adapter (IPEA) in each manufacturing unit, and connects heterogeneous elements to the digital twin cloud service platform software through service encapsulation through IPEA. On the other hand, the interaction with the cloud is adaptively negotiated according to the relationship between the elements. During the execution of the workflow, IPEA will upload the process data extracted from the bottom layer of the physical entity to the digital twin cloud native service software platform in real time, providing basic real-time process data.

[0067] Furthermore, referring to FIG3 , the present invention provides a digital twin cloud-native service software platform that adopts a software-as-a-service architecture and is based on real-time data interaction-driven digital twin model parameter updates and operation matching, visual operation of virtual-real linkage of production systems, collaborative deployment of production processes, and synchronous evolution and iteration. This software platform can realize digital twin service modeling, visualization, production process collaboration, and synchronous evolution and iteration in the cloud.

[0068] As shown in Figures 1 and 3, the cloud-native digital twin service software platform adopts a software-as-a-service architecture, a SpringBoot-based business server, and a web-based functional server software architecture and service modules. At the same time, it introduces a container and model management module (module warehouse) to manage and store digital twin ontology models, and realize unified management and update iteration of digital twin data services, visualization and real-time monitoring, twin models and software services.

[0069] Specific implementation of step S100

[0070] The present invention is based on the data abstraction definition and parameterized description of the production system elements, as well as the fusion of digital and analog knowledge based on mathematical models and mechanism models to construct a high-poly and low-coupling digital twin ontology model. It should be noted that the mathematical model is a three-dimensional grid model of the actual physical production and manufacturing system, which is a grid data mapped from point, vertex, and triangle relationship data based on the physical entity. The mechanism model integrates the multi-dimensional (geometry-kinematic model-motion rules), multi-domain (mechanical-electrical-hydraulic-control), multi-physical properties (mass-material-inertia), and multi-disciplinary (material mechanics-kinematics-dynamics, etc.) of the physical entity. It is necessary to deeply understand the essential characteristics of the object and describe the microscopic mechanism of the physical entity. The fusion of digital and analog knowledge is to combine the mathematical model with the mechanism-based model, and use the advantages of data distribution to abstractly define and parameterize the description of various types of physical entity data, that is, to abstractly define various types of physical entity data as an information library, and adopt parameterized description for the data attributes in the information library to form a high-poly and low-coupling digital twin ontology model.

[0071] Specifically, the present invention digitally abstracts and parametrically describes all elements of a production system, standardizes and precisely defines each element and their relationships, abstractly defines each element of a physical entity into an information library, and parametrically describes the attributes of the elements in the information library to form a highly cohesive and low-coupling digital twin ontology model. The physical elements of the production system include mechanical structure, electrical control, motion rules, dynamics, and physical systems.

[0072] It is understandable that for production systems, random disturbances occur frequently in the production process and actual production conditions are changeable. Therefore, it is necessary to map the working condition information that changes frequently over time into effective model mechanism parameters, and drive the update of the digital twin model through the mechanism parameters to achieve online collaboration of the production process and improve the real-time performance and accuracy during operation. The present invention establishes a matching relationship between on-site production drive data and twin model parameters to achieve automatic matching and rapid updating of digital twin models based on on-site production data.

[0073] For example, referring to Figure 1, the present invention uses a computer structured language to integrate mathematical models and knowledge of mechanism models including motion rules, mechanical structures, physical properties, dynamics, etc. to abstract typical element knowledge such as production process elements, manufacturing resource elements, logistics elements, physical structure elements, motion control systems, etc., and uses abstract definitions to standardize and accurately define each element and its relationship, parametric descriptions to encapsulate each element in modular data, and abstract and express driving rules to construct a digital twin ontology model of all elements such as typical visual scenes, kinematic models, kinematic scenes, dynamic systems, combined structures, model effects, model materials, physical systems, etc. of the production system. Among them, after the development platform is built, it can be packaged and uploaded to the model warehouse for storage through a container, and any client of the digital twin service software platform can pull and download from the model warehouse through the model address.

[0074] This invention dynamically and accurately models physical entities at multiple scales, dimensions, domains, and physical quantities. It uses a computer structured language to integrate mathematical models with mechanism model knowledge, abstracting the various elements of the mechanism model, including the mechanical structure, electrical control, motion rules, dynamic systems, and physical systems of the physical entity. It then standardizes and precisely defines these elements and their relationships using ontological and parametric methods. It can analyze the types and characteristics of real-time data from production systems and define and standardize the production site's equipment data, process data, logistics data, warehousing data, production data, beat data, equipment failure data, and real-time decision-making data.

[0075] In some embodiments, the present invention digitally processes the models, parameters, services, controls, workflows, and security management of actual physical devices using element-based tagging, and divides these into various information libraries based on different granularity perspectives. These libraries include a visual scene library, a geometric structure library, a model effect library, a model material library, a kinematic model library, a kinematic system library, a kinematic scene library, a physical scene library, and a physical model library.

[0076] In some embodiments, the data content of the digital twin ontology model of the present invention consists of data-independent and mutually coupled visual scenes, geometric structures, model effects, model materials, kinematic models, dynamic systems, kinematic scenes, physical systems and physical scenes, etc.

[0077] In some embodiments, the digital twin ontology model is an information component in the manufacturing system, and the use of computer structured language is a prerequisite for building it in accordance with the cloud native principle. The digital twin ontology model is an information-based hardware facility running on a digital digital twin processing cloud platform, while considering the migration and interoperability of different infrastructure platforms such as cloud, fog, and edge nodes. The digital twin ontology model of the present invention adopts a Collada format file based on Extensible Markup Language (XML), and provides a standard language for describing the content, structure and semantics of a file, document or data set through XML. The model, parameters, services, control, workflow and security management of the actual physical device are digitally processed in an element-tagged manner, wherein the element tag includes an information block surrounded by a start and end tag, a description, and can be divided into various information libraries according to different granularity perspectives. Referring to Figure 4, the digital twin ontology model of digital-analog knowledge fusion includes a visual scene information library, a geometric structure information library, a model effect information library, a model material information library, a kinematic model information library, a kinematic system information library, a kinematic scene information library, a physical scene information library, and a physical model information library.

[0078] Furthermore, referring to Figures 1 and 4, the present invention maps the data attributes contained in various information libraries through element definition tags, and describes the parent-child relationship of information library data attributes, the reference relationship between each information library data, and the combined hierarchical relationship with other digital twins through identifiers and parent-child relationships. In this way, when retrieving, addressing, developing, updating, and orchestrating and deploying data for digital twins, a specific information library data range can be accurately located to achieve precise digital control.

[0079] Specifically, the present invention uses XML format to integrate digital model knowledge with each element data for hierarchical classification description, parameterized definition, and driving rule abstraction and expression, encapsulates each element data, and adopts labels and identifiers to instantiate and associate them to form a coupling framework, that is, a digital twin ontology model. Furthermore, in the coupling framework of the present invention, the data is divided layer by layer through parent-child elements to form a hierarchical relationship of each information library. And, each information library follows the topological relationship of configuration element-function element-variable element. And, through<Instance*> The element tag instantiates an object, which is bound through URL addressing and scope identifier addressing.

[0080] The digital twin ontology model of the present invention integrates mathematical models and mechanism model knowledge through element tags, thereby storing various types of data information and cutting the information library so that data information can be freely changed without losing information. It can be combined into a tool chain with rich functions through multiple configuration files. This is no longer the delivery format of traditional mathematical models in some specific software platforms, but a model driven by the content creation of users of creative tools and interactive applications.

[0081] For example, referring to Figure 5, the data content of the cloud-native digital twin ontology model consists of visual scenes, geometric structures, model effects, model materials, kinematic models, dynamic systems, kinematic scenes, physical systems and physical scenes. The data are independent of each other and are coupled to each other through addressing, referencing and instantiation. The visual scene can instantiate geometric bodies, nodes, light sources, controllers, cameras, materials and other information, the model material can instantiate model effect information, the dynamic system can instantiate the target kinematic model, the kinematic scene can instantiate the kinematic model and the dynamic system, and the physical scene can instantiate the physical system and the force environment. At the same time, the coupling framework of the present invention includes the following features: the data is divided layer by layer through parent-child elements to form a hierarchical relationship of each information library; each information library follows the topological relationship of configuration element-function element-variable element; through<Instance*> The element tag instantiates the object, which is bound by URL addressing and scope identifier addressing. The instantiated object can be a functional element of the internal information library or a <technique>Elements reference external configuration files, which greatly increases the user's secondary development performance and improves the adaptive matching mechanism of the digital twin ontology model.

[0082] Specific implementation of step S200

[0083] The present invention discloses a method for parsing a digital twin ontology model based on the fusion of digital and analog knowledge in a cloud scenario. It mainly solves the problem of generating a high-fidelity digital twin model by depicting the multi-dimensional, multi-domain, and multi-physical attribute data of physical entities through data abstraction definition and parametric description based on the fusion of mathematical models and mechanism models of production system elements. The present invention can solve the problems of parsing, reconstructing, and visualizing the digital twin model based on the fusion of digital and analog knowledge in a cloud environment.

[0084] It should be noted that, considering that digital twin models described through abstract definitions and parameterization cannot be provided as object variables to the model's motion control methods for model driving, the data mapping process performed during the parsing process results in a mapping model that is a parameter set of object variables. This parameter set is called a digital twin mapping model. The model described through abstract definitions and parameterization is no longer a three-dimensional model of the physical device, but rather a semantically defined ontological knowledge base, referred to as a digital twin ontology model. Together, the two constitute a complete digital twin model. The resulting digital twin mapping model can be presented in a cloud environment, enabling visualization of the digital twin model in the cloud. Specifically, the digital twin ontology model visualized in a web environment is the mapping model obtained after the ontology model has been parsed and reconstructed using the parsing algorithm.

[0085] In some embodiments, the present invention first parses the digital-analog fusion knowledge of the ontology model, traverses all data during the parsing process to complete the mapping of the ontology model, and reconstructs the mapped ontology model into a variable object that is easy for the program to read, write, modify, and update. The mapped ontology model is then provided to an API callable by external applications to access the variable object of the mapped model for operation. During the traversal process, the grid model child node data is first scanned and read, laying the foundation for the rendering and visualization of the grid model in the cloud scene. Next, the library name and the node name under the corresponding library are parsed, and then the information library resource parsing function is called to obtain the mechanism data information of each parent and child node.

[0086] In some embodiments, the present invention provides a parsing method ColladaParse() for the digital twin ontology model, which includes four major categories: a parsing class parse(), a reconstruction class buildLibrary(), an external interface class getBuild(), and a kinematics system building class setupKinematics(). For example, see the parsing algorithm of the digital twin ontology model in Figure 6. During its execution, parse() obtains the XML source file of the ontology knowledge base through URL addressing. After obtaining the XML source file, the three functions parseScene(), parseId(), and parseLibrary() are executed, which respectively represent the parsing of the scene, information library and id identifier of the digital-analog fusion knowledge base. The has_scene of the parseScene function represents the trigger condition for parsing all elements of the information library contained in the digital-analog fusion knowledge base. After executing the getElementsByTagName method, the information of all element tags of the ontology knowledge base and all ids obtained by the parseId() function are obtained, which are used by the parseLibrary function to perform data parsing of various information libraries. The parsed data is used as the reconstructed data source. The reconstruction class buildLibrary reconstructs the data obtained by the parseLibrary function in the form of object variables. The reconstruction results in the mapping model of the digital-analog fusion knowledge base, which is stored in the form of variables for easy reading, calling and modification. The getBuild class provides an API for external applications to read and write mapping models. External applications can access the mapping model of the digital-analog fusion knowledge base through id or name and return its parameter values. The function of the setupKinematics class is to establish the kinematic system in the ontology knowledge base in the form of object variables, and to reconstruct the variables of the reconstructed kinematic model, kinematic scene, and visual scene using the connect method. The jointIndex and visualElement represent the joint data and visual elements of the reconstructed mapping model.

[0087] It can be understood that the goal of the connect method is to associate the kinematic object variables of the mapping model into a complete kinematic system expression of the device, which is a digital mapping of the kinematic system of the actual device, making it easier for external applications to control it.

[0088] In some embodiments, the present invention packages the parsed and reconstructed digital twin mapping model into a container and uploads it to a cloud-based model repository for storage. This supports dynamic model updates, and any client of the digital twin service software platform can download the model from the model repository using the model address. Furthermore, referring to Figure 1 , the model repository of the present invention is a cloud-native OSS that is containerized and can accept container orchestration.

[0089] Specifically, the digital twin ontology model is deployed in the server's model warehouse, which can be pulled and downloaded by all clouds and supports visualization in the web environment. In the process of controlling or visualizing the ontology model, the digital-analog fusion knowledge of the ontology model must first be parsed. During the parsing process, on the one hand, all data is traversed to complete the mapping of the ontology model. On the other hand, the mapped ontology model is reconstructed into a variable object that is easier for the program to read, write, modify, and update. It is directly controlled and used by developers. This does not change the semantic interpretation of the ontology model, and provides an API that can be called by external applications to access the variable object of the mapping model for operation. During the traversal process, the grid model sub-node data is first scanned and read, laying the foundation for the rendering and visualization of the grid model in the cloud scene; secondly, the library name and the node name under the corresponding library are parsed, and then the information library resource parsing function is called to obtain the mechanism data information of each parent and child node.

[0090] In some embodiments, the present invention performs visualization operations on the parsed digital twin ontology model. Specifically, the visualization operation includes the following steps: pulling the download address and basic information of the model from the warehouse of the digital twin ontology model to read the actual physical model information of the digital twin ontology model and determine whether it needs to be loaded and rendered in the cloud scene; then, defining the initial posture of the model in the scene and updating the posture of the model in the scene; encapsulating all variables under the kinematic model data and dynamic system data of the model into the kinematic variables of the model, and then registering and binding the kinematic variables of the model with the data of the actual physical device stored in the server; after binding, establishing a long connection through WebSocket to realize the real-time display of the operating status of the actual physical device by the model, or reversely controlling the actual physical device through the user data on the browser side.

[0091] For example, referring to the visualization process of the digital twin ontology model in FIG7 , the visualization service of the digital twin ontology model of the present invention is based on the Three.js framework. First, the download address and basic information of the digital twin ontology model are pulled from the digital twin ontology model warehouse MinIO. The server is accessed through the jquery ajax method to read the actual physical model information of the digital twin ontology model. It is determined whether it needs to be loaded and rendered in the cloud scene. The parsing of the ontology model and the reconstruction process of the mapping model are realized through the loading method loader and the parsing algorithm ColladaParse. After reconstruction, the mapping model is preprocessed. The preprocessing process traverses the subclass variable chid of all object variables of the reconstructed mapping model to determine whether chid is grid data. If it is grid data, the renderer work flag is set to true and the rendering work of the model is prepared. If it is non-grid data, an exception is returned and rendering stops. Then, the initial pose of the model in the scene is defined, and the pose of the model is updated in the scene. Next, all variables associated with the model's kinematic model data and dynamics system data are encapsulated into the model's kinematic variables, called kinematics. These variables are then registered and bound to the server's data for the model's actual physical device. After binding, a persistent connection is established via WebSocket, enabling the model to display the actual device's operating status in real time, or to reversely control the device through user data on the browser side. Reverse control involves modifying key information in the server model and indirectly driving the device controller through remote process control.

[0092] It should be noted that the purpose of registration is to allow the server to read the key information of the model and generate a corresponding Consul file. The information in the configuration file includes the model's name, device type, GUID, service communication protocol provided by IPEA, IP address (all registered actual physical devices are bound to the model's kinematic variables through IP address, port, and service type), etc., and the Consul agent is started, and the DNS provided by the LAN is used to discover Consul to achieve connection.

[0093] Specific implementation of step S300

[0094] The present invention provides a motion control method for a digital twin ontology model driven by a data source, which realizes the update iteration and synchronous evolution of the model. Specifically, the production operation status of the digital twin ontology model is automatically matched and the parameters are updated, including the following steps: first, real-time on-site production process data is collected, the production process data is parsed and packaged, and the operation of the digital twin ontology model of the production system is driven by the parsed and packaged data. Then, a parameter mapping strategy for the digital twin ontology model of the production system is formulated, and a control method for updating the twin model parameters is generated in the form of traversal and judgment by extracting and constructing basic function templates. A matching mechanism for the production system model parameters is established based on a dynamic data exchange method, and the automatic update of the parameters of the digital twin ontology model of the production system is completed by managing the version of the digital twin ontology model of the production system, the associated model files and the parameter files.

[0095] The mapping model obtained through analytical reconstruction is specifically represented by object variables that can be directly accessed and manipulated by the motion control method. By modifying and updating these object variables through the motion control method, real-time data interaction-driven production process collaboration and twin model iterative updates are achieved. The motion control method for deploying the ontology model in a web environment is posture transformation control, and the control and posture transformation of the mapping model are driven by external data sources.

[0096] It's understandable that when a mapping model is built in a web environment, it has a local coordinate system, in which the vertices of its skeletons are located. The coordinate system of the scene is called the world coordinate system. To place the mapping model in the scene, the vertices of the graphics need to be transformed from the local coordinate system to the world coordinate system. This process is called the model transformation. The transformation from the world coordinate system to the view coordinate system is called the view transformation. In the direction of observation, there is a visible space, and the graphics in this space are projected onto the canvas. This process is called the projection transformation. The vertices of the skeletons of a base model undergo the model transformation, view transformation, and projection transformation before their position on the canvas is determined. This entire process is called the model's pose transformation. When the pose transformation is controlled and updated by an external application on the mapping model's kinematic data, its motion posture and position in the scene change accordingly.

[0097] Furthermore, the pose transformation of the mapping model can be driven by an external data source, provided by the motion control service in the software backend. The driving data source can be real-time data collected on-site, simulated motion data in a database, or optimal motion trajectory data obtained through machine learning. Specifically, the hierarchical classification description and parameterized data of the digital twin ontology model are interpreted and reconstructed to obtain the digital twin mapping model. The object variables (mapping model) are modified and updated through motion control methods (i.e., pose transformation control), realizing production process collaboration driven by real-time data interaction and iterative updates of the twin model.

[0098] The motion control method (i.e., posture transformation control) of the present invention transmits new motion data to all joint (participating) joint objects in the model kinematic variables kinematics to update the data of the joint variables in the mapping model. Combined with the interpolation animation method, the current variables and interpolation animation parameters are calculated through posture transformation to obtain a new mapping model point vector set, and then the renderer rendering and GPU drawing are executed to obtain the new expression of the model in the scene. As the data source is constantly changed and updated, the execution process is cyclically executed, thereby realizing the operation and maintenance coordination of the ontology model in the cloud environment, and realizing the update iteration and synchronous evolution of the model.

[0099] It should be noted that the collaborative operation and maintenance of the ontology model in the cloud environment refers to the collaborative process of ontology model maintenance, mapping model deployment, rendering, control, and pose transformation in the cloud environment through external applications or backend developers. This enables the matching of on-site production drive data transmitted from the backend with the twin model parameters and rapid updates of motion posture. The control and pose transformation of the mapping model are updated through external data sources, and then the new position of the model on the canvas is obtained through rendering by the renderer and GPU drawing.

[0100] In some embodiments, the dynamic updating of the production system digital twin ontology model includes dynamically updating the production system digital twin ontology model through the synchronous mapping of virtual and real production factors and the association of real-time data with production factors. This synchronous mapping of virtual and real production factors and the association of real-time data with production factors enable dynamic updating of the production process twin model, enabling simulation analysis of production system capacity balance, product capacity, equipment utilization, and other factors, and achieving real-time visualization of the production process and equipment status based on virtual-real linkage.

[0101] Referring to Figures 1 and 3 , the digital twin processing cloud platform of the present invention adopts a cloud-native approach to build a digital twin service software platform, deploying it in the cloud to address the limitations of local deployment while providing containerized services to digital users. Specifically, the digital twin service software platform of the present invention includes a digital twin functional server (i.e., the browser side) and a business server (i.e., the server side).

[0102] In some embodiments, the server primarily interacts with the intelligent generation edge through relevant data and remote process control services. The server utilizes the lightweight SpringBoot framework, resolving the redundancy of configuration files and conflicts between project dependencies during development. The browser primarily provides functional services such as digital twin model visualization and motion control, providing the technical foundation for synchronized evolution and predictive services with actual production and manufacturing systems. The browser uses a web browser as the client, leveraging real-time internet communication technology to enable user-client interaction and provide real-time digital twin services to digital users. This eliminates the complex processes and installation constraints of traditional local deployments, and instead utilizes containers for unified version control and continuous integration, enabling one-time deployment and real-time sharing.

[0103] In some embodiments, in the construction of the digital twin business service software platform of the present invention, the digital twin ontology model of the production system is first dynamically updated, then the data of the heterogeneous elements of the production system are collected, and finally the gRPC two-way streaming service is constructed to meet the real-time demand of a large number of data interaction services of the physical production system, realize two-way equal dialogue between the browser and the server, and realize real-time communication of data. It should be noted that the gRPC protocol is a two-way streaming, long-connected RPC (Remote Procedure Call).

[0104] In some embodiments, referring to FIG3 , constructing a gRPC bidirectional streaming service includes: the business server of the digital twin business service software platform adopts RPC, the gRPC server is deployed in IPEA, the gRPC client is deployed in the service background, and the real-time motion control data of the business server and IPEA are collected through the gRPC protocol. For example, the client request end and the service response end are respectively deployed in the software background and the IPEA edge node, and the server and the client realize bidirectional streaming communication through the port IP. The present invention adopts the gRPC bidirectional streaming service, which makes the collection efficiency of the motion process data higher, meets the real-time requirements of the motion process data, and realizes the synchronous evolution of the digital twin service software and the actual physical equipment of the production system.

[0105] In some embodiments, referring to Figures 1 and 3, in order to realize the collaborative production process driven by real-time data interaction, it is first necessary to collect data from various elements of the production system. The present invention deploys the OPCUA server through IPEA, establishes method nodes and variable nodes for each heterogeneous resource through protocol mapping, and performs adaptive negotiation based on the relationship between resources. During the operation of the system, IPEA uploads the process data extracted from the bottom layer to the database and gRPC client in real time; the Consul server of the middleware uploads the relevant data about each element to the database; deploys remote process control services represented by gRPC, and converts the bottom-level controller data of each manufacturing production equipment into concrete data of the rotation or displacement of the joints of each device; and obtains real-time control information of each production element by sending gRPC service requests on the gRPC client.

[0106] It should be noted that the data and control interfaces of various heterogeneous elements in the manufacturing cell, such as robots, CNC equipment, sensors, RFID, UWB, and HMI, are not uniform, and the data structures obtained through separate collection are also not uniform. This makes it impossible for heterogeneous resources to exchange information with the cloud. IPEA deploys the OPCUA server, and through protocol mapping, establishes method nodes and variable nodes for each heterogeneous resource. Although the communication interfaces and data formats of heterogeneous resources vary, after OPCUA server mapping, service orchestration and management can be unified, and adaptive negotiation can be performed based on the relationships between resources. During system operation, IPEA uploads process data extracted from the underlying layer to the database and gRPC client in real time, providing the software platform with basic process data, thereby supporting the acquisition of key information such as the operating status, operating time, downtime, and failure time of each production system element required for software platform scheduling. The middleware Consul server uploads relevant data about each element to the database for data exchange with the software platform's backend server. It also registers the production element, providing a foundation for subsequent equipment status and safety testing. On the other hand, remote process control services represented by gRPC are deployed to convert the underlying controller data of each manufacturing production equipment, such as motor speed and pulse signals, into concrete data of the rotation or displacement of each equipment joint. The gRPC client in the background of the digital twin service software platform can obtain real-time control information of each production factor by sending gRPC service requests, which is used as the data source in the motion control method.

[0107] This is illustrated by a specific embodiment. Each heterogeneous production factor in the IPE is equipped with an IPEA, which uniformly encapsulates and processes the data sources and operation logic of multi-source heterogeneous production factors. During the IPE operation phase, by parsing the data source attributes of each factor, it dynamically switches to the corresponding underlying communication protocol, and maps the data volume to the OPCUA address space in real time. A data access request is initiated to the OPCUA server through the gRPC server, thereby sending the encapsulated data source of the production factor to the gRPC client. Referring to Figures 8 and 9, for the dual-arm robot work unit, the IPEA is located in the IPE. The seven-axis dual-arm robot has its own GPIO and TCP / IP communication interfaces. The robot teach pendant is connected to the IPEA via the RS-485 interface, and the USB Micro-B is used to power the IPEA. The IPEA is connected to the server via the RJ45 Gigabit Ethernet interface. The server can obtain the real-time production data source of the dual-arm robot through the IPEA, and can also initiate simple control requests to the production factors through the gRPC service.

[0108] Specifically, as shown in Figures 8 and 9, IPEA can collect operating signals from robot sensors, photoelectric sensors, RFID tags, and other sensors during production system operation, as well as data from the robot's control cabinet's underlying controller and user interaction data from the teach pendant. These signals serve as a data source for the robot's operational decisions. Furthermore, IPEA can directly send control commands to unit sensors, cylinders, and the robot's end effector, directly influencing the entire operational process. In addition to data collection and control, IPEA processes and encapsulates production status information, product execution process information, and pulse signals from each joint of the robot equipment. These data, including status and process information, are then uploaded to the database via the Consul server and the gRPC server. The robot pulse signals are converted into actual joint angles during operation and transmitted to the server-side gRPC client, serving as a data source for the browser-side model motion control method. In summary, IPEA plays a key role in the data exchange between IPE and the software platform, integrating hardware and software, and providing data infrastructure services for digital twins.

[0109] It should be appreciated that the method steps in the embodiments of the present invention can be implemented or executed by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer-readable memory. The method can use standard programming techniques. Each program can be implemented in a high-level procedural or object-oriented programming language to communicate with the computer system. However, if desired, the program can be implemented in assembly or machine language. In any case, the language can be a compiled or interpreted language. In addition, for this purpose, the program can be run on a programmed application-specific integrated circuit.

[0110] Furthermore, the operations of the processes described herein may be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by the context. The processes described herein (or variations and / or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) that is executed collectively on one or more processors, by hardware, or a combination thereof. The computer program includes a plurality of instructions that can be executed by one or more processors.

[0111] Further, the method can be implemented in any type of computing platform that is operably connected to a suitable computer, including but not limited to a personal computer, a minicomputer, a mainframe, a workstation, a network or distributed computing environment, a separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, etc. Various aspects of the present invention can be implemented as machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, an optical read and / or write storage medium, an RSM, a ROM, etc., so that it can be read by a programmable computer, and when the storage medium or device is read by the computer, it can be used to configure and operate the computer to perform the process described herein. In addition, the machine-readable code, or portions thereof, can be transmitted over a wired or wireless network. When such media includes instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor, the invention described herein includes these and other different types of non-transitory computer-readable storage media. When programmed according to the methods and techniques of the present invention, the present invention can also include the computer itself.

[0112] The computer program can be applied to input data to perform the functions described herein, thereby converting the input data to generate output data that is stored in a non-volatile memory. The output information can also be applied to one or more output devices such as a display. In a preferred embodiment of the present invention, the converted data represents a physical and tangible object, including a specific visual depiction of the physical and tangible object produced on the display.

[0113] The above description is merely a preferred embodiment of the present invention. The present invention is not limited to the aforementioned embodiments. As long as the technical effects of the present invention are achieved by the same means, any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention. Within the scope of protection of the present invention, various modifications and variations of the technical solutions and / or implementation methods are possible.< / technique>

Claims

1. Digital twin processing method, characterized in that: The method comprises the following steps: S100. Construct a digital twin ontology model based on the fusion of the mathematical model and mechanism model knowledge of the production system; S200, parsing the digital twin ontology model to perform visualization operations; S300, automatically matching and updating parameters of the production and operation status of the digital twin ontology model; S400. Build a digital twin business server, and use the digital twin business server to update, iterate, and synchronously evolve the digital twin ontology model.

2. The method according to claim 1, characterized in that In the step S100, Digitally abstract and parametrically describe all elements of the production system twin model, standardize and accurately define each element and its relationship, abstractly define each element of the physical entity as an information library, adopt parametric description for the element attributes in the information library, and build a high-polymerization and low-coupling digital twin ontology model.

3. The method according to claim 1, characterized in that In the step S100, The elements of a physical entity include mechanical structure, electrical control, motion rules, dynamic system and physical system.

4. The method according to claim 1, wherein In the step S100, the model, parameters, services, controls, workflows and security management of the actual physical device are digitally processed in an element-labeled manner and divided into various information libraries according to different granularity perspectives.

5. The method according to claim 4, characterized in that In the step S100, The information library includes a visual scene information library, a geometric structure information library, a model effect information library, a model material information library, a kinematic model information library, a kinematic system information library, a kinematic scene information library, a physical scene information library and a physical model information library.

6. The method according to claim 1, characterized in that In the step S100, The data content of the digital twin ontology model consists of data-independent and mutually coupled visual scenes, geometric structures, model effects, model materials, kinematic models, dynamic systems, kinematic scenes, physical systems and physical scenes.

7. The method according to claim 6, characterized in that In step S100, XML format is used to integrate digital model knowledge with various element data for hierarchical classification description, parameterized definition, and driving rule abstraction and expression. Each element data is encapsulated, and instantiation association is performed using tags and identifiers to form a coupling framework for the digital twin ontology model.

8. The method according to claim 7, characterized in that In the coupling framework: The data is divided layer by layer through parent-child elements to form a hierarchical relationship of each information base; each information base follows the topological relationship of configuration element-function element-variable element;<Instance*> The element tag instantiates an object, which is bound through URL addressing and scope identifier addressing.

9. The method according to claim 1, characterized in that In the step S200, The model parsing operation includes the following steps: parsing the digital-analog fusion knowledge of the ontology model, traversing all data during the parsing process to complete the mapping of the ontology model, and reconstructing the mapped ontology model into a variable object that is easy for the program to read, write, modify, and update, and providing an API that can be called by external applications to access the variable object of the mapping model for operation.

10. The method according to claim 9, characterized in that In the step S200, During the traversal process, the mesh model child node data is first scanned and read to lay the foundation for the rendering and visualization of the mesh model in the cloud scene. Secondly, the library name and the node name under the corresponding library are parsed, and then the information library resource parsing function is called to obtain the mechanism data information of each parent and child node.

11. The method according to claim 9, characterized in that In the step S200, The visualization operation includes the following steps: pulling the download address and basic information of the digital twin ontology model from the digital twin ontology model warehouse MinIO, accessing the server through the jquery ajax method, reading the actual physical model information of the digital twin ontology model, and judging whether it needs to be loaded and rendered in the cloud scene. The ontology model is parsed and the mapping model is reconstructed through the loading method loader and the parsing algorithm ColladaParse. After reconstruction, the mapping model is preprocessed. The preprocessing process traverses the subclass variable chid of all object variables of the reconstructed mapping model to judge whether chid is grid data. If it is grid Data, set the renderer working flag to true, and prepare for the rendering of the model. If it is non-grid data, an exception is returned and rendering is stopped; define the initial pose of the model in the scene, and update the pose of the model in the scene; encapsulate all variables under the model's kinematic model data and dynamic system data into the model's kinematic variables kinematics, and then register and bind the model's kinematic variables kinematics with the data of the actual physical device of the model stored on the server. After binding, establish a long connection through WebSocket, or reversely control the actual physical device through user data on the browser side.

12. The method according to claim 11, characterized in that In the step S200, The reverse control comprises the steps of: modifying key information of the server model and indirectly driving the device controller by means of remote process control.

13. The method according to claim 1, wherein The step S300 includes: S310: Collect production process data, parse and encapsulate the production process data, and drive the operation of the production system digital twin ontology model through the parsed and encapsulated data; S320. Formulate a parameter mapping strategy for the digital twin ontology model of the production system, extract and construct basic function templates, and generate a control method for updating the twin model parameters in the form of traversal and judgment; S330. Establish a matching mechanism for production system model parameters based on dynamic data exchange, and complete the update of production system digital twin ontology model parameters by managing the version, associated model files and parameter files of the production system digital twin ontology model.

14. The method according to claim 1, wherein In step S400, setting up the server includes the following steps: S411. Dynamically update the digital twin ontology model of the production system; S412, collect data on various heterogeneous elements of the production system; S413. Build a gRPC bidirectional streaming service.

15. The method according to claim 13, characterized in that The step S411 includes: The digital twin ontology model of the production system is dynamically updated through the synchronous mapping of virtual and real production factors and the association of real-time data with production factors.

16. The method according to claim 14, characterized in that The step S412 includes: The OPCUA server is deployed through IPEA, and method nodes and variable nodes are established for each heterogeneous resource through protocol mapping. Adaptive negotiation is performed based on the relationship between resources. During system operation, IPEA will upload the process data extracted from the bottom layer to the database and gRPC client in real time; the middleware Consul server will upload the relevant data about each factor to the database; remote process control services represented by gRPC are deployed to convert the bottom-level controller data of each manufacturing production equipment into concrete data of the rotation or displacement of each equipment joint; the gRPC client obtains real-time control information of each production factor by sending gRPC service requests.

17. The method according to claim 14, characterized in that The step S413 includes: The digital twin business server uses RPC, deploys the gRPC server in IPEA, and deploys the gRPC client in the service background. The real-time motion control data of the business server and IPEA is collected through the gRPC protocol.

18. The method according to claim 14, characterized in that In step S400, the model update iteration and synchronous evolution include the following steps: driving the posture transformation of the digital twin ontology model of the production system through real-time data in the server, passing the new motion data to all joint objects in the model kinematic variables kinematics to update the data of the joint variables in the mapping model, combining the interpolation animation method, calculating the current variables and interpolation animation parameters through posture transformation to obtain a new mapping model point vector set, and then executing the renderer rendering and GPU drawing to obtain the new expression of the model in the scene. As the data source continues to change and update, the execution process is cyclical, thereby realizing the operation and maintenance collaboration of the ontology model in the cloud environment.

19. Digital twin processing system, characterized in that include: A computer device, wherein the computer device executes a digital twin processing method based on a production system, the method comprising: S100: Construct a digital twin ontology model by integrating the mathematical model of the production system with the knowledge of the mechanism model; S200, parsing the digital twin ontology model to perform visualization operations; S300, automatically matching and updating parameters of the production and operation status of the digital twin ontology model; S400. Build a digital twin business server, and use the digital twin business server to update, iterate, and synchronously evolve the digital twin ontology model.

20. Digital twin processing cloud platform, characterized by: The system comprises a digital twin functional server, a digital twin business server and a computer device. The computer device forms a digital twin ontology model based on the fusion of the mathematical model and mechanism model knowledge of the production system. The digital twin processing method executed by the digital twin ontology model includes: S100. Construct a digital twin ontology model based on the fusion of the mathematical model and mechanism model knowledge of the production system; S200, parsing the digital twin ontology model to perform visualization operations; S300, automatically matching and updating parameters of the production and operation status of the digital twin ontology model; S400: Maintain the digital twin business server, and update, iterate, and synchronously evolve the digital twin ontology model through the digital twin business server.

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