WO2023246347A1 - 数字孪生处理方法及数字孪生系统 - Google Patents

数字孪生处理方法及数字孪生系统 Download PDF

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Publication number
WO2023246347A1
WO2023246347A1 PCT/CN2023/093048 CN2023093048W WO2023246347A1 WO 2023246347 A1 WO2023246347 A1 WO 2023246347A1 CN 2023093048 W CN2023093048 W CN 2023093048W WO 2023246347 A1 WO2023246347 A1 WO 2023246347A1
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Prior art keywords
digital twin
data
management platform
equipment
iot
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PCT/CN2023/093048
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English (en)
French (fr)
Inventor
谢海琴
陈录城
陈辉
闵成波
王勇
刘焕焕
Original Assignee
海尔数字科技（上海）有限公司
卡奥斯工业智能研究院(青岛)有限公司
卡奥斯物联科技股份有限公司
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Publication of WO2023246347A1 publication Critical patent/WO2023246347A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/25—Integrating or interfacing systems involving database management systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y10/00—Economic sectors
    • G16Y10/25—Manufacturing
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y20/00—Information sensed or collected by the things
    • G16Y20/20—Information sensed or collected by the things relating to the thing itself
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y20/00—Information sensed or collected by the things
    • G16Y20/30—Information sensed or collected by the things relating to resources, e.g. consumed power
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y40/00—IoT characterised by the purpose of the information processing
    • G16Y40/10—Detection; Monitoring
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y40/00—IoT characterised by the purpose of the information processing
    • G16Y40/20—Analytics; Diagnosis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y40/00—IoT characterised by the purpose of the information processing
    • G16Y40/30—Control
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

  • This application belongs to the field of digital twin technology, and specifically relates to a digital twin processing method and a digital twin system.
  • Digital twin refers to the digital model of a physical product in virtual space, which contains product information throughout the entire life cycle from product conception to product delisting. This "twin” is not only similar to its twin brother in the real space (including product specifications, geometric models, material properties, simulation data and other information), but it can also reflect the operating status of the product through data fed back by sensors installed on the product. Will "perform" exactly like the real product.
  • the industrial digital twin system is based on the industrial Internet and is mainly used for product R&D, design and production manufacturing, and cannot be extended to the entire life cycle of industrial products.
  • this application provides a digital twin processing method and a digital twin system.
  • this application provides a digital twin processing method, which is applied to a digital twin management platform.
  • the method includes:
  • converting the device data into action data corresponding to the mechanism model includes:
  • the simulation data is converted into action data corresponding to the mechanism model of the target device.
  • the digital twin management platform is communicatively connected with the industrial mechanism platform;
  • the acquisition of the mechanism model corresponding to the target device includes:
  • generating a digital twin corresponding to the target device based on the mechanism model and the action data includes:
  • the action data is fused with each model parameter in the mechanism model to generate a digital twin corresponding to the target device.
  • the method further includes:
  • the business message is converted into message display data corresponding to the digital twin for display.
  • the digital twin management platform includes: a data computing component; the device data is stored in an IoT database by the IoT management platform; the business data is stored by the IoT management platform in business database;
  • the device data Before converting the device data into corresponding simulation data, it also includes:
  • the step of converting the service data into corresponding service messages includes:
  • the digital twin management platform is communicatively connected with the Anden subsystem
  • the method also includes:
  • the abnormality early warning table sent by the Anden subsystem; the abnormality early warning table is generated when the Anden subsystem detects an abnormality in the industrial equipment in each production line;
  • this application provides a digital twin processing device located on a digital twin management platform.
  • the device includes:
  • the acquisition module is used to obtain the equipment data of the target equipment in each stage of the industrial equipment life cycle in the IoT management platform and obtain the corresponding mechanism model of the target equipment;
  • a conversion module used to convert the device data into action data corresponding to the mechanism model
  • a generation module configured to generate a digital twin corresponding to the target device based on the mechanism model and the action data
  • a display module is used to display the digital twin.
  • the conversion module is specifically used to:
  • the digital twin management platform is communicatively connected with the industrial mechanism platform;
  • the acquisition module is specifically used to:
  • the generation module is specifically used to:
  • the action data is fused with each model parameter in the mechanism model to generate a digital twin corresponding to the target device.
  • the device further includes:
  • a business processing module used to obtain business data corresponding to the target device collected by the IoT management platform; convert the business data into corresponding business messages; convert the business messages into messages corresponding to the digital twin Display data for presentation.
  • the digital twin management platform includes: a data computing component; the device data is stored in an IoT database by the IoT management platform; the business data is stored by the IoT management platform in business database;
  • the acquisition module is also used to:
  • the business processing module converts the business data into corresponding business messages, it is specifically used to:
  • the business data is read from the business database; and the business data is statistically processed according to preset business requirement data to generate the business message.
  • the digital twin management platform is communicatively connected with the Anden subsystem
  • the device also includes:
  • the abnormality display module is used to obtain the production line abnormality early warning table sent by the Anden subsystem; the abnormality early warning table is generated when the Anden subsystem detects an abnormality in the industrial equipment in each production line; the product The line abnormality early warning table is displayed accordingly.
  • this application provides a digital twin system, including: an IoT management platform that communicates with each other and the digital twin management platform as described in any one of the first aspects.
  • the IoT management platform includes an edge management sub-platform and an IoT sub-platform that are communicatively connected to each other; the edge management sub-platform is communicatively connected to the production information management system;
  • the edge management sub-platform is used to collect equipment data of target equipment in each production line corresponding to each stage of the industrial equipment life cycle and equipment data of corresponding target equipment in the production information management system;
  • the IoT sub-platform is used to send the device data of the target device and the mechanism model corresponding to the target device to the digital twin management platform.
  • it also includes: an Andeng subsystem; the Andeng subsystem is communicatively connected to the digital twin management platform;
  • the Andeng subsystem includes: an abnormality triggering terminal and a processing server;
  • the exception triggering terminal is used to generate corresponding exception information according to the exception instructions issued by the user, and send the exception information to the processing server;
  • the processing server is configured to generate a corresponding production line abnormality early warning table according to the abnormality information, and send the production line abnormality early warning table to the digital twin management platform.
  • this application provides a digital twin management platform, including:
  • the memory stores computer execution instructions
  • the processor executes the computer execution instructions stored in the memory to implement the digital twin processing method provided in the first aspect or any possible implementation manner in the first aspect.
  • the present application provides a computer-readable storage medium in which computer-executable instructions are stored, and when executed by a processor, the computer-executable instructions are used to implement the first aspect or any of the first aspects.
  • a possible implementation method provides a digital twin processing method.
  • this application provides a chip including:
  • the memory stores a computer program
  • the processor executes the computer program stored in the memory, the digital twin processing method provided in the first aspect or any possible implementation manner in the first aspect is implemented.
  • the present application provides a computer program product, including a computer program that, when executed by a processor, implements the digital twin processing method provided by the above-mentioned first aspect or any possible implementation of the first aspect. .
  • a computer program including: program code.
  • the program code performs digital twin processing as provided in the first aspect or any possible implementation manner of the first aspect. method.
  • the digital twin processing method includes: obtaining the equipment data of the target equipment in each stage of the industrial equipment life cycle in the IoT management platform and obtaining the corresponding mechanism model of the target equipment; The data is converted into action data corresponding to the mechanism model; a digital twin corresponding to the target device is generated based on the mechanism model and the action data; and the digital twin is displayed.
  • the digital twin processing method of this application can generate the corresponding data twin based on the mechanism model and equipment data by obtaining the equipment data of the target equipment in each stage of the industrial equipment life cycle in the IoT management platform and obtaining the mechanism model corresponding to the target equipment.
  • the digital twin can be displayed to provide a basis for subsequent optimization of each stage in the equipment life cycle. At the same time, since it covers all stages of the life cycle, the life cycle coverage of digital twins for industrial products is improved.
  • Figure 1 is an example diagram of an application scenario provided by the embodiment of this application.
  • FIG. 2 is a schematic flow chart of the digital twin processing method provided by this application.
  • FIG. 3 is a schematic structural diagram of the digital twin system provided by this application.
  • FIG. 4 is a schematic diagram of the overall data flow of the digital twin provided by this application.
  • Figure 5 is a schematic diagram of network communication of the Andon subsystem provided by this application.
  • FIG. 6 is a schematic structural diagram of the digital twin processing device provided by this application.
  • Figure 7 is a schematic structural diagram of the digital twin management platform provided by this application.
  • the words “if” or “if” as used herein may be interpreted as “when” or “when” or “in response to determination” or “in response to detection.”
  • the phrase “if determined” or “if (stated condition or event) is detected” may be interpreted as “when determined” or “in response to determining” or “when (stated condition or event) is detected )” or “in response to detecting (a stated condition or event)”.
  • digital twins in the industrial field focus on building digital twins for smart factories, smart workshops and smart equipment to ensure production.
  • industrial digital twin system relying on the perception and feedback control pathways of the virtual-real interaction module, precise mapping, interactive fusion and intelligent feedback control of physical entities and virtual entities are achieved based on the real data of industrial production activities and the feedback control instructions of intelligent applications.
  • the functions of the physical space and digital space can be continuously improved and iteratively upgraded during production operations.
  • the current digital twin technology solution is based on the industrial Internet and is mainly used for product R&D, design and production. It is difficult to run through the entire life cycle of industrial equipment and cannot connect the various stages of industrial equipment such as design, manufacturing, debugging, operation, and maintenance. The problem. Therefore, the life cycle coverage of digital twins for industrial products needs to be improved.
  • the inventor found in the research that in order to solve this problem, all equipment data of the life cycle of industrial equipment can be obtained, and at the same time combined with the corresponding mechanism Models are used to generate corresponding digital twins, thereby improving the life cycle coverage of industrial products.
  • the digital twin processing method includes: obtaining the equipment data of the target equipment in each stage of the industrial equipment life cycle in the IoT management platform and obtaining the corresponding mechanism model of the target equipment. Convert device data into action data corresponding to the mechanism model. Generate a digital twin corresponding to the target device based on the mechanism model and action data. Demonstrate the digital twin.
  • the digital twin processing method of this application can generate the corresponding data twin based on the mechanism model and equipment data by obtaining the equipment data of the target equipment in each stage of the industrial equipment life cycle in the IoT management platform and obtaining the mechanism model corresponding to the target equipment.
  • Digital twins can be displayed to provide a basis for subsequent optimization at each stage of the equipment life cycle.
  • the life cycle coverage of digital twins for industrial products is improved.
  • FIG. 1 is an example diagram of an application scenario provided by the embodiment of this application.
  • the application scenario includes a digital twin management platform 10, an IoT management platform 30 and an industrial equipment production line 50.
  • the industrial equipment production line 50 includes multiple production lines, such as production line a, production line b, production line c to production line n, etc. in the figure.
  • the digital twin management platform 10 may include a twin display module, a twin management module, a twin data processing module, etc.
  • the IoT management platform 30 may include an edge management module, an IoT module, etc.
  • the IoT management platform 30 can collect equipment data of the industrial equipment production line, thereby obtaining equipment data at each stage of the industrial equipment life cycle.
  • the IoT management platform 30 can also obtain device data by connecting to other management platforms, such as through a production information management platform.
  • the mechanism model may be stored in the IoT management platform 30, or the mechanism model may be obtained from other platforms, such as from an industrial mechanism platform.
  • the mechanism model matches each industrial equipment. The higher the matching degree of the mechanism model, the better the effect of the corresponding generated twin numbers.
  • the IoT management platform 30 collects equipment data of each production line in the industrial equipment production line 50 in real time. For example, it can include equipment data of production lines in various stages of the life cycle such as R&D, manufacturing, and maintenance. , and then the IoT management platform 30 sends the collected device data and the corresponding mechanism model to the digital twin management platform 10.
  • the digital twin management platform 10 converts device data into action data corresponding to the mechanism model, and generates a digital twin corresponding to the target device based on the mechanism model and action data. At the same time, the digital twin management platform 10 displays digital twins.
  • the IoT management platform 30 will continue to collect equipment data at each stage of the industrial equipment life cycle. After receiving the subsequent equipment data, the digital twin management platform 10 will generate The digital twin is updated and iterated so that the digital twin can more effectively reflect the life process development status of the corresponding industrial equipment. The displayed digital twin can provide a basis for subsequent optimization of the industrial equipment production line structure.
  • FIG. 2 is a schematic flow chart of the digital twin processing method provided by this application.
  • the execution subject of the embodiment of the present application is a digital twin processing device, and the digital twin processing device can be integrated in the digital twin management platform.
  • a digital twin management platform can include multiple electronic devices. The method includes:
  • the life cycle includes stages such as research and development, manufacturing, maintenance, update and optimization, covering different levels of the enterprise's production and manufacturing process from the equipment layer and production line layer to the workshop layer and factory layer, and runs through the design, process management and optimization of production and manufacturing. , resource allocation, parameter adjustment, quality management and traceability, energy efficiency management, production scheduling and other aspects.
  • the device data of the target device can include static data and dynamic data.
  • the static data includes device appearance, device structure, etc.
  • the dynamic data can include device operation data, device energy consumption, etc.
  • the IoT management platform can be built based on the business as a service BaaS layer and the IoT layer based on the industrial Internet.
  • the platform architecture of the IoT management platform can be developed through the BaaS layer developer platform. Specifically, it can be built based on the springboot framework.
  • each platform in this embodiment, including the digital twin management platform can be built based on the BaaS layer engine, thereby organically combining technical solutions with business logic and best practices.
  • the BaaS engine emphasizes business logic and best practices and is used to quickly build a cloud service platform for digital twin applications.
  • BaaS capabilities include:
  • the "Mechanism Model Platform” mainly provides enterprise equipment models and equipment mechanism management, including thousands of mechanism model libraries.
  • Knowledge Graph Service provides equipment failure and maintenance knowledge content management.
  • the "Identification Analysis Platform” records the spare parts traceability information of equipment and products in a one-object-one-code manner, effectively solving the management problem of enterprise spare parts.
  • the “Industrial Big Data Platform” provides real-time access to massive data.
  • the "artificial intelligence platform” enhances enterprise intelligence capabilities through data and algorithm models.
  • the IoT management platform can obtain equipment data of target equipment in each stage of the industrial equipment life cycle by collecting equipment data corresponding to the production line at each stage of the industrial equipment life cycle. It can also collect some equipment data through the connected production information management system.
  • the mechanism model can be obtained from the industrial mechanism platform at the BaaS layer, or it can be first obtained from the industrial mechanism platform by the IoT management platform, and then obtained from the IoT management platform.
  • the device data is static parameter data, it needs to be converted into action data corresponding to the mechanism model in order to simulate the target device.
  • the equipment data is a rotational speed of 300 rpm, which needs to be converted into corresponding rotation action data.
  • a digital twin corresponding to the target device can be constructed and generated.
  • the display of digital twins can be a full-scenario display, which facilitates users to view the digital twin of each device and can better see the changes in the device life cycle.
  • comprehensive status monitoring of the equipment can be achieved, and at the same time, it can also provide a better reference basis for subsequent iterations of the digital twin and equipment updates.
  • the corresponding data twin can be generated based on the mechanism model and equipment data.
  • Display the digital twin to provide a basis for optimization at each stage of the subsequent equipment life cycle.
  • the life cycle coverage of digital twins for industrial products is improved.
  • the digital twin processing method provided in this embodiment includes the following steps.
  • step 201 is similar to the implementation of step 101 in the previous embodiment of this application, and will not be described again here.
  • Each device is preset with a corresponding device identification, which can be stored and managed in the identification resolution platform of the BaaS layer.
  • the industrial mechanism platform stores mechanism models corresponding to each industrial equipment, and the industrial mechanism models can be configured.
  • device data can be converted into corresponding simulation data first, providing a basis for subsequent conversion into action data.
  • Device data can be converted into simulation data using commonly used simulation data generators.
  • the simulation data can be converted through the action data conversion engine, thereby converting the simulation data into action data corresponding to the mechanism model of the target device.
  • Fusing the action data with the mechanism model can transform the mechanism model from a static state to a dynamic state.
  • the mechanism model can be made to simulate the operation of the target device, thereby generating a digital twin corresponding to the target device.
  • the service data of the target device can also be combined for analysis and display. details as follows:
  • the IoT management platform can collect business data corresponding to the target device, such as from the big data platform at the BaaS layer.
  • Business data may include the price, sale time, etc. of the target device.
  • the digital twin can simultaneously display the corresponding message display data, thereby providing a basis for users to subsequently optimize the business-related stages of industrial equipment.
  • the digital twin management platform includes: data computing component.
  • Device data is stored in the IoT database by the IoT management platform.
  • Business data is stored in the business database by the IoT management platform.
  • the IoT database and business database can use Oracle database (a relational database), SQL (English full name: Structured Query Language server database, structured query language) Server database, MySQL database (a relational database), TDengine high Performance time series database, etc.
  • the interaction efficiency between the digital twin management platform and the database can be improved through the WebSocket communication protocol.
  • a WebSocket data service can be built based on the WebSocket communication protocol to build an overall process from obtaining device data to converting device data.
  • Preset business requirements can be set in advance according to actual application scenarios, such as transportation optimization, production line process optimization, etc. In this way, the business data can be counted and analyzed according to the preset business requirements, and corresponding business messages can be generated.
  • step 206 is similar to the implementation of step 104 in the previous embodiment of this application, and will not be described again here.
  • the digital twin management platform communicates with the Anden subsystem, and opens up all aspects of manual exception handling informatization through the Anden subsystem, further improving the efficiency of exception handling, and optimizing all aspects of the industrial equipment life cycle. Provides the basis for the entire process of stages.
  • Methods also include:
  • the abnormality early warning table is generated when the ANDEN subsystem detects an abnormality in the industrial equipment in each production line.
  • the production line abnormality early warning table can be generated based on the abnormal information triggered by the abnormal trigger terminal in the Anden subsystem.
  • the Anden subsystem includes multiple abnormality triggering terminals, which can be triggered by users for abnormal problems. When users find abnormal problems on the production line, they can trigger abnormal problems through the abnormality triggering terminals, thereby causing the Anden subsystem to generate Production line abnormality early warning table.
  • the digital twin management platform When the digital twin management platform receives the production line abnormality early warning table, it can be combined with the digital twin for unified display. For example, if the abnormality warning table is related to a certain industrial equipment, it can be displayed simultaneously with the digital twin corresponding to the certain industrial equipment. At the same time, the Andeng subsystem can also upload the abnormality early warning table to the client that manages abnormal problems to notify managers to handle the abnormality. If the abnormal problem can be solved without manual work, the Anden subsystem can also determine the abnormal solution strategy from the preset database based on the abnormal information, so as to solve the corresponding abnormal problem according to the abnormal solution strategy.
  • FIG. 3 is a schematic structural diagram of the digital twin system provided by this application. As shown in Figure 3, in this embodiment, a digital twin system is also provided.
  • the digital twin system 100 includes:
  • the IoT management platform 30 and the digital twin management platform 10 in any of the above embodiments are communicatively connected to each other.
  • the digital twin management platform 10 can use the springboot framework based on JAVA (software language) coding, and can interact with each digital twin rendering device to synchronize device layout, device data configuration, device combination and other information.
  • JAVA software language
  • the IoT management platform 30 is used for edge management, collection of production line data, business data collection, and management of IoT data.
  • the IoT management platform 30 includes an edge management sub-platform and an IoT sub-platform that are communicatively connected to each other.
  • the edge management sub-platform communicates with the production information management system.
  • the edge management sub-platform is used to collect equipment data of target equipment in each production line corresponding to each stage of the industrial equipment life cycle and equipment data of corresponding target equipment in the production information management system.
  • the IoT sub-platform is used to send the device data of the target device and the corresponding mechanism model of the target device to the digital twin management platform.
  • the digital twin system 100 also includes: Andon subsystem.
  • the Anden subsystem is communicated with the digital twin management platform 10.
  • Andeng subsystem includes: exception trigger terminal and processing server.
  • the exception triggering terminal is used to generate corresponding exception information according to the exception instructions issued by the user, and send the exception information to the processing server.
  • the processing server is used to generate a corresponding production line abnormality early warning table based on the abnormal information, and send the production line abnormality early warning table to the digital twin management platform.
  • the digital twin system includes a digital twin management platform, an IoT management platform, a business database (referred to as a business library in the figure), an IoT database (referred to as an IoT library cluster in the figure), and the Anden subsystem (only The backend of the Anden system is shown, but the entire structure is not shown).
  • the IoT management platform is responsible for edge management, production line data collection, business data collection, and IoT data management.
  • the digital twin management platform is responsible for digital twin processing and parameter configuration synchronization for full-scenario digital twin content display to achieve real-time display of the full scene.
  • the Andeng subsystem is responsible for alarming and handling abnormal production lines.
  • the digital twin management platform includes a full-scenario digital twin module, an action service module, a data service module, and a data computing component.
  • the IoT management platform includes edge management sub-platforms including edge management and edge agents in the figure, where edge agents refer to edge physical devices. It also includes the Internet of Things sub-platform, the IoT platform in the picture, and the anti-control agent module.
  • the production information management system is the MES in the picture (full name in English: Manufacturing Execution System).
  • the business database uses Oracle database, SQL Server database, and MySQL database.
  • the IoT database can use zookeeper, a distributed application coordination service software, to perform distributed management of the database, support distributed locks, and cluster member election.
  • the IoT management platform can directly collect data from production line equipment and connect to the MES system to obtain equipment data in the MES system.
  • the data is synchronized to the IoT sub-platform using the mqtt (English full name: Message Queuing Telemetry Transport) protocol, and the IoT sub-platform stores the device data in the IoT database.
  • mqtt English full name: Message Queuing Telemetry Transport
  • the data in the IoT database passes through the websocket protocol and the action service module in the digital twin management platform.
  • the device data is converted into simulation data through the simulation data generator, and the simulation data is converted into corresponding action data through the real-time action conversion engine.
  • the business data in this embodiment is stored in the business database.
  • the business data in the business database is extracted through the data calculation component, and statistical calculation processing is performed to generate report data.
  • the report data can be written into the IoT database as shown in the figure, and then converted into digital twin report display content through data services and restful (English full name: Representational State Transfer) services. Report data can also be directly converted into digital twin report display content by data services and restful services.
  • the Anden system when the Anden system detects problems in the production line in the background, it can send the production line abnormality early warning table (referred to as the report in the figure) to the data service of the digital twin management platform, and convert the report data through the data service and restful service. Display content for digital twin reports.
  • the production line abnormality early warning table referred to as the report in the figure
  • FIG. 5 is a schematic diagram of network communication of the Andon subsystem provided by this application. As shown in Figure 5, in order to better understand the Andon subsystem of this embodiment, a detailed description will be given below with reference to the accompanying drawings.
  • the small program in the electronic device is connected through the interface standard and data interface of the Anden server.
  • the "report/response" of the small program will be forwarded to the nginx server.
  • the nginx server Through the nginx server reverse proxy, the nginx server requires https (full name: Hyper Text Transfer Protocol over SecureSocket Layer (Chinese: Hypertext Transfer Security Protocol) protocol establishes a network connection with the applet of the electronic device and performs current limiting processing.
  • the nginx server will forward the content of the mini program to the server, and the server communicates and connects with the front end, the Internet of Things sub-platform (i.e., the IoT platform in the figure), the message queue, and the database respectively. They are all intranet operations. Note: All interfaces accessing the server require authentication permissions and support current limiting to ensure service stability.
  • the IoT platform can receive abnormal triggers from IoT devices and issue automatic alarms.
  • the front-end refers to the front-end electronic equipment.
  • a mobile terminal can be used to convert the triggered abnormal information into a production line abnormality early warning table and display the report on a large screen.
  • the production line abnormality early warning table can also be sent to the digital twin management platform.
  • the IoT platform, front-end and server use http (English full name: Hyper Text Transfer Protocol, Chinese: Hyper Text Transfer Protocol) protocol for communication connections.
  • the server can manage and process exception alarms, exception messages, and data stored in the database.
  • the Andon subsystem is a system for notifying management, maintenance, and other workers of quality or process issues.
  • the Andeng subsystem can be mainly divided into two parts functionally:
  • Abnormal triggering terminal adopts mobile terminal and supports multi-terminal deployment such as IOS and Android. It is mainly used for manual triggering, response, forwarding, exception removal, re-routing and other operations.
  • Processing server mainly for basic information management, parameter configuration, analysis reports, etc.
  • the Andeng subsystem is mainly used to solve exception handling problems and open up all aspects of manual exception handling informatization.
  • the Andeng subsystem includes abnormal manual reporting, exception handling, abnormal timeout automatic upgrade reporting, and exception handling personnel level settings.
  • the Anden subsystem will revolve around a core processing logic and solve some common exception handling branches at the same time to ensure exception handling and form a relatively good closed-loop experience when manual processing is solved through the Anden system.
  • the front end of the Anden subsystem uses mobile terminals as the main media to serve users, adopting a lightweight and simple design. Establish robust processing flow and logic on the back end, as well as configure the organizational structure of processing personnel.
  • the mobile terminal of the Andeng subsystem can be developed using the DingTalk mini program method.
  • the management background is solved on the computer side, including parameter configuration issues such as exception handling process configuration, handler hierarchical structure configuration, delayed reporting time control, and data retrieval and query functions are also implemented.
  • FIG. 6 is a schematic structural diagram of the digital twin processing device provided by this application. As shown in Figure 6, the digital twin processing device 200 is located in the digital twin management platform. The digital twin processing device 200 includes:
  • the acquisition module 201 is used to acquire the equipment data of the target equipment in each stage of the industrial equipment life cycle in the IoT management platform and obtain the corresponding mechanism model of the target equipment.
  • the conversion module 202 is used to convert device data into action data corresponding to the mechanism model.
  • the generation module 203 is used to generate a digital twin corresponding to the target device based on the mechanism model and action data.
  • Display module 204 is used to display the digital twin.
  • the digital twin processing device provided in Figure 6 can execute the corresponding method embodiments described above. Its implementation principles and technical effects are similar and will not be described again here.
  • the digital twin processing device provided by this application further refines the digital twin processing device based on the digital twin processing device provided in the previous embodiment, and obtains the digital twin processing device of this embodiment.
  • the conversion module 202 is specifically used to:
  • the digital twin management platform is communicatively connected with the industrial mechanism platform.
  • the acquisition module 201 is specifically used to:
  • the generation module 203 is specifically used to:
  • the digital twin processing device 200 also includes:
  • the business processing module is used to obtain the business data corresponding to the target device collected by the IoT management platform. Convert business data into corresponding business messages. Convert business messages into message display data corresponding to digital twins for display.
  • the digital twin management platform includes: a data computing component.
  • Device data is stored in the IoT database by the IoT management platform.
  • Business data is stored in the business database by the IoT management platform.
  • Obtain module 201 is also used for:
  • the business processing module converts business data into corresponding business messages, it is specifically used to:
  • the digital twin management platform is communicatively connected with the Anden subsystem.
  • the digital twin processing device 200 also includes:
  • the exception display module is used to obtain the production line abnormality early warning table sent by the Anden subsystem.
  • the abnormality early warning table is generated when the ANDEN subsystem detects an abnormality in the industrial equipment in each production line. Display the production line abnormality early warning table accordingly.
  • the digital twin processing device of this embodiment can execute the foregoing corresponding method embodiments. Its implementation principles and technical effects are similar and will not be described again here.
  • FIG 7 is a schematic structural diagram of the digital twin management platform provided by this application.
  • the digital twin management platform includes: a processor 301 and a memory 302.
  • Memory 302 stores computer programs.
  • the processor 301 executes the computer program stored in the memory to implement the steps of the digital twin processing method in the above method embodiment.
  • the processor 301 and the memory 302 are electrically connected directly or indirectly to realize data transmission or interaction.
  • these components can be electrically connected to each other through one or more communication buses or signal lines, such as through a bus.
  • the memory 302 stores computer execution instructions for implementing the data access control method, including at least one software function module that can be stored in the memory 302 in the form of software or firmware.
  • the processor 301 runs the software programs and modules stored in the memory 302, Thereby performing various functional applications and data processing.
  • the memory 302 may be, but is not limited to, random access memory (Random Access Memory, referred to as RAM), read-only memory (Read Only Memory, referred to as: ROM), programmable read-only memory (Programmable Read-Only Memory, referred to as: PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc.
  • RAM random access memory
  • ROM read-only memory
  • PROM programmable read-only memory
  • PROM Programmable Read-Only Memory
  • EPROM Erasable Programmable Read-Only Memory
  • EEPROM Electrically Erasable Programmable Read-Only Memory
  • the software programs and modules in the memory 302 may also include an operating system, which may include various software components and/or drivers for managing system tasks (such as memory management, storage device control, power management, etc.), and Can communicate with various hardware or software components to provide a running environment for other software components.
  • the processor 301 may be an integrated circuit chip with signal processing capabilities.
  • the above-mentioned processor 301 may be a general-purpose processor, including a central processing unit (Central Processing Unit, referred to as: CPU), a network processor (Network Processor, referred to as: NP), etc.
  • CPU Central Processing Unit
  • NP Network Processor
  • a general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.
  • An embodiment of the present application also provides a chip, including: a processor and a memory.
  • the computer program is stored in the memory, and when the processor executes the computer program stored in the memory, the steps of the digital twin processing method in the above method embodiment are implemented.
  • An embodiment of the present application also provides a computer-readable storage medium.
  • Computer-executable instructions are stored in the computer-readable storage medium. When the computer-executable instructions are executed by a processor, they are used to implement the digital twin processing method in the above method embodiment. step.
  • An embodiment of the present application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the digital twin processing method in the above method embodiment.
  • An embodiment of the present application also provides a computer program, including: program code.
  • program code executes the digital twin processing method provided in the first aspect or any possible implementation manner in the first aspect. .
  • Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory.
  • Volatile memory may include random access memory (RAM) or external cache memory.
  • RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Synchlink DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
  • SRAM static RAM
  • DRAM dynamic RAM
  • SDRAM synchronous DRAM
  • DDRSDRAM double data rate SDRAM
  • ESDRAM enhanced SDRAM
  • SLDRAM synchronous chain Synchlink DRAM
  • Rambus direct RAM
  • DRAM direct memory bus dynamic RAM
  • RDRAM memory bus dynamic RAM

Abstract

本申请属于数字孪生技术领域，具体涉及一种数字孪生处理方法及数字孪生系统，用以提高数字孪生对工业产品的生命周期覆盖性。其中，该数字孪生处理方法包括：获取物联管理平台中工业设备生命周期各阶段中目标设备的设备数据以及获取目标设备对应的机理模型；将所述设备数据转化为所述机理模型对应的动作数据；根据所述机理模型和所述动作数据生成目标设备对应的数字孪生；对所述数字孪生进行展示。本申请的方法可以将数字孪生进行展示，以为后续设备生命周期内的各阶段优化提供基础。同时，由于覆盖了生命周期各阶段，提高了数字孪生对工业产品的生命周期覆盖性。

Description

数字孪生处理方法及数字孪生系统
本申请要求于2022年06月21日提交中国专利局、申请号为202210707316.9、申请名称为“数字孪生处理方法及数字孪生系统”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请属于数字孪生技术领域，具体涉及一种数字孪生处理方法及数字孪生系统。
背景技术
数字孪生是指物理产品在虚拟空间中的数字模型，包含了从产品构思到产品退市全生命周期的产品信息。这个“双胞胎”不仅与真实空间中的孪生兄弟形似(包含产品规格，几何模型，材料性能，仿真数据等信息)，而且还能通过安装在产品上的传感器反馈回来的数据，反映产品运行状况，将“表现”得与真实产品一模一样。
目前，在工业数字孪生系统中，以工业互联网为基础，主要用于产品的研发设计和生产制造，无法延伸至工业产品的整个生命周期。
可见，上述方式中数字孪生对工业产品的生命周期覆盖性还有待提高。
发明内容
为了解决现有技术中的上述问题，即为了提高数字孪生对工业产品的生命周期覆盖性，本申请提供了一种数字孪生处理方法及数字孪生系统。
第一方面，本申请提供一种数字孪生处理方法，应用于数字孪生管理平台，所述方法包括：
获取物联管理平台中工业设备生命周期各阶段中目标设备的设备数据以及获取目标设备对应的机理模型；
将所述设备数据转化为所述机理模型对应的动作数据；
根据所述机理模型和所述动作数据生成目标设备对应的数字孪生；
对所述数字孪生进行展示。
在一种可能的实现方式中，所述将所述设备数据转化为所述机理模型对应的动作数据，包括：
将所述设备数据转化为对应模拟数据；
将所述模拟数据转化为目标设备对应机理模型的动作数据。
在一种可能的实现方式中，所述数字孪生管理平台与工业机理平台通信连接；
所述获取目标设备对应的机理模型，包括：
根据目标设备对应的设备标识从工业机理平台中获取对应的机理模型。
在一种可能的实现方式中，所述根据所述机理模型和所述动作数据生成目标设备对应的数字孪生，包括：
将所述动作数据与所述机理模型中的各模型参数进行融合，以生成目标设备对应的数字孪生。
在一种可能的实现方式中，所述方法还包括：
获取所述物联管理平台采集的目标设备对应的业务数据；
将所述业务数据转化为对应业务报文；
将所述业务报文转化为所述数字孪生对应的报文展示数据，以进行展示。
在一种可能的实现方式中，所述数字孪生管理平台包括：数据计算组件；所述设备数据由所述物联管理平台存储于物联数据库；所述业务数据由所述物联管理平台存储于业务数据库；
所述将所述设备数据转化为对应模拟数据之前还包括：
通过WebSocket通信协议从所述物联数据库中获取所述目标设备对应的设备数据；
所述将所述业务数据转化为对应业务报文，包括：
从所述业务数据库中读取所述业务数据；
根据预设业务需求数据对所述业务数据进行统计处理，以生成所述业务报文。
在一种可能的实现方式中，所述数字孪生管理平台与安灯子系统通信连接；
所述方法还包括：
获取安灯子系统发送的产线异常预警报表；所述异常预警报表为所述安灯子系统检测到各产线中的工业设备出现异常时生成的；
将所述产线异常预警报表进行对应展示。
第二方面，本申请提供一种数字孪生处理装置，位于数字孪生管理平台，所述装置包括：
获取模块，用于获取物联管理平台中工业设备生命周期各阶段中目标设备的设备数据以及获取目标设备对应的机理模型；
转化模块，用于将所述设备数据转化为所述机理模型对应的动作数据；
生成模块，用于根据所述机理模型和所述动作数据生成目标设备对应的数字孪生；
展示模块，用于对所述数字孪生进行展示。
在一种可能的实现方式中，所述转化模块具体用于：
将所述设备数据转化为对应模拟数据；将所述模拟数据转化为目标设备对应机理模型的动作数据。
在一种可能的实现方式中，所述数字孪生管理平台与工业机理平台通信连接；
所述获取模块在获取目标设备对应的机理模型时，具体用于：
根据目标设备对应的设备标识从工业机理平台中获取对应的机理模型。
在一种可能的实现方式中，所述生成模块具体用于：
将所述动作数据与所述机理模型中的各模型参数进行融合，以生成目标设备对应的数字孪生。
在一种可能的实现方式中，所述装置还包括：
业务处理模块，用于获取所述物联管理平台采集的目标设备对应的业务数据；将所述业务数据转化为对应业务报文；将所述业务报文转化为所述数字孪生对应的报文展示数据，以进行展示。
在一种可能的实现方式中，所述数字孪生管理平台包括：数据计算组件；所述设备数据由所述物联管理平台存储于物联数据库；所述业务数据由所述物联管理平台存储于业务数据库；
所述获取模块还用于：
通过WebSocket通信协议从所述物联数据库中获取所述目标设备对应的设备数据；
所述业务处理模块在将所述业务数据转化为对应业务报文时，具体用于：
从所述业务数据库中读取所述业务数据；根据预设业务需求数据对所述业务数据进行统计处理，以生成所述业务报文。
在一种可能的实现方式中，所述数字孪生管理平台与安灯子系统通信连接；
所述装置还包括：
异常展示模块，用于获取安灯子系统发送的产线异常预警报表；所述异常预警报表为所述安灯子系统检测到各产线中的工业设备出现异常时生成的；将所述产线异常预警报表进行对应展示。
第三方面，本申请提供一种数字孪生系统，包括：相互通信连接的物联管理平台和如第一方面任一项所述的数字孪生管理平台。
在一种可能的实现方式中，所述物联管理平台包括相互通信连接的边缘管理子平台和物联子平台；所述边缘管理子平台与生产信息化管理系统通信连接；
所述边缘管理子平台用于采集工业设备生命周期各阶段对应的各产线中目标设备的设备数据以及生产信息化管理系统中对应的目标设备的设备数据；
所述物联子平台用于将所述目标设备的设备数据和所述目标设备对应的机理模型发送至所述数字孪生管理平台。
在一种可能的实现方式中，还包括：安灯子系统；所述安灯子系统与所述数字孪生管理平台通信连接；
所述安灯子系统包括：异常触发终端和处理服务器；
所述异常触发终端用于根据用户下发的异常指令生成对应异常信息，并将所述异常信息发送至处理服务器；
所述处理服务器用于根据所述异常信息生成对应产线异常预警报表，并将所述产线异常预警报表发送至所述数字孪生管理平台。
第四方面，本申请提供一种数字孪生管理平台，包括：
存储器和处理器；
所述存储器存储计算机执行指令；
所述处理器执行所述存储器存储的计算机执行指令，以实现第一方面或第一方面中任一可能的实施方式所提供的数字孪生处理方法。
第五方面，本申请提供一种计算机可读存储介质，所述计算机可读介质中存储有计算机执行指令，所述计算机执行指令被处理器执行时用于实现第一方面或第一方面中任一可能的实施方式所提供的数字孪生处理方法。
第六方面，本申请提供一种芯片，包括：
处理器和存储器；
所述存储器存储有计算机程序；
所述处理器执行所述存储器存储的计算机程序时，实现第一方面或第一方面中任一可能的实施方式所提供的数字孪生处理方法。
第七方面，本申请提供一种计算机程序产品，包括计算机程序，所述计算机程序在被处理器执行时实现上述第一方面或第一方面中任一可能的实施方式所提供的数字孪生处理方法。
第八方面，提供一种计算机程序，包括：程序代码，当计算机运行所述计算机程序时，所述程序代码执行如第一方面或第一方面中任一可能的实施方式所提供的数字孪生处理方法。
本领域技术人员能够理解的是，本申请中，数字孪生处理方法包括：获取物联管理平台中工业设备生命周期各阶段中目标设备的设备数据以及获取目标设备对应的机理模型；将所述设备数据转化为所述机理模型对应的动作数据；根据所述机理模型和所述动作数据生成目标设备对应的数字孪生；对所述数字孪生进行展示。本申请的数字孪生处理方法通过获取物联管理平台中工业设备生命周期各阶段中目标设备的设备数据以及获取目标设备对应的机理模型，可以根据机理模型和设备数据生成对应的数据孪生，同时，可以将数字孪生进行展示，以为后续设备生命周期内的各阶段优化提供基础。同时，由于覆盖了生命周期各阶段，提高了数字孪生对工业产品的生命周期覆盖性。
附图说明
下面参照附图来描述本申请的数字孪生处理方法及数字孪生系统的优选实施方式。附图为：
图1为本申请实施例提供的应用场景示例图；
图2是本申请提供的数字孪生处理方法的流程示意图；
图3是本申请提供的数字孪生系统的结构示意图；
图4是本申请提供的数字孪生整体数据的流转示意图；
图5是本申请提供的安灯子系统的网络通信示意图；
图6是本申请提供的数字孪生处理装置的结构示意图；
图7是本申请提供的数字孪生管理平台的结构示意图。
具体实施方式
首先，本领域技术人员应当理解的是，这些实施方式仅仅用于解释本申请的技术原理，并非旨在限制本申请的保护范围。本领域技术人员可以根据需要对其做出调整，以便适应具体的应用场合。
在本申请实施例中使用的术语是仅仅处于描述特定实施例的目的，而非旨在限制本申请。在本申请实施例中所使用的单数形式的“一种”和“该”也旨在包括多数形式，除非上下文清楚地表示其他含义。
应当理解，本文中使用的术语“和/或”仅仅是一种描述关联对象的关联关系，表示可以存在三种关系，例如，A和/或B，可以表示为：单独存在A，同时存在A和B，单独存在B这三种情况。另外，本文中字符“/”，一般表示前后关联对象是一种“或”的关系。
取决于语境，如在此所使用的词语“如果”、“若”可以被解释成为“在……时”或“当……时”或“响应于确定”或“响应于检测”。类似地，取决于语境，短语“如果确定”或“如果检测(陈述的条件或事件)”可以被解释成为“当确定时”或“响应于确定”或“当检测(陈述的条件或事件)时”或“响应于检测(陈述的条件或事件)”。
还需要说明的是，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的商品或者系统不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种商品或者系统所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括要素的商品或者系统中还存在另外的相同要素。
随着科技的不断发展，工业领域逐渐往智能化、数字化发展，从而提高工业设备研发、制造、维护等阶段的效率。数字孪生是目前的主要工业领域的主要研究方向，通过在生产车间搭建数字孪生展示系统，可以促进生产车间的数字化、信息化，整合数据资源，统一数据接入输出标准，将数字作为产线降本增效的核心驱动力。
作为现实世界实物或系统的数字化表达，数字孪生在工业领域，专注于为智能工厂、智能车间和智能设备构建数字孪生以保障生产。在工业数字孪生系统中，依托虚实交互板块的感知和反馈控制通路，根据工业生产活动的真实数据和智能应用的反馈控制指令实现物理实体与虚拟实体的精准映射、交互融合和智能反馈控制。同时，物理空间和数字空间的功能又可在生产运行中进行持续改进迭代升级。
目前的数字孪生技术解决方案以工业互联网为基础，主要用于产品的研发设计和生产制造，难以贯穿工业设备的整个生命周期，无法打通工业设备在设计、制造、调试、运行、维护等各个阶段的问题。因而，数字孪生对工业产品的生命周期覆盖性还有待提高。
所以针对上述方式中数字孪生对工业产品的生命周期覆盖性还有待提高的问题，发明人在研究中发现，为了解决该问题，可以获取工业设备的生命周期的所有设备数据，同时结合对应的机理模型来生成对应的数字孪生，从而提高对工业产品的生命周期覆盖性。
具体的，数字孪生处理方法包括：获取物联管理平台中工业设备生命周期各阶段中目标设备的设备数据以及获取目标设备对应的机理模型。将设备数据转化为机理模型对应的动作数据。根据机理模型和动作数据生成目标设备对应的数字孪生。对数字孪生进行展示。
本申请的数字孪生处理方法通过获取物联管理平台中工业设备生命周期各阶段中目标设备的设备数据以及获取目标设备对应的机理模型，可以根据机理模型和设备数据生成对应的数据孪生，同时，可以将数字孪生进行展示，以为后续设备生命周期内的各阶段优化提供基础。同时，由于覆盖了生命周期各阶段，提高了数字孪生对工业产品的生命周期覆盖性。
发明人基于上述的创造性发现，提出了本申请的技术方案。
图1为本申请实施例提供的应用场景示例图。如图1所示，在应用场景中包括数字孪生管理平台10、物联管理平台30以及工业设备产线50。其中，工业设备产线50中包括多个产线，比如图中产线a、产线b、产线c至产线n等。数字孪生管理平台10可以包括孪生展示模块、孪生管理模块、孪生数据处理模块等，物联管理平台30可以包括边缘管理模块、物联模块等。本实施例的应用场景中，物联管理平台30可以采集工业设备产线的设备数据，从而获取工业设备生命周期各阶段的设备数据。物联管理平台30也可以通过连接其他管理平台获取设备数据，比如通过生产信息管理平台获取。
物联管理平台30中可以存储有机理模型，也可以从其他平台获取机理模型，比如从工业机理平台获取。机理模型与每个工业设备相匹配，匹配度越高的机理模型，可以使对应生成的孪生数字效果越好。在进行孪生数字搭建时，本应用场景中，物联管理平台30实时采集工业设备产线50中各产线的设备数据，比如可以包括研发、制造、维护等生命周期各阶段产线的设备数据，然后物联管理平台30将采集到的设备数据和对应的机理模型发送至数字孪生管理平台10。数字孪生管理平台10将设备数据转化为机理模型对应的动作数据，并根据机理模型和动作数据生成目标设备对应的数字孪生。同时，数字孪生管理平台10对数字孪生进行展示。
在数字孪生管理平台10对数字孪生进行展示的过程中，物联管理平台30会继续采集工业设备生命周期各阶段的设备数据，数字孪生管理平台10在接收到后续的设备数据后，将对生成的数字孪生进行更新迭代，以使数字孪生可以更有效的反映对应工业设备的生命进程发展状态。展示的数字孪生可以为后续优化工业设备产线结构提供基础。
下面结合说明书附图对本申请实施例进行介绍。
图2是本申请提供的数字孪生处理方法的流程示意图。如图2所示，本实施例中，本申请实施例的执行主体为数字孪生处理装置，该数字孪生处理装置可以集成在数字孪生管理平台中。数字孪生管理平台可以包括多个电子设备。该方法包括：
S101、获取物联管理平台中工业设备生命周期各阶段中目标设备的设备数据以及获取目标设备对应的机理模型。
其中，生命周期包括研发、制造、维护、更新优化等阶段，涵盖企业生产制造过程从设备层、产线层到车间层、工厂层等不同的层级，贯穿于生产制造的设计、工艺管理和优化、资源配置、参数调整、质量管理和追溯、能效管理、生产排程等各个环节。
目标设备的设备数据可以包括静态数据和动态数据，静态数据包括设备外观、设备结构等，动态数据可以包括设备运行数据、设备能耗等。通过获取工业设备生命周期各阶段中目标设备的设备数据可以提高后续孪生数字对工业设备生命周期的覆盖度。
物联管理平台可以是基于工业互联网的业务即服务BaaS层和物联网层，通过构建得到。比如可以通过BaaS层的开发者平台开发物联管理平台的平台架构，具体的可以基于springboot框架进行构建。同时，本实施例中包括数字孪生管理平台在内的各个平台都可以基于BaaS层引擎进行构建，从而实现将技术方案与商业逻辑和最佳实践有机结合。
BaaS引擎强调商业逻辑和最佳实践，用于快速搭建数字孪生应用的云端服务平台，其中BaaS能力包括：
“机理模型平台”主要提供企业设备模型，设备机理管理，包括上千个机理模型库。
“知识图谱服务”提供设备故障及维修保养知识内容管理。
“标识解析平台”以一物一码的方式记录了设备及产品的零配件追溯信息，有效解决了企业备品备件的管理问题。
“工业大数据平台”提供了海量数据的实时接入。
“人工智能平台”通过数据以及算法模型提升企业智能化能力。
物联管理平台可以通过采集工业设备生命周期各阶段产线对应的设备数据从而得到工业设备生命周期各阶段中目标设备的设备数据，也可以通过连接的生产信息化管理系统采集部分设备数据。
机理模型可以是从BaaS层的工业机理平台获取，也可以是首先由物联管理平台从工业机理平台获取，再从物联管理平台中获取。机理模型与工业设备之间的匹配度越高，越能模拟工业设备的生命周期过程，从而提高数字孪生的准确性。
S102、将设备数据转化为机理模型对应的动作数据。
由于设备数据是静态的参数数据，需要转化成机理模型对应的动作数据，以方面对目标设备进行模拟。比如设备数据是转速300转每秒，则需转化成对应的旋转动作数据。
S103、根据机理模型和动作数据生成目标设备对应的数字孪生。
将机理模型和动作数据相融合，可以构建并生成目标设备对应的数字孪生。
S104、对数字孪生进行展示。
对数字孪生进行展示可以是全场景的展示方式，从而方便用户对每个设备的数字孪生进行查看，且可以更好地看出设备生命周期的变化情况。同时，通过对数字孪生进行展示可以实现对设备全面的状态监测，同时，还可以为后续数字孪生迭代、设备的更新迭代提供更好的参考基础。
本公开实施例中，通过获取物联管理平台中工业设备生命周期各阶段中目标设备的设备数据以及获取目标设备对应的机理模型，可以根据机理模型和设备数据生成对应的数据孪生，同时，可以将数字孪生进行展示，以为后续设备生命周期内的各阶段优化提供基础。同时，由于覆盖了生命周期各阶段，提高了数字孪生对工业产品的生命周期覆盖性。
同时，在本申请上一实施例提供的数字孪生处理方法的基础上，可以进一步的细化本申请方案。则本实施例提供的数字孪生处理方法包括以下步骤。
S201、获取物联管理平台中工业设备生命周期各阶段中目标设备的设备数据。
步骤201的实现方式与本申请上一实施例中的步骤101的实现方式类似，在此不再一一赘述。
S202、根据目标设备对应的设备标识从工业机理平台中获取对应的机理模型。
每个设备都预设有对应的设备标识，该设备标识可以在BaaS层的标识解析平台中进行存储和管理。同时，工业机理平台中存储有各工业设备对应的机理模型，且可以对工业机理模型进行配置。
S203、将设备数据转化为对应模拟数据。
由于单纯的设备数据仅能反映设备的运行状态，比如转速、频率等。并不能直观的展现设备的动作。因而，可以先将设备数据转化为对应模拟数据，为后续转化为动作数据提供基础。将设备数据转化为模拟数据可以通过常用的模拟数据生成器来转化。
S204、将模拟数据转化为目标设备对应机理模型的动作数据。
在将设备数据转化为模拟数据后，具有了能转化为动作数据的基础，可以通过动作数据转换引擎来对模拟数据进行数据转换，从而将模拟数据转化为目标设备对应机理模型的动作数据。
S205、将动作数据与机理模型中的各模型参数进行融合，以生成目标设备对应的数字孪生。
将动作数据与机理模型进行融合，可以使机理模型能从静止状态转化为动态状态，同时，根据该动作数据，可以使机理模型模拟目标设备运转，从而生成目标设备对应的数字孪生。
可选的，本实施例中还可以结合目标设备的业务数据进行分析和展示。具体如下：
获取物联管理平台采集的目标设备对应的业务数据。
将业务数据转化为对应业务报文。
将业务报文转化为数字孪生对应的报文展示数据，以进行展示。
物联管理平台可以采集目标设备对应的业务数据，比如从BaaS层的大数据平台中获取。业务数据可以包括目标设备的价格、售卖时间等。
通过将业务报文转化为数字孪生对应的报文展示数据，可以使数字孪生同步展示对应报文展示数据，从而为用户后续优化工业设备的业务相关阶段提供基础。
可选的，数字孪生管理平台包括：数据计算组件。设备数据由物联管理平台存储于物联数据库。业务数据由物联管理平台存储于业务数据库。物联数据库和业务数据库可以采用Oracle数据库(一种关系型数据库)、SQL(英文全称为：Structured Query Language server database，结构化查询语言)Server数据库、MySQL数据库(一种关系型数据库)，TDengine高性能时序数据库等。
将设备数据转化为对应模拟数据之前还包括：
通过WebSocket通信协议从物联数据库中获取目标设备对应的设备数据。
将业务数据转化为对应业务报文，包括：
从业务数据库中读取业务数据。
根据预设业务需求数据对业务数据进行统计处理，以生成业务报文。
本实施例中，通过WebSocket通信协议可以提高数字孪生管理平台与数据库之间的交互效率。同时，可以基于WebSocket通信协议构建WebSocket数据服务，以构建获取设备数据至转化设备数据的整体流程。
预设业务需求可以预先根据实际应用场景进行设置，比如可以设置为运输优化、产线流程优化等。从而根据预设业务需求对业务数据进行统计以及分析，从而生成对应业务报文。
S206、对数字孪生进行展示。
步骤206的实现方式与本申请上一实施例中的步骤104的实现方式类似，在此不再一一赘述。
可选的，本实施例中，数字孪生管理平台与安灯子系统通信连接，通过安灯子系统打通人工异常处理信息化的各个环节，进一步提高异常处理效率，以及为优化工业设备生命周期各阶段的整个流程提供基础。
方法还包括：
获取安灯子系统发送的产线异常预警报表。异常预警报表为安灯子系统检测到各产线中的工业设备出现异常时生成的。
将产线异常预警报表进行对应展示。
产线异常预警报表可以根据安灯子系统中的异常触发终端触发的异常信息生成。安灯子系统包括多个异常触发终端，该异常触发终端可以由用户进行异常问题触发，当用户发现产线上出现异常问题时，可以通过异常触发终端触发异常问题，从而使安灯子系统生成产线异常预警报表。
当数字孪生管理平台接收到产线异常预警报表时，可以结合数字孪生进行统一展示。比如该异常预警报表与某工业设备相关，则可以与某工业设备对应的数字孪生进行同步展示。同时，安灯子系统也可以将异常预警报表上传至管理异常问题的客户端，以通知管理人员进行异常处理。若异常问题可以不通过人工解决，则安灯子系统还可以根据异常信息从预设数据库中确定异常解决策略，从而根据该异常解决策略解决对应的异常问题。
图3是本申请提供的数字孪生系统的结构示意图。如图3所示，本实施例中，还提供一种数字孪生系统，该数字孪生系统100包括：
相互通信连接的物联管理平台30和如上述任一实施例中的数字孪生管理平台10。数字孪生管理平台10，可以基于JAVA(软件语言)编码使用springboot框架，可与各个数字孪生渲染设备进行交互同步设备布局，设备数据配置，设备组合等信息。
物联管理平台30用于边缘管理、产线数据的采集、业务数据采集以及物联数据的管理。
可选的，本实施例中，物联管理平台30包括相互通信连接的边缘管理子平台和物联子平台。边缘管理子平台与生产信息化管理系统通信连接。
边缘管理子平台用于采集工业设备生命周期各阶段对应的各产线中目标设备的设备数据以及生产信息化管理系统中对应的目标设备的设备数据。
物联子平台用于将目标设备的设备数据和目标设备对应的机理模型发送至数字孪生管理平台。
可选的，本实施例中，数字孪生系统100还包括：安灯子系统。安灯子系统与数字孪生管理平台10通信连接。
安灯子系统包括：异常触发终端和处理服务器。
异常触发终端用于根据用户下发的异常指令生成对应异常信息，并将异常信息发送至处理服务器。
处理服务器用于根据异常信息生成对应产线异常预警报表，并将产线异常预警报表发送至数字孪生管理平台。
为了更好的理解本实施例的数字孪生系统，下面将结合图4来进行详细说明。图4中，数字孪生系统包括数字孪生管理平台、物联管理平台、业务数据库(图中简称为业务库)、物联数据库(图中简称为IoT库集群)以及安灯子系统(图中仅展示了安灯系统后台，未展示全部结构)。物联管理平台负责进行边缘管理、产线数据的采集、业务数据采集以及物联数据的管理。数字孪生管理平台负责数字孪生的处理以及全场景数字孪生内容展示的参数配置同步，实现全场景的实时展示。安灯子系统负责产线异常报警和处理。
数字孪生管理平台包括全场景数字孪生模块、动作服务模块、数据服务模块、数据计算组件。物联管理平台包括边缘管理子平台包括图中的边缘管理和边缘Agent，其中，边缘Agent指边缘实体设备。还包括物联子平台即图中IoT平台，反控代理模块。生产信息化管理系统即图中MES(英文全称为：Manufacturing Execution System)。业务数据库采用了Oracle数据库、SQL Server数据库、MySQL数据库。物联数据库可以采用zookeeper，一种分布式应用程序协调服务软件，来对数据库进行分布式管理，支持分布式锁，集群成员选举。
物联管理平台能直接采集产线上产线设备的数据，并接驳MES系统以获取MES系统中的设备数据。同时，在采集完设备数据后，将数据以mqtt(英文全称为：Message Queuing Telemetry Transport，消息队列遥测传输)协议同步至物联子平台，由物联子平台将设备数据存储在物联数据库中。同时提供反控代理，为产线未来的反控实现提供支撑。
物联数据库中的数据通过websocket协议，经由数字孪生管理平台中的动作服务模块，通过其中的模拟数据生成器将设备数据转化为模拟数据，通过实时动作转换引擎将模拟数据转化为对应动作数据。
本实施例的业务数据，存储在业务库中，通过数据计算组件抽取业务库中的业务数据，并进行统计计算处理，生成报表数据。该报表数据可以如图中所示，写入物联数据库中，然后再通过数据服务以及restful(英文全称为：Representational State Transfer)服务将报表数据转化为数字孪生的报表展示内容。也可以直接由数据服务以及restful服务将报表数据转化为数字孪生的报表展示内容。
同时，安灯系统后台在监测到产线出现问题，则可以将产线异常预警报表(图中简称为报表)，发送至数字孪生管理平台的数据服务，通过数据服务以及restful服务将报表数据转化为数字孪生的报表展示内容。
图5是本申请提供的安灯子系统的网络通信示意图，如图5所示，为了更好的理解本实施例的安灯子系统，下面将结合附图进行具体说明。
电子设备中的小程序通过安灯服务器的接口标准和数据接口对接，小程序“上报/响应”会转发到nginx服务器，通过nginx服务器反向代理，nginx服务器需要https(全称：Hyper Text Transfer Protocol over SecureSocket Layer，中文为：超文本传输安全协议)协议与电子设备的小程序构建网络连接，并进行限流处理。nginx服务器会将小程序的内容转发到服务端，服务端与前端、物联子平台(即图中IoT平台)、消息队列、数据库分别通信连接，其都属于内网操作。注：所有访问服务端的接口都需要鉴别权限，同时支持限流，确保服务稳定。
IoT平台可以接收到物联设备的异常触发，从而进行自动报警。
前端指前端电子设备，比如可以采用移动端，可以将触发的异常信息转化为产线异常预警报表，并通过大屏显示报表，也可以将该产线异常预警报表发送至数字孪生管理平台。IoT平台、前端与服务端通过http(英文全称为：Hyper Text Transfer Protocol，中文为：超文本传输协议) 协议进行通信连接。
服务端可以对异常报警、异常消息以及数据库中存储的数据进行管理和处理。
安灯子系统是指向管理、维护和其他工人通知质量或流程问题的一套系统。安灯子系统从功能上主要可分为两部分：
(1)异常触发终端，采用移动端并支持IOS、安卓等多端部署，主要用于人工触发、响应、转发、解除异常、复线等操作。
(2)处理服务器，主要是对于基础信息管理、参数配置、分析报表等。
安灯子系统主要用以解决异常处理问题，打通人工异常处理信息化的各个环节，安灯子系统包含异常人工上报，异常处理，异常超时自动升级上报，异常处理人员层级设置。
安灯子系统会围绕一个核心处理逻辑，同时解决常见的一些异常处理分支，保证异常处理，在人工处理通过安灯系统解决时能形成比较良好的闭环体验。
安灯子系统前端以移动端为主要媒介服务用户，采取轻量化简洁化设计。后端建设鲁棒的处理流程和逻辑，以及处理人员组织架构配置。安灯子系统移动端可以采用钉钉小程序方式进行开发。管理后台在计算机端解决，包含异常处理流程配置，处理人员层级结构配置，延迟上报时间控制在内的参数配置问题，同时实现数据调阅查询功能。
图6是本申请提供的数字孪生处理装置的结构示意图。如图6所示，该数字孪生处理装置200位于数字孪生管理平台中，数字孪生处理装置200包括：
获取模块201，用于获取物联管理平台中工业设备生命周期各阶段中目标设备的设备数据以及获取目标设备对应的机理模型。
转化模块202，用于将设备数据转化为机理模型对应的动作数据。
生成模块203，用于根据机理模型和动作数据生成目标设备对应的数字孪生。
展示模块204，用于对数字孪生进行展示。
图6提供的数字孪生处理装置，可以执行前述相应方法实施例，其实现原理和技术效果类似，在此不再赘述。
同时，本申请提供的数字孪生处理装置在上一实施例提供的数字孪生处理装置的基础上，对数字孪生处理装置进行了进一步的细化，得到本实施例的数字孪生处理装置。
在一种可能的实现方式中，转化模块202具体用于：
将设备数据转化为对应模拟数据。将模拟数据转化为目标设备对应机理模型的动作数据。
在一种可能的实现方式中，数字孪生管理平台与工业机理平台通信连接。
获取模块201在获取目标设备对应的机理模型时，具体用于：
根据目标设备对应的设备标识从工业机理平台中获取对应的机理模型。
在一种可能的实现方式中，生成模块203具体用于：
将动作数据与机理模型中的各模型参数进行融合，以生成目标设备对应的数字孪生。
在一种可能的实现方式中，数字孪生处理装置200还包括：
业务处理模块，用于获取物联管理平台采集的目标设备对应的业务数据。将业务数据转化为对应业务报文。将业务报文转化为数字孪生对应的报文展示数据，以进行展示。
在一种可能的实现方式中，数字孪生管理平台包括：数据计算组件。设备数据由物联管理平台存储于物联数据库。业务数据由物联管理平台存储于业务数据库。
获取模块201还用于：
通过WebSocket通信协议从物联数据库中获取目标设备对应的设备数据。
业务处理模块在将业务数据转化为对应业务报文时，具体用于：
从业务数据库中读取业务数据。根据预设业务需求数据对业务数据进行统计处理，以生成业务报文。
在一种可能的实现方式中，数字孪生管理平台与安灯子系统通信连接。
数字孪生处理装置200还包括：
异常展示模块，用于获取安灯子系统发送的产线异常预警报表。异常预警报表为安灯子系统检测到各产线中的工业设备出现异常时生成的。将产线异常预警报表进行对应展示。
本实施例的数字孪生处理装置，可以执行前述相应方法实施例，其实现原理和技术效果类似，在此不再赘述。
图7为本申请提供的数字孪生管理平台的结构示意图。图7所示，该数字孪生管理平台包括：处理器301和存储器302。存储器302存储有计算机程序。处理器301执行存储器存储的计算机程序，实现上述方法实施例中数字孪生处理方法的步骤。
在上述数字孪生管理平台中，处理器301与存储器302之间直接或间接地电性连接，以实现数据的传输或交互。例如，这些元件相互之间可以通过一条或者多条通信总线或信号线实现电性连接，如可以通过总线连接。存储器302中存储有实现数据访问控制方法的计算机执行指令，包括至少一个可以软件或固件的形式存储于存储器302中的软件功能模块，处理器301通过运行存储在存储器302内的软件程序以及模块，从而执行各种功能应用以及数据处理。
存储器302可以是，但不限于，随机存取存储器(Random Access Memory，简称：RAM)，只读存储器(Read Only Memory，简称：ROM)，可编程只读存储器(Programmable Read-Only Memory，简称：PROM)，可擦除只读存储器(Erasable Programmable Read-Only Memory，简称：EPROM)，电可擦除只读存储器(Electric Erasable Programmable Read-Only Memory，简称：EEPROM)等。进一步地，上述存储器302内的软件程序以及模块还可包括操作系统，其可包括各种用于管理系统任务(例如内存管理、存储设备控制、电源管理等)的软件组件和/或驱动，并可与各种硬件或软件组件相互通信，从而提供其他软件组件的运行环境。
处理器301可以是一种集成电路芯片，具有信号的处理能力。上述的处理器301可以是通用处理器，包括中央处理器(Central Processing Unit，简称：CPU)、网络处理器(Network Processor，简称：NP)等。可以实现或者执行本申请实施例中的公开的各方法、步骤及逻辑框图。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
本申请的一实施例还提供了一种芯片，包括：处理器和存储器。存储器上存储有计算机程序，处理器执行存储器存储的计算机程序时，实现上述方法实施例中数字孪生处理方法的步骤。
本申请的一实施例还提供了一种计算机可读存储介质，计算机可读存储介质中存储有计算机执行指令，计算机执行指令被处理器执行时用于实现上述方法实施例中数字孪生处理方法的步骤。
本申请的一实施例还提供了一种计算机程序产品，包括计算机程序，所述计算机程序在被处理器执行时实现上述方法实施例中数字孪生处理方法的步骤。
本申请的一实施例还提供一种计算机程序，包括：程序代码，当计算机运行计算机程序时，程序代码执行如第一方面或第一方面中任一可能的实施方式所提供的数字孪生处理方法。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机程序来指令相关的硬件来完成，计算机程序可存储于一非易失性计算机可读取存储介质中，该计算机程序在执行时，可包括如上述各方法的实施例的流程。其中，本申请所提供的各实施例中所使用的对存储器、存储、数据库或其它介质的任何引用，均可包括非易失性和/或易失性存储器。非易失性存储器可包括只读存储器(ROM)、可编程ROM(PROM)、电可编程ROM(EPROM)、电可擦除可编程ROM(EEPROM)或闪存。易失性存储器可包括随机存取存储器(RAM)或者外部高速缓冲存储器。作为说明而非局限，RAM以多种形式可得，诸如静态RAM(SRAM)、动态RAM(DRAM)、同步DRAM(SDRAM)、双数据率SDRAM(DDRSDRAM)、增强型SDRAM(ESDRAM)、同步链路(Synchlink)DRAM(SLDRAM)、存储器总线(Rambus)直接RAM(RDRAM)、直接存储器总线动态RAM(DRDRAM)、以及存储器总线动态RAM(RDRAM)等。
至此，已经结合附图所示的优选实施方式描述了本申请的技术方案， 但是，本领域技术人员容易理解的是，本申请的保护范围显然不局限于这些具体实施方式。在不偏离本申请的原理的前提下，本领域技术人员可以对相关技术特征作出等同的更改或替换，这些更改或替换之后的技术方案都将落入本申请的保护范围之内。

Claims (14)

  1. 一种数字孪生处理方法，其特征在于，应用于数字孪生管理平台，所述方法包括：
    获取物联管理平台中工业设备生命周期各阶段中目标设备的设备数据以及获取目标设备对应的机理模型；
    将所述设备数据转化为所述机理模型对应的动作数据；
    根据所述机理模型和所述动作数据生成目标设备对应的数字孪生；
    对所述数字孪生进行展示。
  2. 根据权利要求1所述的数字孪生处理方法，其特征在于，所述将所述设备数据转化为所述机理模型对应的动作数据，包括：
    将所述设备数据转化为对应模拟数据；
    将所述模拟数据转化为目标设备对应机理模型的动作数据。
  3. 根据权利要求1或2所述的数字孪生处理方法，其特征在于，所述数字孪生管理平台与工业机理平台通信连接；
    所述获取目标设备对应的机理模型，包括：
    根据目标设备对应的设备标识从工业机理平台中获取对应的机理模型。
  4. 根据权利要求1或2所述的数字孪生处理方法，其特征在于，所述根据所述机理模型和所述动作数据生成目标设备对应的数字孪生，包括：
    将所述动作数据与所述机理模型中的各模型参数进行融合，以生成目标设备对应的数字孪生。
  5. 根据权利要求1至4任一项所述的数字孪生处理方法，其特征在于，所述方法还包括：
    获取所述物联管理平台采集的目标设备对应的业务数据；
    将所述业务数据转化为对应业务报文；
    将所述业务报文转化为所述数字孪生对应的报文展示数据，以进行展示。
  6. 根据权利要求5所述的数字孪生处理方法，其特征在于，所述数字孪生管理平台包括：数据计算组件；所述设备数据由所述物联管理平台存储于物联数据库；所述业务数据由所述物联管理平台存储于业务数据库；
    所述将所述设备数据转化为对应模拟数据之前还包括：
    通过WebSocket通信协议从所述物联数据库中获取所述目标设备对应的设备数据；
    所述将所述业务数据转化为对应业务报文，包括：
    从所述业务数据库中读取所述业务数据；
    根据预设业务需求数据对所述业务数据进行统计处理，以生成所述业务报文。
  7. 根据权利要求1至4任一项所述的数字孪生处理方法，其特征在于，所述数字孪生管理平台与安灯子系统通信连接；
    所述方法还包括：
    获取安灯子系统发送的产线异
    常预警报表；所述异常预警报表为所述安灯子系统检测到各产线中的工业设备出现异常时生成的；
    将所述产线异常预警报表进行对应展示。
  8. 一种数字孪生系统，其特征在于，包括：相互通信连接的物联管理平台和如权利要求1至7任一项所述的数字孪生管理平台。
  9. 根据权利要求8所述的数字孪生系统，其特征在于，所述物联管理平台包括相互通信连接的边缘管理子平台和物联子平台；所述边缘管理子平台与生产信息化管理系统通信连接；
    所述边缘管理子平台用于采集工业设备生命周期各阶段对应的各产线中目标设备的设备数据以及生产信息化管理系统中对应的目标设备的设备数据；
    所述物联子平台用于将所述目标设备的设备数据和所述目标设备对应的机理模型发送至所述数字孪生管理平台。
  10. 根据权利要求8或9所述的数字孪生系统，其特征在于，还包括：安灯子系统；所述安灯子系统与所述数字孪生管理平台通信连接；
    所述安灯子系统包括：异常触发终端和处理服务器；
    所述异常触发终端用于根据用户下发的异常指令生成对应异常信息，并将所述异常信息发送至处理服务器；
    所述处理服务器用于根据所述异常信息生成对应产线异常预警报表，并将所述产线异常预警报表发送至所述数字孪生管理平台。
  11. 一种数字孪生处理装置，其特征在于，位于数字孪生管理平台，所述装置包括：
    获取模块，用于获取物联管理平台中工业设备生命周期各阶段中目标设备的设备数据以及获取目标设备对应的机理模型；
    转化模块，用于将所述设备数据转化为所述机理模型对应的动作数据；
    生成模块，用于根据所述机理模型和所述动作数据生成目标设备对应的数字孪生；
    展示模块，用于对所述数字孪生进行展示。
  12. 一种计算机可读存储介质，其特征在于，用于存储计算机程序，所述计算机程序包括实现如权利要求1至7中任一项所述的数字孪生处理方法的指令。
  13. 一种计算机程序产品，其特征在于，包括计算机程序，所述计算机程序被处理器执行时实现权利要求1至7中任一项所述的数字孪生处理方法。
  14. 一种计算机程序，其特征在于，包括：程序代码，当计算机运行所述计算机程序时，所述程序代码执行如权利要求1至7中任一项所述的数字孪生处理方法。
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190354922A1 (en) * 2017-11-21 2019-11-21 International Business Machines Corporation Digital twin management in iot systems
CN112085261A (zh) * 2020-08-19 2020-12-15 浙江工业大学 基于云端融合和数字孪生技术的企业生产现状诊断方法
CN113534760A (zh) * 2021-08-02 2021-10-22 上海奇梦网络科技有限公司 一种基于数字孪生平台的制造业工厂管理系统
CN113805550A (zh) * 2021-09-30 2021-12-17 上海卫星装备研究所 基于数字孪生的航天器装配过程管控方法及系统
CN115034638A (zh) * 2022-06-21 2022-09-09 海尔数字科技（上海）有限公司 数字孪生处理方法及数字孪生系统

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190354922A1 (en) * 2017-11-21 2019-11-21 International Business Machines Corporation Digital twin management in iot systems
CN112085261A (zh) * 2020-08-19 2020-12-15 浙江工业大学 基于云端融合和数字孪生技术的企业生产现状诊断方法
CN113534760A (zh) * 2021-08-02 2021-10-22 上海奇梦网络科技有限公司 一种基于数字孪生平台的制造业工厂管理系统
CN113805550A (zh) * 2021-09-30 2021-12-17 上海卫星装备研究所 基于数字孪生的航天器装配过程管控方法及系统
CN115034638A (zh) * 2022-06-21 2022-09-09 海尔数字科技（上海）有限公司 数字孪生处理方法及数字孪生系统

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