Digital twin processing method and digital twin system

Abstract

The application belongs to the technical field of digital twinning, and particularly relates to a digital twinning processing method and a digital twinning system, which are used to improve the life cycle coverage of digital twinning on industrial products. The digital twinning processing method comprises the following steps: obtaining equipment data of a target equipment in each stage of the life cycle of an industrial equipment in an Internet of Things management platform and obtaining a mechanism model corresponding to the target equipment; converting the equipment data into action data corresponding to the mechanism model; generating a digital twin corresponding to the target equipment according to the mechanism model and the action data; and displaying the digital twin. The method can display the digital twin to provide a basis for optimizing each stage in the life cycle of the equipment. Meanwhile, since each stage of the life cycle is covered, the life cycle coverage of digital twinning on industrial products is improved.

Description

Technical Field

[0001] This application belongs to the field of digital twin technology, specifically relating to a digital twin processing method and a digital twin system. Background Technology

[0002] A digital twin is a digital model of a physical product in virtual space, containing product information throughout its entire lifecycle, from conception to market withdrawal. This "twin" not only resembles its real-world counterpart (including product specifications, geometric models, material properties, simulation data, etc.), but also reflects the product's operational status through data fed back from sensors installed on the product, making it "perform" exactly like the real product.

[0003] Currently, industrial digital twin systems, based on the Industrial Internet, are mainly used for product research and development, design, and manufacturing, and cannot be extended to the entire life cycle of industrial products.

[0004] It is evident that the coverage of digital twins across the entire lifecycle of industrial products using the methods described above still needs improvement. Summary of the Invention

[0005] To address the aforementioned problems in the prior art, namely to improve the lifecycle coverage of digital twins for industrial products, this application provides a digital twin processing method and a digital twin system.

[0006] Firstly, this application provides a digital twin processing method applied to a digital twin management platform, the method comprising:

[0007] Acquire equipment data of target devices at each stage of the industrial equipment lifecycle in the IoT management platform, and obtain the corresponding mechanism model of the target devices;

[0008] The device data is converted into action data corresponding to the mechanism model;

[0009] A digital twin of the target device is generated based on the aforementioned mechanism model and the aforementioned action data;

[0010] The digital twin is then demonstrated.

[0011] In one possible implementation, converting the device data into action data corresponding to the mechanism model includes:

[0012] The device data is converted into corresponding simulation data;

[0013] The simulation data is then converted into action data for the corresponding mechanism model of the target device.

[0014] In one possible implementation, the digital twin management platform is communicatively connected to the industrial mechanism platform;

[0015] The acquisition of the mechanism model corresponding to the target device includes:

[0016] The corresponding mechanism model is obtained from the industrial mechanism platform based on the equipment identifier of the target equipment.

[0017] In one possible implementation, generating a digital twin of the target device based on the mechanism model and the action data includes:

[0018] The motion data is fused with the parameters of each model in the mechanism model to generate a digital twin of the target device.

[0019] In one possible implementation, the method further includes:

[0020] Obtain the business data corresponding to the target device collected by the IoT management platform;

[0021] The business data is converted into corresponding business messages;

[0022] The business message is converted into message display data corresponding to the digital twin for display.

[0023] In one possible implementation, the digital twin management platform includes: a data computing component; the device data is stored in an IoT database by the IoT management platform; and the business data is stored in a business database by the IoT management platform.

[0024] Before converting the device data into corresponding analog data, the process also includes:

[0025] The device data corresponding to the target device is obtained from the IoT database through the WebSocket communication protocol;

[0026] The step of converting the business data into corresponding business messages includes:

[0027] Read the business data from the business database;

[0028] The business data is statistically processed based on preset business requirements data to generate the business message.

[0029] In one possible implementation, the digital twin management platform is communicatively connected to the Andon subsystem;

[0030] The method further includes:

[0031] Obtain the production line anomaly warning report sent by the Andon subsystem; the anomaly warning report is generated by the Andon subsystem when it detects an anomaly in the industrial equipment in each production line;

[0032] The corresponding production line anomaly early warning report will be displayed.

[0033] Secondly, this application provides a digital twin processing apparatus located on a digital twin management platform, the apparatus comprising:

[0034] The acquisition module is used to acquire equipment data of target equipment at each stage of the industrial equipment lifecycle in the IoT management platform, as well as to acquire the corresponding mechanism model of the target equipment.

[0035] The conversion module is used to convert the device data into action data corresponding to the mechanism model;

[0036] The generation module is used to generate a digital twin of the target device based on the mechanism model and the action data;

[0037] The display module is used to display the digital twin.

[0038] In one possible implementation, the conversion module is specifically used for:

[0039] The device data is converted into corresponding simulation data; the simulation data is then converted into action data of the target device's corresponding mechanism model.

[0040] In one possible implementation, the digital twin management platform is communicatively connected to the industrial mechanism platform;

[0041] When acquiring the mechanism model corresponding to the target device, the acquisition module is specifically used for:

[0042] The corresponding mechanism model is obtained from the industrial mechanism platform based on the equipment identifier of the target equipment.

[0043] In one possible implementation, the generation module is specifically used for:

[0044] The motion data is fused with the parameters of each model in the mechanism model to generate a digital twin of the target device.

[0045] In one possible implementation, the device further includes:

[0046] The business processing module is used to acquire business data corresponding to the target device collected by the IoT management platform; convert the business data into corresponding business messages; and convert the business messages into message display data corresponding to the digital twin for display.

[0047] In one possible implementation, the digital twin management platform includes: a data computing component; the device data is stored in an IoT database by the IoT management platform; and the business data is stored in a business database by the IoT management platform.

[0048] The acquisition module is also used for:

[0049] The device data corresponding to the target device is obtained from the IoT database through the WebSocket communication protocol;

[0050] When the service processing module converts the service data into corresponding service messages, it is specifically used for:

[0051] Read the business data from the business database; perform statistical processing on the business data according to preset business requirement data to generate the business message.

[0052] In one possible implementation, the digital twin management platform is communicatively connected to the Andon subsystem;

[0053] The device further includes:

[0054] The anomaly display module is used to obtain production line anomaly warning reports sent by the Andon subsystem; the anomaly warning reports are generated when the Andon subsystem detects anomalies in industrial equipment in each production line; and the production line anomaly warning reports are displayed accordingly.

[0055] Thirdly, this application provides a digital twin system, comprising: an IoT management platform and a digital twin management platform as described in any of the first aspects, which are interconnected.

[0056] In one possible implementation, the IoT management platform includes an edge management sub-platform and an IoT sub-platform that are interconnected; the edge management sub-platform is interconnected with the production information management system.

[0057] 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, as well as equipment data of target equipment in the production information management system.

[0058] 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.

[0059] In one possible implementation, it further includes: an Andon subsystem; the Andon subsystem being communicatively connected to the digital twin management platform;

[0060] The Andon subsystem includes: an abnormal triggering terminal and a processing server;

[0061] The exception triggering terminal is used to generate corresponding exception information according to the exception command issued by the user, and send the exception information to the processing server;

[0062] The processing server is used to generate a corresponding production line anomaly early warning report based on the anomaly information, and send the production line anomaly early warning report to the digital twin management platform.

[0063] Fourthly, this application provides a digital twin management platform, comprising:

[0064] Memory and processor;

[0065] The memory stores computer-executed instructions;

[0066] The processor executes computer execution instructions stored in the memory to implement the digital twin processing method provided by the first aspect or any possible implementation of the first aspect.

[0067] Fifthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the digital twin processing method provided in the first aspect or any possible implementation thereof.

[0068] Sixthly, this application provides a chip, comprising:

[0069] Processor and memory;

[0070] The memory stores computer programs;

[0071] When the processor executes the computer program stored in the memory, it implements the digital twin processing method provided by the first aspect or any possible implementation of the first aspect.

[0072] In a seventh aspect, this application provides a computer program product, including a computer program that, when executed by a processor, implements the digital twin processing method provided in the first aspect or any possible implementation thereof.

[0073] Those skilled in the art will understand that the digital twin processing method in this application includes: acquiring equipment data of a target device at each stage of the industrial equipment lifecycle in an IoT management platform and acquiring the corresponding mechanism model of the target device; converting the equipment data into action data corresponding to the mechanism model; generating a digital twin of the target device based on the mechanism model and the action data; and displaying the digital twin. The digital twin processing method of this application, by acquiring equipment data of a target device at each stage of the industrial equipment lifecycle in an IoT management platform and acquiring the corresponding mechanism model of the target device, can generate a corresponding data twin based on the mechanism model and equipment data. Simultaneously, it can display the digital twin to provide a basis for subsequent optimization at each stage of the equipment lifecycle. Furthermore, because it covers all stages of the lifecycle, it improves the lifecycle coverage of the digital twin for industrial products. Attached Figure Description

[0074] The preferred embodiments of the digital twin processing method and digital twin system of this application will now be described with reference to the accompanying drawings. The drawings are as follows:

[0075] Figure 1 Example diagrams of application scenarios provided in the embodiments of this application;

[0076] Figure 2 This is a flowchart illustrating the digital twin processing method provided in this application;

[0077] Figure 3 This is a schematic diagram of the structure of the digital twin system provided in this application;

[0078] Figure 4 This is a schematic diagram illustrating the flow of the overall digital twin data provided in this application;

[0079] Figure 5 This is a network communication diagram of the Andon subsystem provided in this application;

[0080] Figure 6 This is a schematic diagram of the structure of the digital twin processing device provided in this application;

[0081] Figure 7 This is a schematic diagram of the structure of the digital twin management platform provided in this application. Detailed Implementation

[0082] First, those skilled in the art should understand that these embodiments are merely for explaining the technical principles of this application and are not intended to limit the scope of protection of this application. Those skilled in the art can make adjustments as needed to adapt to specific application scenarios.

[0083] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms "a" and "the" as used in the embodiments of this application are also intended to include the plural forms unless the context clearly indicates otherwise.

[0084] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can be represented as: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0085] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0086] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a product or system comprising a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a product or system. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the product or system that includes that element.

[0087] With the continuous development of technology, the industrial sector is gradually moving towards intelligent and digital transformation, thereby improving the efficiency of industrial equipment research and development, manufacturing, and maintenance. Digital twins are currently a major research direction in major industrial fields. By building digital twin display systems in production workshops, the digitalization and informatization of production workshops can be promoted, data resources can be integrated, data access and output standards can be unified, and digital technology can be used as a core driving force for cost reduction and efficiency improvement on production lines.

[0088] As a digital representation of real-world objects or systems, digital twins in the industrial sector focus on building digital twins for smart factories, smart workshops, and smart equipment to ensure production. In industrial digital twin systems, relying on the sensing and feedback control pathways of the virtual-physical interaction module, precise mapping, interactive fusion, and intelligent feedback control between physical and virtual entities are achieved based on real data from industrial production activities and feedback control commands from intelligent applications. Simultaneously, the functions of both physical and digital spaces can be continuously improved and upgraded during production operations.

[0089] Current digital twin technology solutions, based on the Industrial Internet, are primarily used for product research and development, design, and manufacturing. They struggle to cover the entire lifecycle of industrial equipment and cannot address issues at each stage, including design, manufacturing, commissioning, operation, and maintenance. Therefore, the lifecycle coverage of digital twins for industrial products needs improvement.

[0090] Therefore, in response to the problem that the coverage of digital twins for the life cycle of industrial products still needs to be improved in the above-mentioned methods, the inventors discovered in their 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, the corresponding mechanism model can be combined to generate the corresponding digital twin, thereby improving the coverage of the life cycle of industrial products.

[0091] Specifically, the digital twin processing method includes: acquiring equipment data of the target device at each stage of the industrial equipment lifecycle in the IoT management platform, and acquiring the corresponding mechanism model of the target device. The equipment data is then converted into action data corresponding to the mechanism model. A digital twin of the target device is generated based on the mechanism model and action data. Finally, the digital twin is displayed.

[0092] The digital twin processing method of this application acquires equipment data of the target equipment at each stage of the industrial equipment lifecycle in the IoT management platform, as well as the corresponding mechanism model of the target equipment. Based on the mechanism model and equipment data, a corresponding data twin can be generated. Furthermore, the digital twin can be displayed to provide a basis for subsequent optimization at each stage of the equipment lifecycle. Moreover, because it covers all stages of the lifecycle, it improves the lifecycle coverage of the digital twin for industrial products.

[0093] Based on the above-mentioned inventive discovery, the inventor has proposed the technical solution of this application.

[0094] Figure 1 The diagram illustrates application scenarios provided in the embodiments of this application. For example... Figure 1 As shown, 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 through production line n in the diagram. The digital twin management platform 10 may include a twin display module, a twin management module, and a twin data processing module, while the IoT management platform 30 may include an edge management module and an IoT module. In this embodiment, the IoT management platform 30 can collect equipment data from the industrial equipment production line to obtain equipment data at each stage of the industrial equipment's lifecycle. The IoT management platform 30 can also obtain equipment data by connecting to other management platforms, such as a production information management platform.

[0095] The IoT management platform 30 can store mechanism models or acquire them from other platforms, such as industrial mechanism platforms. Each mechanism model is matched to a specific industrial device; a higher degree of matching results in a better-looking digital twin. In this application scenario, during the construction of the digital twin, the IoT management platform 30 collects real-time equipment data from each production line in the industrial equipment production line 50. This data may include equipment data from production lines at various stages of the lifecycle, such as R&D, manufacturing, and maintenance. The IoT management platform 30 then sends the collected equipment data and corresponding mechanism models to the digital twin management platform 10. The digital twin management platform 10 converts the equipment data into action data corresponding to the mechanism models and generates a digital twin of the target device based on the mechanism models and action data. Simultaneously, the digital twin management platform 10 displays the digital twin.

[0096] During the process of displaying the digital twin on the digital twin management platform 10, the IoT management platform 30 continues to collect equipment data at each stage of the industrial equipment's lifecycle. After receiving subsequent equipment data, the digital twin management platform 10 will update and iterate the generated digital twin to more effectively reflect the lifecycle development status of the corresponding industrial equipment. The displayed digital twin can provide a foundation for subsequent optimization of the industrial equipment production line structure.

[0097] The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0098] Figure 2 This is a flowchart illustrating the digital twin processing method provided in this application. Figure 2 As shown, in this embodiment, the executing entity of this invention is a digital twin processing device, which can be integrated into a digital twin management platform. The digital twin management platform may include multiple electronic devices. The method includes:

[0099] S101. Obtain the equipment data of the target equipment in each stage of the industrial equipment lifecycle in the IoT management platform, and obtain the corresponding mechanism model of the target equipment.

[0100] The life cycle includes stages such as R&D, manufacturing, maintenance, and upgrades and optimizations, covering different levels of the enterprise's production and manufacturing process from the equipment level, production line level to the workshop level and factory level, and running through all aspects of production and manufacturing, including design, process management and optimization, resource allocation, parameter adjustment, quality management and traceability, energy efficiency management, and production scheduling.

[0101] Equipment data for the target device can include both static and dynamic data. Static data includes the device's appearance and structure, while dynamic data can include operational data and energy consumption. Acquiring equipment data for the target device at each stage of the industrial equipment lifecycle can improve the coverage of subsequent digital twins across the industrial equipment lifecycle.

[0102] The IoT management platform can be built upon the Business as a Service (BaaS) layer and the IoT layer of the Industrial Internet. For example, the platform architecture of the IoT management platform can be developed using the BaaS layer developer platform, specifically based on the Spring Boot framework. Furthermore, in this embodiment, all platforms, including the digital twin management platform, can be built on the BaaS layer engine, thereby organically combining technical solutions with business logic and best practices.

[0103] BaaS engines emphasize business logic and best practices, serving as cloud service platforms for rapidly building digital twin applications. BaaS capabilities include:

[0104] The "Mechanism Model Platform" primarily provides enterprise equipment models and equipment mechanism management, including a library of thousands of mechanism models.

[0105] The "Knowledge Graph Service" provides management of knowledge content related to equipment failure and maintenance.

[0106] The "Identifier Resolution Platform" records the traceability information of equipment and product spare parts in a one-item-one-code manner, effectively solving the problem of spare parts management for enterprises.

[0107] The "Industrial Big Data Platform" provides real-time access to massive amounts of data.

[0108] The "artificial intelligence platform" enhances enterprises' intelligent capabilities through data and algorithm models.

[0109] The IoT management platform can obtain the equipment data of the target equipment in each stage of the industrial equipment lifecycle by collecting equipment data corresponding to the production line at each stage of the industrial equipment lifecycle, and can also collect partial equipment data through the connected production information management system.

[0110] The mechanism model can be obtained from the industrial mechanism platform at the BaaS layer, or it can be obtained first from the industrial mechanism platform by the IoT management platform, and then from the IoT management platform. The higher the degree of matching between the mechanism model and the industrial equipment, the better it can simulate the life cycle process of the industrial equipment, thereby improving the accuracy of the digital twin.

[0111] S102. Convert the equipment data into action data corresponding to the mechanism model.

[0112] Since the equipment data is static parameter data, it needs to be converted into motion data corresponding to the mechanism model in order to simulate the target equipment. For example, if the equipment data is a rotational speed of 300 revolutions per second, it needs to be converted into corresponding rotational motion data.

[0113] S103. Generate a digital twin of the target device based on the mechanism model and motion data.

[0114] By fusing mechanistic models and motion data, a digital twin of the target device can be constructed and generated.

[0115] S104. Demonstrate the digital twin.

[0116] Digital twins can be displayed across all scenarios, allowing users to easily view the digital twin of each device and better understand changes throughout the device's lifecycle. Furthermore, displaying digital twins enables comprehensive device status monitoring and provides a better reference basis for subsequent digital twin iterations and device updates.

[0117] In this embodiment, by acquiring equipment data of the target device at each stage of the industrial equipment lifecycle in the IoT management platform and obtaining the corresponding mechanism model of the target device, a corresponding data twin can be generated based on the mechanism model and equipment data. Simultaneously, the digital twin can be displayed to provide a basis for subsequent optimization at each stage of the equipment lifecycle. Furthermore, because it covers all stages of the lifecycle, the lifecycle coverage of the digital twin for industrial products is improved.

[0118] Furthermore, based on the digital twin processing method provided in the previous embodiment of the present invention, the solution of the present invention can be further refined. Therefore, the digital twin processing method provided in this embodiment includes the following steps.

[0119] S201. Obtain equipment data of the target equipment in each stage of the industrial equipment lifecycle in the IoT management platform.

[0120] The implementation of step 201 is similar to that of step 101 in the previous embodiment of the present invention, and will not be described in detail here.

[0121] S202. Obtain the corresponding mechanism model from the industrial mechanism platform based on the equipment identifier corresponding to the target equipment.

[0122] Each device has a pre-defined device identifier, which can be stored and managed in the BaaS layer's identifier resolution platform. Simultaneously, the industrial mechanism platform stores the mechanism models corresponding to each industrial device, and these models can be configured.

[0123] S203. Convert the equipment data into corresponding analog data.

[0124] Since equipment data alone can only reflect the operating status of the equipment, such as speed and frequency, it cannot intuitively show the equipment's actions. Therefore, the equipment data can be converted into corresponding analog data first, providing a foundation for subsequent conversion into motion data. Converting equipment data into analog data can be done using commonly used analog data generators.

[0125] S204. Convert the simulation data into action data of the target device's corresponding mechanism model.

[0126] After converting device data into analog data, it has the foundation to be converted into motion data. The motion data conversion engine can be used to convert the analog data into motion data corresponding to the target device's mechanism model.

[0127] S205. The motion data is fused with the parameters of each model in the mechanism model to generate a digital twin of the target device.

[0128] By fusing motion data with the mechanistic model, the mechanistic model can be transformed from a static state to a dynamic state. At the same time, based on the motion data, the mechanistic model can simulate the operation of the target device, thereby generating a digital twin of the target device.

[0129] Optionally, this embodiment can also combine the target device's service data for analysis and display. Specifically:

[0130] Obtain business data corresponding to the target device collected by the IoT management platform.

[0131] Transform business data into corresponding business messages.

[0132] The business messages are converted into corresponding message display data in the digital twin for presentation.

[0133] The IoT management platform can collect business data corresponding to the target device, such as data from a BaaS layer big data platform. This business data can include information such as the target device's price and sales date.

[0134] By converting business messages into corresponding message display data in a digital twin, the digital twin can synchronously display the corresponding message display data, thus providing a foundation for users to optimize business-related stages of industrial equipment in the future.

[0135] Optionally, the digital twin management platform includes: a data computing component. Device data is stored in an IoT database by the IoT management platform. Business data is stored in a business database by the IoT management platform. The IoT database and the business database can use Oracle (a relational database), SQL Server (Structured Query Language), MySQL (a relational database), TDengine high-performance time-series database, etc.

[0136] Before converting device data into corresponding analog data, the following steps are also included:

[0137] Device data corresponding to the target device is obtained from the IoT database through the WebSocket communication protocol.

[0138] Transforming business data into corresponding business messages, including:

[0139] Read business data from the business database.

[0140] The business data is statistically processed based on the preset business requirements data to generate business messages.

[0141] In this embodiment, the WebSocket communication protocol can improve the interaction efficiency between the digital twin management platform and the database. Simultaneously, a WebSocket data service can be built based on the WebSocket communication protocol to construct a complete process from acquiring device data to converting it into usable data.

[0142] Pre-defined business requirements can be set in advance according to actual application scenarios, such as transportation optimization or production line process optimization. This allows for the statistical analysis of business data based on the pre-defined requirements, thereby generating corresponding business messages.

[0143] S206. Demonstrate the digital twin.

[0144] The implementation of step 206 is similar to that of step 104 in the previous embodiment of the present invention, and will not be described in detail here.

[0145] Optionally, in this embodiment, the digital twin management platform is connected to the Andon subsystem. The Andon subsystem connects all aspects of manual anomaly handling through information technology, further improving anomaly handling efficiency and providing a foundation for optimizing the entire process at each stage of the industrial equipment lifecycle.

[0146] The method also includes:

[0147] Retrieve production line anomaly warning reports sent by the Andon subsystem. These reports are generated by the Andon subsystem when it detects anomalies in industrial equipment on various production lines.

[0148] The corresponding production line anomaly warning reports will be displayed.

[0149] Production line anomaly early warning reports can be generated based on anomaly information triggered by anomaly triggering terminals in the Andon subsystem. The Andon subsystem includes multiple anomaly triggering terminals, which can be triggered by users to identify anomalies on the production line. When a user discovers an anomaly, they can trigger the anomaly through the anomaly triggering terminal, thereby enabling the Andon subsystem to generate production line anomaly early warning reports.

[0150] When the digital twin management platform receives a production line anomaly warning report, it can display it uniformly in conjunction with the digital twin. For example, if the anomaly warning report is related to a specific industrial equipment, it can be displayed synchronously with the corresponding digital twin of that industrial equipment. Simultaneously, the Andon subsystem can also upload the anomaly warning report to the client managing anomalies to notify managers to handle the anomaly. If the anomaly can be resolved without manual intervention, the Andon subsystem can also determine anomaly resolution strategies from a pre-set database based on the anomaly information, and then resolve the corresponding anomaly according to those strategies.

[0151] Figure 3 This is a schematic diagram of the structure of the digital twin system provided in this application. (Example:) Figure 3 As shown, in this embodiment, a digital twin system is also provided, the digital twin system 100 including:

[0152] The IoT management platform 30 and the digital twin management platform 10, as described in any of the above embodiments, are interconnected. The digital twin management platform 10 can be coded in JAVA (a software language) using the Spring Boot framework, and can interact with each digital twin rendering device to synchronize device layout, device data configuration, device combination, and other information.

[0153] The IoT management platform 30 is used for edge management, production line data collection, business data collection, and IoT data management.

[0154] Optionally, in this embodiment, the IoT management platform 30 includes an edge management sub-platform and an IoT sub-platform that are interconnected. The edge management sub-platform is communicatively connected to the production information management system.

[0155] 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 lifecycle, as well as equipment data of target equipment in the production information management system.

[0156] 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.

[0157] Optionally, in this embodiment, the digital twin system 100 further includes an Andon subsystem. The Andon subsystem is communicatively connected to the digital twin management platform 10.

[0158] The Andon subsystem includes: an exception triggering terminal and a processing server.

[0159] The exception triggering terminal is used to generate corresponding exception information based on the exception command issued by the user, and send the exception information to the processing server.

[0160] The processing server is used to generate corresponding production line anomaly warning reports based on the anomaly information, and then send the production line anomaly warning reports to the digital twin management platform.

[0161] To better understand the digital twin system of this embodiment, the following will be combined with... Figure 4 Let me explain in detail. Figure 4 In this system, the digital twin system comprises a digital twin management platform, an IoT management platform, a business database (referred to as the business database in the diagram), an IoT database (referred to as the IoT database cluster in the diagram), and an Andon subsystem (only the Andon system backend is shown in the diagram, not the full structure). 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 processing the digital twin and synchronizing parameter configurations for displaying digital twin content across all scenarios, enabling real-time display across all scenarios. The Andon subsystem is responsible for production line anomaly alarms and handling.

[0162] 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 an edge management sub-platform (as shown in the diagram), comprising edge management and edge agents (where edge agents refer to edge physical devices). It also includes an IoT sub-platform (the IoT platform shown in the diagram) and a reverse control agent module. The production information management system is the MES (Manufacturing Execution System) shown in the diagram. The business database uses Oracle, SQL Server, and MySQL databases. The IoT database can utilize ZooKeeper, a distributed application coordination service software, for distributed database management, supporting distributed locks and cluster member election.

[0163] The IoT management platform can directly collect data from production line equipment and connect to the MES system to obtain equipment data from the MES system. Simultaneously, after collecting the equipment data, it synchronizes the data to the IoT sub-platform using the MQTT (Message Queuing Telemetry Transport) protocol, where the IoT sub-platform stores the equipment data in its IoT database. It also provides a reverse control agent to support future reverse control implementation on the production line.

[0164] Data from the IoT database is transmitted via the WebSocket protocol through the motion service module in the digital twin management platform. The simulation data generator converts the device data into simulated data, and the real-time motion conversion engine converts the simulated data into corresponding motion data.

[0165] In this embodiment, the business data is stored in a business database. A data computing component extracts the business data from the database, performs statistical calculations, and generates report data. This report data can be written to an IoT database, as shown in the figure, and then transformed into a digital twin report display content through data services and RESTful (Representational State Transfer) services. Alternatively, the report data can be directly transformed into digital twin report display content by data services and RESTful services.

[0166] Meanwhile, when the Andon system backend detects a problem on the production line, it can send the production line anomaly warning report (referred to as the report in the figure) to the data service of the digital twin management platform. The data service and RESTful service will then transform the report data into digital twin report display content.

[0167] Figure 5 This is a network communication diagram of the Andon subsystem provided in this application, such as... Figure 5 As shown, in order to better understand the Andon subsystem of this embodiment, a detailed description will be given below in conjunction with the accompanying drawings.

[0168] The mini-programs in electronic devices connect to the Andon server's interface standards and data interfaces. The mini-program's "reporting / response" is forwarded to the Nginx server, which acts as a reverse proxy. The Nginx server requires HTTPS (Hypertext Transfer Protocol over SecureSocket Layer) to establish a network connection with the mini-program on the electronic device and performs rate limiting. The Nginx server forwards the mini-program's content to the server-side. The server-side communicates with the front-end, the IoT sub-platform (i.e., the IoT platform in the diagram), the message queue, and the database, all within the internal network. Note: All access to the server-side interfaces requires authentication and rate limiting is supported to ensure service stability.

[0169] The IoT platform can receive abnormal triggers from IoT devices and thus issue automatic alarms.

[0170] The front end refers to front-end electronic devices, such as mobile devices, which can convert triggered abnormal information into production line abnormality early warning reports and display them on a large screen. Alternatively, these reports can be sent to the digital twin management platform. The IoT platform, front end, and server communicate with each other via the HTTP (Hypertext Transfer Protocol) protocol.

[0171] The server can manage and process abnormal alarms, abnormal messages, and data stored in the database.

[0172] The Andon subsystem is a system for notifying management, maintenance, and other workers of quality or process issues. Functionally, the Andon subsystem can be mainly divided into two parts:

[0173] (1) Anomaly triggering terminal, which adopts mobile terminal and supports deployment on multiple terminals such as iOS and Android, is mainly used for manual triggering, response, forwarding, anomaly resolution, and reconnection.

[0174] (2) Processing the server, mainly for basic information management, parameter configuration, analysis reports, etc.

[0175] The Andon subsystem is mainly used to solve the problem of anomaly handling and to connect all aspects of manual anomaly handling with information technology. The Andon subsystem includes manual anomaly reporting, anomaly handling, automatic escalation reporting of anomaly timeout, and setting of anomaly handling personnel hierarchy.

[0176] The Andon subsystem revolves around a core processing logic while also addressing common exception handling branches, ensuring a smooth closed-loop experience when exceptions are handled manually and resolved through the Andon system.

[0177] The Andon subsystem's front-end primarily serves users via mobile devices, employing a lightweight and simplified design. The back-end features robust processing flows and logic, as well as configuration of the personnel organizational structure. The Andon subsystem's mobile app can be developed using DingTalk mini-programs. The management back-end is handled on a computer, including parameter configuration for exception handling processes, personnel hierarchy, and delayed reporting time control, while also providing data retrieval and query functions.

[0178] Figure 6 This is a schematic diagram of the structure of the digital twin processing device provided in this application. Figure 6 As shown, the digital twin processing device 200 is located in the digital twin management platform, and the digital twin processing device 200 includes:

[0179] 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 to acquire the corresponding mechanism model of the target equipment.

[0180] The conversion module 202 is used to convert device data into action data corresponding to the mechanism model.

[0181] The generation module 203 is used to generate a digital twin of the target device based on the mechanism model and motion data.

[0182] Display module 204 is used to display digital twins.

[0183] Figure 6 The provided digital twin processing device can execute the aforementioned corresponding method embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

[0184] Meanwhile, the digital twin processing device provided by the present invention is further refined based on the digital twin processing device provided in the previous embodiment, resulting in the digital twin processing device of this embodiment.

[0185] In one possible implementation, the conversion module 202 is specifically used for:

[0186] The equipment data is converted into corresponding simulation data. The simulation data is then converted into action data for the target equipment's corresponding mechanism model.

[0187] In one possible implementation, the digital twin management platform communicates with the industrial mechanism platform.

[0188] When acquiring the mechanism model corresponding to the target device, module 201 is specifically used for:

[0189] The corresponding mechanism model is obtained from the industrial mechanism platform based on the equipment identifier of the target equipment.

[0190] In one possible implementation, the generation module 203 is specifically used for:

[0191] The motion data is fused with the parameters of each model in the mechanistic model to generate a digital twin of the target device.

[0192] In one possible implementation, the digital twin processing device 200 further includes:

[0193] The business processing module is used to acquire business data corresponding to the target devices collected by the IoT management platform. It then converts the business data into corresponding business messages. Finally, it converts the business messages into digital twin-based message display data for presentation.

[0194] In one possible implementation, the digital twin management platform includes: a data computing component; device data stored in an IoT database by the IoT management platform; and business data stored in a business database by the IoT management platform.

[0195] Module 201 is also used for:

[0196] Device data corresponding to the target device is obtained from the IoT database through the WebSocket communication protocol.

[0197] When the business processing module converts business data into corresponding business messages, it is specifically used for:

[0198] Read business data from the business database. Perform statistical processing on the business data based on preset business requirements to generate business messages.

[0199] In one possible implementation, the digital twin management platform communicates with the Andon subsystem.

[0200] The digital twin processing device 200 also includes:

[0201] The anomaly display module is used to retrieve production line anomaly warning reports sent by the Andon subsystem. These reports are generated when the Andon subsystem detects anomalies in industrial equipment on various production lines. The module displays these production line anomaly warning reports accordingly.

[0202] The digital twin processing device in this embodiment can execute the aforementioned corresponding method embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

[0203] Figure 7 A schematic diagram of the structure of the digital twin management platform provided in this application. Figure 7As shown, the digital twin management platform includes a processor 301 and a memory 302. The memory 302 stores a computer program. 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.

[0204] In the aforementioned digital twin management platform, the processor 301 and the memory 302 are electrically connected directly or indirectly to enable data transmission or interaction. For example, these components can be electrically connected to each other via one or more communication buses or signal lines, such as a bus connection. The memory 302 stores computer-executable instructions that implement data access control methods, including at least one software functional module that can be stored in the memory 302 in the form of software or firmware. The processor 301 executes various functional applications and data processing by running the software programs and modules stored in the memory 302.

[0205] The memory 302 may be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. Furthermore, the software programs and modules within the aforementioned 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 an operating environment for other software components.

[0206] Processor 301 can be an integrated circuit chip with signal processing capabilities. The aforementioned processor 301 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor.

[0207] One embodiment of this application also provides a chip, including a processor and a memory. The memory stores a computer program, and when the processor executes the computer program stored in the memory, it implements the steps of the digital twin processing method described in the above method embodiments.

[0208] An embodiment of this application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the steps of the digital twin processing method in the above-described method embodiments.

[0209] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0210] The technical solutions of this application have been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. Without departing from the principles of this application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of this application.

Claims

1. A digital twin processing method, characterized in that, Applied to a digital twin management platform, the digital twin management platform includes: a data computing component, and the method includes: Acquire device data of target devices at each stage of the industrial equipment lifecycle in the IoT management platform, wherein the device data is stored in the IoT database by the IoT management platform; The device data corresponding to the target device is obtained from the IoT database through the WebSocket communication protocol; The device data is converted into corresponding simulation data, and the simulation data is converted into action data corresponding to the action of the target device corresponding to the mechanism model of the target device. Based on the aforementioned mechanism model and the aforementioned action data, a digital twin covering each stage of the target device's lifecycle is generated. The digital twin is then displayed; The IoT management platform acquires business data corresponding to the target device, and the business data is stored in the business database by the IoT management platform. Read the business data from the business database, perform statistical processing on the business data according to preset business requirement data, and generate business messages; The business message is converted into message display data corresponding to the digital twin for display.

2. The digital twin processing method according to claim 1, characterized in that, The digital twin management platform is communicatively connected to the industrial mechanism platform; Obtain the mechanism model corresponding to the target device, including: The corresponding mechanism model is obtained from the industrial mechanism platform based on the equipment identifier of the target equipment.

3. The digital twin processing method according to claim 1, characterized in that, The step of generating a digital twin of the target device based on the mechanism model and the action data includes: The motion data is fused with the parameters of each model in the mechanism model to generate a digital twin of the target device.

4. The digital twin processing method according to any one of claims 1 to 3, characterized in that, The digital twin management platform is communicatively connected to the Andon subsystem; The method further includes: Retrieve production line anomaly warning reports sent by the Andon subsystem; The production line anomaly early warning report is generated by the Andon subsystem when it detects an anomaly in the industrial equipment of each production line. The corresponding production line anomaly early warning report will be displayed.

5. A digital twin system, characterized in that, include: The IoT management platform and the digital twin management platform are interconnected and the digital twin management platform is used to execute the digital twin processing method according to any one of claims 1 to 4.

6. The digital twin system according to claim 5, characterized in that, The IoT management platform includes an edge management sub-platform and an IoT sub-platform that are interconnected; the edge management sub-platform is interconnected 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, as well as equipment data of 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.

7. The digital twin system according to claim 5 or 6, characterized in that, Also includes: Andon subsystem; The Andon subsystem is communicatively connected to the digital twin management platform; The Andon subsystem includes: an abnormal triggering terminal and a processing server; The exception triggering terminal is used to generate corresponding exception information according to the exception command issued by the user, and send the exception information to the processing server; The processing server is used to generate a corresponding production line anomaly early warning report based on the anomaly information, and send the production line anomaly early warning report to the digital twin management platform.

8. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-4.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-4.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, is used to implement the method of any one of claims 1-4.

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