Digital twin data mapping synchronization method, apparatus, device, medium, and product

Abstract

The application provides a digital twin data mapping synchronization method, device, equipment, medium and product. The method comprises: obtaining original state data of a target industrial entity from a plurality of heterogeneous data sources; standardizing the original state data to obtain standard state data; mapping the standard state data through a preset static mapping rule and an intelligent learning algorithm to obtain attribute data of a corresponding three-dimensional model; generating a mapping relationship table according to the standard state data and the attribute data of the corresponding three-dimensional model; sending the attribute data to a digital twin platform to enable the digital twin platform to map the attribute data to the three-dimensional model to obtain a digital twin model; monitoring real-time state data of the target industrial entity in the plurality of heterogeneous data sources in real time to obtain changed data; and synchronizing the changed data to the digital twin platform according to the mapping relationship table. The accuracy of data mapping is improved, thereby improving the efficiency of data synchronization.

Description

Technical Field

[0001] This application relates to the field of data synchronization technology, and in particular to a digital twin data mapping synchronization method, apparatus, device, medium and product. Background Technology

[0002] Against the backdrop of the rapid development of intelligent manufacturing, digital twin technology has become a core tool in the industrial field for achieving equipment monitoring, production optimization, and predictive maintenance.

[0003] Currently, digital twin platforms typically acquire data from physical devices or systems and then associate it with model attributes through static mapping rules to drive model updates.

[0004] However, the low applicability of static mapping rules in existing technologies leads to insufficient mapping accuracy and low data synchronization efficiency. Summary of the Invention

[0005] This application provides a method, apparatus, device, medium, and product for digital twin data mapping and synchronization, in order to improve the accuracy of data mapping and the efficiency of data synchronization.

[0006] Firstly, this application provides a digital twin data mapping synchronization method, including:

[0007] Obtain raw state data of the target industrial entity from multiple heterogeneous data sources;

[0008] The original state data is standardized to obtain standard state data;

[0009] The standard state data is mapped using preset static mapping rules and intelligent learning algorithms to obtain the attribute data of the corresponding three-dimensional model. The three-dimensional model is constructed by the digital twin platform based on the geometric structure data of the target industrial entity. The attribute data includes static attribute data and dynamic attribute data.

[0010] A mapping table is generated based on the standard state data and the corresponding attribute data of the three-dimensional model, wherein the mapping table includes the correspondence between the state data of the target industrial entity and the attribute data of the three-dimensional model;

[0011] The attribute data is sent to the digital twin platform so that the digital twin platform maps the attribute data to the three-dimensional model to obtain a digital twin model;

[0012] Real-time monitoring of the real-time status data of the target industrial entity from the multiple heterogeneous data sources is used to obtain change data;

[0013] The changed data is synchronized to the digital twin platform according to the mapping table, so that the digital twin platform updates the digital twin model.

[0014] In one possible implementation, the step of mapping the standard state data using a preset static mapping rule and an intelligent learning algorithm to obtain the corresponding attribute data of the 3D model includes: mapping the standard state data according to the preset static mapping rule to obtain basic mapping attribute data; obtaining standard state data with an empty mapping result; using an intelligent learning algorithm to perform supplementary mapping processing on the standard state data with an empty mapping result to obtain supplementary mapping attribute data; and obtaining the attribute data of the 3D model based on the basic mapping attribute data and the supplementary mapping attribute data.

[0015] In one possible implementation, before using an intelligent learning algorithm to perform supplementary mapping processing on the standard state data whose mapping result is empty to obtain supplementary mapping attribute data, the method further includes: obtaining a mapping relationship pair between historical standard state data and the attribute data of the three-dimensional model from a preset distributed file system; obtaining a preset initial machine learning algorithm; and training the initial machine learning algorithm using the mapping relationship pair to obtain an intelligent learning algorithm.

[0016] In one possible implementation, obtaining the attribute data of the 3D model based on the basic mapping attribute data and the supplementary mapping attribute data includes: identifying sensitive data in the basic mapping attribute data and the supplementary mapping attribute data; adding desensitization tags to the sensitive data according to a preset desensitization rule; and generating the attribute data of the 3D model based on the basic mapping attribute data and the supplementary mapping attribute data containing the desensitization tags.

[0017] In one possible implementation, sending the attribute data to the digital twin platform includes: parsing the attribute data to obtain a de-identification tag; performing de-identification processing on the attribute data according to the de-identification tag to obtain de-identified attribute data; and sending the de-identified attribute data to the digital twin platform.

[0018] In one possible implementation, synchronizing the changed data to the digital twin platform according to the mapping table, so that the digital twin platform updates the digital twin model, includes: mapping the changed data according to the mapping table to obtain corresponding changed attribute data; encrypting the changed attribute data according to a preset encryption algorithm to obtain encrypted data; establishing a bidirectional communication channel with the digital twin platform using a preset communication network protocol; and sending the encrypted data to the digital twin platform through the bidirectional communication channel, so that the digital twin platform decrypts the encrypted data to obtain the changed attribute data, and updates the digital twin model according to the changed attribute data.

[0019] In one possible implementation, the standardization process of the original state data to obtain standard state data includes: cleaning the original state data to obtain first state data; converting the first state data according to a preset data format and data unit to obtain second state data; and standardizing the second state data according to a preset data standard to obtain standard state data.

[0020] Secondly, this application provides a digital twin data mapping synchronization device, comprising:

[0021] The acquisition module is used to acquire raw state data of the target industrial entity from multiple heterogeneous data sources;

[0022] The first processing module is used to standardize the original state data to obtain standard state data.

[0023] The second processing module is used to map the standard state data through preset static mapping rules and intelligent learning algorithms to obtain the attribute data of the corresponding three-dimensional model, wherein the three-dimensional model is constructed by the digital twin platform based on the geometric structure data of the target industrial entity, and the attribute data includes static attribute data and dynamic attribute data.

[0024] The generation module is used to generate a mapping relationship table based on the standard state data and the corresponding attribute data of the three-dimensional model, wherein the mapping relationship table includes the correspondence between the state data of the target industrial entity and the attribute data of the three-dimensional model;

[0025] A sending module is used to send the attribute data to the digital twin platform, so that the digital twin platform maps the attribute data to the three-dimensional model to obtain a digital twin model;

[0026] The monitoring module is used to monitor the real-time status data of the target industrial entity in the multiple heterogeneous data sources in order to obtain change data;

[0027] The synchronization module is used to synchronize the changed data to the digital twin platform according to the mapping relationship table, so that the digital twin platform can update the digital twin model.

[0028] Thirdly, this application provides a digital twin data mapping synchronization device, including: a memory and a processor;

[0029] The memory stores computer-executed instructions;

[0030] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0031] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible embodiments of the first aspect.

[0032] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0033] The digital twin data mapping and synchronization method, apparatus, equipment, medium, and product provided in this application acquire the original state data of the target industrial entity through multiple heterogeneous data sources, realize data sharing across multiple platforms, and improve the accuracy of data mapping by combining static mapping rules with intelligent learning algorithms for data mapping processing, thereby improving the efficiency of data synchronization. Attached Figure Description

[0034] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0035] Figure 1 A schematic diagram illustrating a scenario for the digital twin data mapping and synchronization method provided in this application embodiment;

[0036] Figure 2 A flowchart illustrating a digital twin data mapping synchronization method provided in one embodiment of this application;

[0037] Figure 3 A schematic diagram of the data synchronization architecture of the industrial digital infrastructure provided in this application embodiment;

[0038] Figure 4 A schematic diagram of data flow provided for an embodiment of this application;

[0039] Figure 5 A schematic diagram of the structure of the digital twin data mapping synchronization device provided in the embodiments of this application;

[0040] Figure 6 This is a schematic diagram of the structure of a digital twin data mapping and synchronization device provided in an embodiment of this application.

[0041] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0042] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0043] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with relevant laws, regulations and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0044] Currently, digital twin platforms typically acquire data from physical devices or systems and then associate it with model attributes through static mapping rules to drive model updates. However, existing static mapping rules have poor applicability, resulting in insufficient mapping accuracy and low data synchronization efficiency.

[0045] The digital twin data mapping and synchronization method provided in this application integrates heterogeneous data sources to achieve multi-platform data sharing. It uses a combination of static mapping rules and intelligent learning algorithms for data mapping processing, which improves the accuracy of data mapping and thus improves the efficiency of data synchronization.

[0046] Figure 1 This is a schematic diagram illustrating a scenario of the digital twin data mapping and synchronization method provided in an embodiment of this application. Figure 1 As shown, the specific application scenarios of this application include: service device 101, digital twin platform 102, and heterogeneous data source 103.

[0047] The service device 101 can be a server. Optionally, it can be a single server or a cluster of multiple servers.

[0048] The number of heterogeneous data sources 103 can be multiple.

[0049] Specifically, the service device 101 obtains the original state data of the target industrial entity from multiple heterogeneous data sources 103; the service device 101 performs mapping processing on the original state data to obtain the attribute data of the corresponding three-dimensional model; the service device 101 sends the attribute data of the three-dimensional model to the digital twin platform 102 so that the digital twin platform 102 maps the attribute data to the three-dimensional model to obtain the digital twin model.

[0050] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0051] Figure 2 This is a flowchart illustrating a digital twin data mapping synchronization method provided in one embodiment of this application. The execution entity of this embodiment can be... Figure 1 The service device 101 shown can also be other computer devices, and this embodiment does not impose any particular limitations on it. Figure 2 As shown, the method includes:

[0052] S201: Obtain the raw state data of the target industrial entity from multiple heterogeneous data sources.

[0053] Optionally, heterogeneous data sources may include the following: IoT platforms, Manufacturing Execution Systems (MES), access control systems, and security systems.

[0054] Optionally, the raw status data may include one or more of the following: equipment status, production parameters, personnel flow, and safety monitoring.

[0055] S202: Standardize the raw state data to obtain standard state data.

[0056] Optionally, the original state data is standardized to obtain standard state data, including: cleaning the original state data to obtain first state data; converting the first state data according to a preset data format and data unit to obtain second state data; and standardizing the second state data according to a preset data standard to obtain standard state data.

[0057] Through format conversion and standardization, compatibility and adaptation of communication protocols across multiple heterogeneous platforms were achieved. This significantly improved the universality of data mapping and synchronization, as well as system integration efficiency.

[0058] Optionally, the original state data is cleaned to obtain the first state data, including: using open-source extraction, transformation, load (ETL) tools to clean the collected data to remove duplicate records and erroneous data to obtain the first state data.

[0059] Optionally, the default data format can be JSON (JavaScript Object Notation) or Extensible Markup Language (XML).

[0060] S203: Standard state data is mapped using preset static mapping rules and intelligent learning algorithms to obtain the attribute data of the corresponding three-dimensional model. The three-dimensional model is constructed by the digital twin platform based on the geometric structure data of the target industrial entity. The attribute data includes static attribute data and dynamic attribute data.

[0061] Optionally, static attribute data includes: name, description, attribution, and instance number, etc.; dynamic attribute data includes: position, rotation angle, scaling, and size, etc.

[0062] Optionally, the standard state data is mapped using preset static mapping rules and intelligent learning algorithms to obtain the corresponding attribute data of the 3D model. This includes: mapping the standard state data according to preset static mapping rules to obtain basic mapping attribute data; obtaining standard state data with empty mapping results; using intelligent learning algorithms to perform supplementary mapping processing on the standard state data with empty mapping results to obtain supplementary mapping attribute data; and obtaining the attribute data of the 3D model based on the basic mapping attribute data and the supplementary mapping attribute data.

[0063] By combining static mapping rules with intelligent learning algorithms, the accuracy and adaptability of mapping are significantly improved. This enhances the flexibility and accuracy of cross-platform data mapping.

[0064] Optionally, in one embodiment of this application, before using an intelligent learning algorithm to perform supplementary mapping processing on the standard state data with empty mapping results to obtain supplementary mapping attribute data, the method further includes: obtaining a mapping relationship pair between historical standard state data and attribute data of the three-dimensional model from a preset distributed file system; obtaining a preset initial machine learning algorithm; and training the initial machine learning algorithm using the mapping relationship pair to obtain an intelligent learning algorithm.

[0065] Optionally, the preset initial machine learning algorithm can be a neural network algorithm or a decision tree algorithm.

[0066] By training machine learning algorithms with historical data, they can autonomously learn and discover complex, non-linear correspondences between data and model attributes, reducing reliance on manually predefined static mapping rules. They can handle new data patterns or device types not covered by static mapping rules, improving the completeness of data mapping and the system's adaptability to different operating conditions.

[0067] S204: Generate a mapping table based on the standard state data and the corresponding attribute data of the 3D model. The mapping table includes the correspondence between the state data of the target industrial entity and the attribute data of the 3D model.

[0068] Optionally, a mapping table is generated based on the standard state data and the corresponding attribute data of the 3D model, including: associating and structurally encapsulating the standard state data and the corresponding attribute data of the 3D model item by item to establish a unique correspondence between the state data fields and the model attribute fields; validating and deduplicating the correspondence to obtain a standardized correspondence; and solidifying the standardized correspondence to obtain a mapping table containing a bidirectional correspondence between the state data of the target industrial entity and the attribute data of the 3D model.

[0069] By solidifying the data correspondence obtained from the mapping process into a unified mapping table, a stable and unique matching benchmark is provided for the construction of the digital twin model and subsequent data synchronization; repeated mapping calculations are avoided, improving the consistency and reliability of data mapping and model-driven processes; and the structured correspondence enables the standardized binding of multi-source state data and 3D model attributes, ensuring the accuracy and traceability of the synchronization between the digital twin model and the physical entity state.

[0070] S205: Send attribute data to the digital twin platform so that the digital twin platform can map the attribute data to the 3D model to obtain the digital twin model.

[0071] S206: Real-time monitoring of the real-time status data of target industrial entities from multiple heterogeneous data sources to obtain change data.

[0072] Optionally, real-time monitoring of the real-time status data of the target industrial entity in multiple heterogeneous data sources to obtain change data includes: real-time monitoring of the real-time status data of the target industrial entity in multiple heterogeneous data sources through triggers in multiple heterogeneous data sources to obtain change data.

[0073] S207: Synchronize the changed data to the digital twin platform according to the mapping relationship table so that the digital twin platform can update the digital twin model.

[0074] Optionally, synchronizing the changed data to the digital twin platform according to the mapping relationship table includes: mapping the changed data according to the mapping relationship table to obtain the corresponding changed attribute data; encrypting the changed attribute data according to a preset encryption algorithm to obtain encrypted data; establishing a two-way communication channel with the digital twin platform using a preset communication network protocol; sending the encrypted data to the digital twin platform through the two-way communication channel so that the digital twin platform can decrypt the encrypted data to obtain the changed attribute data, and update the digital twin model according to the changed attribute data.

[0075] By utilizing a bidirectional communication channel with end-to-end encryption, the real-time performance and security of data synchronization are significantly improved. This ensures immediate consistency between the digital twin model and the physical device status, enhancing the real-time response capability to abnormal events in industrial scenarios.

[0076] Optionally, in one embodiment of this application, after mapping the changed data according to the mapping relationship table to obtain the corresponding changed attribute data, the method further includes: if there is changed data with an empty mapping result, then a preset static mapping rule and an intelligent learning algorithm are used to map the changed data to obtain the corresponding changed attribute data.

[0077] The digital twin data mapping and synchronization method provided in this application embodiment achieves multi-platform data sharing by obtaining the original state data of the target industrial entity from multiple heterogeneous data sources. By combining static mapping rules with intelligent learning algorithms for data mapping processing, the accuracy of data mapping is improved, thereby improving the efficiency of data synchronization.

[0078] In one embodiment of this application, based on the above embodiments, the step "obtaining the attribute data of the 3D model according to the basic mapping attribute data and the supplementary mapping attribute data" is also provided in another way, which is detailed below:

[0079] Identify sensitive data in the basic mapping attribute data and supplementary mapping attribute data; add desensitization tags to the sensitive data according to preset desensitization rules; generate attribute data of the 3D model based on the basic mapping attribute data and supplementary mapping attribute data containing desensitization tags.

[0080] Accordingly, step S205 also provides another implementation method, detailed below:

[0081] Parse the attribute data to obtain the desensitization marker; perform desensitization processing on the attribute data according to the desensitization marker to obtain desensitized attribute data; send the desensitized attribute data to the digital twin platform.

[0082] Optionally, identifying sensitive data in the basic mapping attribute data and supplementary mapping attribute data includes: scanning and analyzing the basic mapping attribute data and supplementary mapping attribute data according to preset sensitive feature definitions to identify sensitive data. The digital twin data mapping synchronization method provided in this application improves data security by marking sensitive data and performing anonymization processing on it.

[0083] In one embodiment of this application, a digital twin data mapping and synchronization system is provided, which includes: a data acquisition layer, a data processing layer, a data mapping layer, a data synchronization layer, and a data security module.

[0084] Optionally, the data acquisition layer is the data input end of the entire system, used to collect raw status data in real time from multiple heterogeneous data sources, such as various external platforms or systems. The raw status data includes, but is not limited to, key information such as equipment status, production parameters, personnel flow and safety monitoring, providing raw status data sources for subsequent data mapping and synchronization.

[0085] Optionally, a data acquisition layer is used to acquire data from multiple different data sources, including IoT devices, enterprise information systems, and security monitoring systems. It supports various industrial communication protocols and data formats to adapt to the communication needs of different devices.

[0086] Optionally, the data acquisition layer performs preliminary data cleaning on the raw state data before it is transmitted to the data processing layer to remove invalid or erroneous data points. This reduces the burden on subsequent processing.

[0087] Optionally, the data acquisition layer establishes a network connection with an external platform or system and possesses corresponding data acquisition permissions and an application programming interface (API). Furthermore, sufficient computing resources are required to handle concurrent data streams.

[0088] Optionally, the data acquisition layer can employ hardware integration, software integration, and data acquisition agents for data acquisition. Specifically, this includes: hardware integration: using hardware devices such as industrial gateways to directly connect to sensors and controllers for real-time data acquisition; software integration: developing or integrating application programming interfaces (APIs) to interface with external system databases or data interfaces via software, enabling automatic data acquisition; and data acquisition agents: deploying data acquisition agents at the data source end to communicate with the data source and forward data to the data processing layer.

[0089] Optionally, the data mapping configuration data for the API interface in the data acquisition layer includes: number, description, project, server address, port, path, and protocol, etc.

[0090] Optionally, a data processing layer receives raw state data from the data acquisition layer and further cleans, transforms, and standardizes it to ensure data quality and consistency, providing accurate and usable data for data mapping. Specifically, this includes: data cleaning: removing duplicate data, correcting obvious errors, filling in missing values, and ensuring data integrity and accuracy; data transformation: converting data of different formats and units into a unified format and unit to facilitate subsequent processing and mapping; and data standardization: ensuring data meets the requirements of the digital twin platform through standardization, improving data usability and consistency.

[0091] Optionally, the data processing layer possesses powerful data processing capabilities, typically involving databases and data processing software such as ETL tools. Furthermore, sufficient storage resources are available for temporary storage of processed data.

[0092] Optionally, the data processing layer cleans, transforms, and standardizes the raw state data, including: using ETL tools to extract, transform, and load data; using preset data transformation scripts to perform customized transformation processing on the data; and using machine learning models to intelligently process the data, such as anomaly detection and predictive imputation.

[0093] Optionally, the data mapping layer is a crucial step in achieving a one-to-one mapping between data and the 3D model. It matches the processed data with the attributes of the 3D model in the digital twin platform, achieving precise data mapping and ensuring that the digital twin model accurately reflects the state of the physical entity.

[0094] Optionally, the data mapping layer automatically maps data to corresponding 3D model attributes using predefined mapping rules or intelligent learning algorithms. It responds to data changes by updating the state of the 3D model in real time to reflect the latest state of the physical world. Mapping errors are detected during the mapping process, and corresponding processing methods are used to handle these errors, ensuring data accuracy.

[0095] Optionally, the data mapping layer includes a mapping engine, which includes a static mapping rule base and an intelligent learning algorithm.

[0096] Optionally, a static mapping rule base is used to store predefined static mapping rules to guide the matching of data with model attributes.

[0097] Optionally, the intelligent learning algorithm can be a neural network algorithm or a decision tree algorithm; it automatically discovers the mapping relationship between data and model attributes through machine learning techniques.

[0098] Optionally, the mapping engine can handle complex data relationships and attribute mappings, and update the mapping results to the digital twin platform in real time.

[0099] Optionally, a data synchronization layer is used to synchronize the mapped attribute data to the digital twin platform in real time, ensuring that the digital twin model remains synchronized with the physical world and providing users with real-time data views and analysis.

[0100] Optionally, the data synchronization layer supports real-time data updates to reflect the latest changes in the physical world. It can store historical data, supporting data traceability and analysis. It ensures data consistency in multi-user and multi-device environments.

[0101] Optionally, the operating conditions for the data synchronization layer include: a high-speed network connection and a stable data storage system to support real-time data synchronization and storage. In addition, sufficient computing resources are required to handle concurrent data synchronization requests.

[0102] Optionally, the data synchronization layer can be implemented by: establishing real-time bidirectional communication between the system and the digital twin platform using the WebSocket protocol; using triggers at the database level across multiple heterogeneous data sources to automatically synchronize and update the data to the digital twin platform when changes occur; using a distributed file system to store large amounts of historical data and support rapid data access and synchronization; and using a time-series database to store and synchronize time-series data, such as sensor data, to support real-time monitoring and analysis.

[0103] Optionally, the data synchronization layer receives the synchronization results from the digital twin platform and feeds the synchronization results back to the data acquisition layer.

[0104] Optionally, the data security module is a core component of digital twin data mapping and synchronization, used to ensure data security throughout the entire data lifecycle, including data acquisition, data transmission, data storage, data processing, and data display, to prevent data leakage, tampering, and unauthorized access.

[0105] Optionally, the data security module can cover the entire data lifecycle, providing security protection at every stage from data collection to display. It complies with data protection regulations and industry standards. It can adapt to different data types and business scenarios, providing customized security strategies. It can respond quickly to security incidents, minimizing the impact of data breaches.

[0106] Optionally, the data security module includes predefined data security policies and compliance requirements. Data security is achieved through technologies such as encryption, access control, and audit logs.

[0107] Optionally, the data security module operation steps include: encrypting data during transmission or in static storage using preset strong encryption standards; employing role-based access control policies and preset least privilege principles to screen authorized users for access to sensitive data; anonymizing sensitive data; deploying a security information and event management system to monitor data access and operation behavior in real time to detect and respond to security threats; recording detailed logs of data access and operations for post-event auditing and investigation; and pre-setting an emergency response plan for responding to data breaches and other security incidents.

[0108] The digital twin data mapping and synchronization system provided in this application includes a data acquisition layer, a data processing layer, a data mapping layer, a data synchronization layer, and a data security module. It can implement the digital twin data mapping and synchronization method in the above embodiments. By obtaining the original state data of the target industrial entity from multiple heterogeneous data sources, it realizes data sharing across multiple platforms. By combining static mapping rules with intelligent learning algorithms for data mapping processing, it improves the accuracy of data mapping, thereby improving the efficiency of data synchronization.

[0109] One embodiment of this application provides the relationship between the layers in the above-described digital twin data mapping synchronization system, as detailed below:

[0110] Optionally, the relationship between the data acquisition layer and the data processing layer includes:

[0111] Data Flow: The data acquisition layer interfaces with external systems or platforms, responsible for real-time data acquisition and transmitting the raw state data to the data processing layer. The data processing layer receives the raw state data from the data acquisition layer and performs further processing.

[0112] Dependency: The data processing layer's operation depends on the quality and integrity of the data provided by the data acquisition layer. If the data acquisition layer fails to acquire data correctly or data is lost, the data processing layer will be unable to perform its functions.

[0113] Feedback Mechanism: When feedback conditions are met, the data processing layer may send a feedback signal to the data acquisition layer, indicating that certain data needs to be re-acquired or acquisition parameters need to be adjusted. Feedback conditions include: insufficient completeness of the original state data or detection of a new device, etc.

[0114] Optionally, the relationship between the data processing layer and the data mapping layer includes:

[0115] Data transformation: After cleaning, transforming and standardizing the collected raw state data, the data processing layer passes the processed data to the data mapping layer.

[0116] Mapping Basis: The data mapping layer performs data mapping based on the standardized data provided by the data processing layer. If the data processing layer fails to process the data correctly, it will affect the accuracy of the data mapping.

[0117] Real-time updates: The data processing layer can process data quickly to ensure that the data mapping layer can update the attribute data of the 3D model in real time.

[0118] Optionally, the relationship between the data mapping layer and the data synchronization layer includes:

[0119] Mapping result transmission: The data mapping layer maps the processed data to the 3D model attribute data and transmits the mapping result to the data synchronization layer.

[0120] Synchronization Requirements: The data synchronization layer needs to synchronize data to the digital twin platform based on the mapping results provided by the data mapping layer. The efficiency of the data synchronization layer directly affects the real-time performance and accuracy of the digital twin model.

[0121] Consistency maintenance: The data synchronization layer is used to maintain data consistency in the digital twin platform and ensure that the mapping results of the data mapping layer can be accurately reflected in the digital twin model.

[0122] Optionally, the relationship between the data synchronization layer and the data acquisition layer includes:

[0123] Loop feedback: The data synchronization layer feeds back the synchronization results to the data acquisition layer so that adjustments can be made during the data acquisition process. For example, if the data synchronization layer detects data loss or errors, it will re-trigger the data acquisition layer to re-acquire data.

[0124] System stability: The stability and efficiency of the data synchronization layer are crucial to the entire system, directly affecting the output quality of the data acquisition layer and the overall performance of the system.

[0125] Optionally, the relationship between the data security module and other layers includes:

[0126] The data acquisition layer, data processing layer, data mapping layer, and data synchronization layer all rely on the data security module to protect data. The data security module provides other layers with security services such as encryption, access control, and data anonymization to ensure data security at every stage. The monitoring and auditing functions of the data security module provide other layers with the ability to detect and respond to security incidents.

[0127] In one embodiment of this application, based on the above embodiments, the digital twin data mapping synchronization system further includes cross-layer collaboration, as detailed below:

[0128] Overall coordination: The system also includes a central coordinator to manage data flow and workflow between layers, ensuring smooth data transfer between layers and enabling them to work collaboratively.

[0129] Exception handling: There is an exception handling mechanism between each layer, so that when an error or exception occurs in any layer, other layers can be notified in a timely manner for adjustment or remediation.

[0130] Performance monitoring: The system also includes performance monitoring tools to monitor the operating status and performance indicators of each layer, ensuring the efficiency and stability of the entire data mapping and synchronization process.

[0131] In one embodiment of this application, a process for implementing a digital twin data mapping synchronization method using a digital twin data mapping synchronization system is provided. Taking a smart manufacturing factory as an example, this factory uses a digital twin platform to monitor and manage its production lines. The factory's MES system, IoT platform, access control system, and security system jointly provide data to the digital twin platform. Details are as follows:

[0132] Step 1: The operational steps of the data acquisition layer include: IoT platform data acquisition: Deploying industrial gateways to connect to sensors and controllers to collect real-time data such as equipment status, temperature, and pressure. MES system data acquisition: Obtaining production orders, production progress, and quality control data from the MES system via API interfaces. Access control system data acquisition: Using RFID technology to collect employee entry and exit records, including timestamps and employee IDs. Security system data acquisition: Obtaining video surveillance data through the security system's API interfaces for security analysis and incident response. Data security measures: Enabling end-to-end encryption during the data acquisition phase to ensure data security during transmission.

[0133] Optionally, industrial gateways support multiple communication protocols to adapt to different devices.

[0134] Step Two: The data processing layer's operational steps include: Data Cleaning: Using ETL tools to clean the collected data, removing duplicate records and obvious errors. Data Transformation: Converting data from different formats to a unified format and data from different units to a unified unit. Data Security Measures: Implementing strict access control during the data processing phase to ensure that only authorized data processing personnel can access sensitive data.

[0135] Step 3: The operation steps of the data mapping layer include: Static mapping rules: Defining mapping rules for each data type according to preset data rules to obtain static mapping rules. For example, mapping equipment status data to the corresponding parts of the 3D model, or mapping production progress data to the 3D model of the production line. Intelligent learning algorithm application: Employing machine learning algorithms, such as decision trees, to automatically learn and optimize the mapping relationship between data and 3D model attributes. Mapping engine: Constructing a mapping engine that includes static mapping rules and intelligent learning algorithms, capable of handling complex data relationships and attribute mappings, and updating the mapping results to the digital twin platform in real time. Data security measures: During the data mapping process, sensitive data is marked for de-identification processing in subsequent steps.

[0136] Step Four: The operation steps of the data synchronization layer include: Real-time data synchronization: Using the WebSocket protocol to achieve real-time bidirectional communication between the system and the digital twin platform to realize real-time data synchronization. Database triggers: Using triggers at the database level of multiple heterogeneous data sources to automatically synchronize and update to the digital twin platform when data changes. Time series database application: Using a time series database to store and synchronize time series data, such as sensor data, to support real-time monitoring and analysis. Data security measures: During the data synchronization process, ensuring that all synchronized data is encrypted, and that only authorized users and systems can receive the synchronized data.

[0137] In one embodiment of this application, the application scenario is as follows: Suppose a critical production equipment in a factory fails, the following are the detailed steps of the digital twin data mapping synchronization method:

[0138] The sensors on the IoT platform detect device malfunctions, acquire the raw status data of the malfunctioning device, and send the raw status data to the industrial gateway.

[0139] After receiving the raw status data sent by the industrial gateway, the ETL tool cleans and transforms the data format to obtain standard status data, which is then stored in the database.

[0140] The mapping engine maps standard state data onto the corresponding 3D model components in the digital twin platform according to preset static mapping rules.

[0141] The digital twin platform receives the mapped data and updates the status of the 3D model in real time via the WebSocket protocol, displaying equipment faults so that factory managers can monitor equipment faults through the digital twin platform, use the platform's analysis tools to diagnose faults, and formulate maintenance plans.

[0142] Figure 3A schematic diagram of the data synchronization architecture of the industrial digital infrastructure provided in this application embodiment is shown below. Figure 3 As shown, the industrial digital infrastructure system includes the physical world, the first layer, the second layer, the third layer, and the fourth layer.

[0143] The physical world includes: factory areas, production lines, and equipment. The physical world corresponds to the modeling / twin construction of the digital twin platform, providing the platform with basic information about the physical entities.

[0144] The first layer can be the equipment layer, which includes automated production lines and sensors. The equipment layer is used for the data acquisition and access steps of the digital twin data mapping and synchronization system, and is responsible for collecting raw operational data from physical devices.

[0145] The second layer can be the data acquisition layer, which includes the Supervisory Control and Data Acquisition (SCADA) system, Computer Numerical Control (CNC), and database. The data acquisition layer corresponds to the establishment of data standards for the digital twin data mapping and synchronization system. Data standards are used to unify data formats, units, and encoding rules.

[0146] The third layer can be the Manufacturing Execution System, which includes: Warehouse Management System (WMS), Advanced Planning and Scheduling (APS), and Manufacturing Operations Management (MOM); and a corresponding business model for building the digital twin data mapping and synchronization system, used to extract business logic and static mapping rules.

[0147] The fourth layer can be the business layer, which includes: Strategic Enterprise Resource Planning (SERP), Product Lifecycle Management (PLM), and Supply Chain Management (SCM); and the corresponding business model for building the digital twin data mapping synchronization system, which is used to extract business logic and static mapping rules.

[0148] Figure 4 This is a schematic diagram of data flow provided for an embodiment of this application. For example... Figure 4As shown, the object platform includes an object model library, which includes models and attribute groups. The attribute groups include data types, data definitions, and data rules.

[0149] The data types, data definitions, and data rules in the Gewu platform are transformed and mapped to generate attribute group templates, behavior templates, data mappings, and artificial intelligence for the digital twin model of the digital twin platform.

[0150] Figure 5 This is a schematic diagram of the structure of the digital twin data mapping and synchronization device provided in the embodiments of this application, as shown below. Figure 5 As shown, the digital twin data mapping synchronization device provided in this embodiment includes: an acquisition module 501, a first processing module 502, a second processing module 503, a generation module 504, a sending module 505, a monitoring module 506, and a synchronization module 507.

[0151] The acquisition module 501 is used to acquire the raw state data of the target industrial entity from multiple heterogeneous data sources;

[0152] The first processing module 502 is used to standardize the raw state data to obtain standard state data;

[0153] The second processing module 503 is used to map standard state data through preset static mapping rules and intelligent learning algorithms to obtain the attribute data of the corresponding three-dimensional model. The three-dimensional model is constructed by the digital twin platform based on the geometric structure data of the target industrial entity. The attribute data includes static attribute data and dynamic attribute data.

[0154] The generation module 504 is used to generate a mapping relationship table based on the standard state data and the corresponding attribute data of the three-dimensional model. The mapping relationship table includes the correspondence between the state data of the target industrial entity and the attribute data of the three-dimensional model.

[0155] The sending module 505 is used to send attribute data to the digital twin platform so that the digital twin platform can map the attribute data to the three-dimensional model to obtain the digital twin model;

[0156] The monitoring module 506 is used to monitor the real-time status data of the target industrial entity in multiple heterogeneous data sources in order to obtain change data;

[0157] The synchronization module 507 is used to synchronize changed data to the digital twin platform according to the mapping relationship table, so that the digital twin platform can update the digital twin model.

[0158] In one possible implementation, the second processing module 503 is specifically used for: mapping standard state data according to a preset static mapping rule to obtain basic mapping attribute data; obtaining standard state data with empty mapping results; using an intelligent learning algorithm to perform supplementary mapping processing on the standard state data with empty mapping results to obtain supplementary mapping attribute data; and obtaining attribute data of the three-dimensional model based on the basic mapping attribute data and the supplementary mapping attribute data.

[0159] In one possible implementation, the second processing module 503 is further specifically used to: obtain a mapping relationship pair between historical standard state data and attribute data of a 3D model from a preset distributed file system; obtain a preset initial machine learning algorithm; and train the initial machine learning algorithm using the mapping relationship pair to obtain an intelligent learning algorithm.

[0160] In one possible implementation, the second processing module 503, when obtaining the attribute data of the 3D model based on the basic mapping attribute data and the supplementary mapping attribute data, is specifically used to: identify sensitive data in the basic mapping attribute data and the supplementary mapping attribute data; add desensitization tags to the sensitive data according to preset desensitization rules; and generate the attribute data of the 3D model based on the basic mapping attribute data and the supplementary mapping attribute data containing the desensitization tags.

[0161] In one possible implementation, the sending module 505 is specifically used to: parse the attribute data to obtain the desensitization mark; perform desensitization processing on the attribute data according to the desensitization mark to obtain desensitized attribute data; and send the desensitized attribute data to the digital twin platform.

[0162] In one possible implementation, the synchronization module 507 is specifically used for: mapping the changed data according to the mapping relationship table to obtain the corresponding changed attribute data; encrypting the changed attribute data according to a preset encryption algorithm to obtain encrypted data; establishing a two-way communication channel with the digital twin platform using a preset communication network protocol; sending the encrypted data to the digital twin platform through the two-way communication channel so that the digital twin platform can decrypt the encrypted data to obtain the changed attribute data, and update the digital twin model according to the changed attribute data.

[0163] In one possible implementation, the first processing module 502 is specifically used for: cleaning the original state data to obtain first state data; converting the first state data according to a preset data format and data unit to obtain second state data; and standardizing the second state data according to a preset data standard to obtain standard state data.

[0164] The digital twin data mapping synchronization device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0165] Figure 6 This is a schematic diagram of the structure of a digital twin data mapping and synchronization device provided in an embodiment of this application. Figure 6 As shown, the digital twin data mapping synchronization device provided in this embodiment includes at least one processor 601 and a memory 602. Optionally, the device further includes a communication component 603. The processor 601, memory 602, and communication component 603 are connected via a bus 604.

[0166] In a specific implementation, at least one processor 601 executes computer execution instructions stored in memory 602, causing at least one processor 601 to perform the above-described method.

[0167] The specific implementation process of processor 601 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0168] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0169] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0170] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0171] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0172] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0173] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0174] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0175] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0176] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0177] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0178] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0179] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0180] Finally, it should be noted that other embodiments of this application will readily conceive of by those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and alterations may be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A digital twin data mapping and synchronization method, characterized in that, include: Obtain raw state data of the target industrial entity from multiple heterogeneous data sources; The original state data is standardized to obtain standard state data; The standard state data is mapped using preset static mapping rules and intelligent learning algorithms to obtain the attribute data of the corresponding three-dimensional model. The three-dimensional model is constructed by the digital twin platform based on the geometric structure data of the target industrial entity. The attribute data includes static attribute data and dynamic attribute data. A mapping table is generated based on the standard state data and the corresponding attribute data of the three-dimensional model, wherein the mapping table includes the correspondence between the state data of the target industrial entity and the attribute data of the three-dimensional model; The attribute data is sent to the digital twin platform so that the digital twin platform maps the attribute data to the three-dimensional model to obtain a digital twin model; Real-time monitoring of the real-time status data of the target industrial entity from the multiple heterogeneous data sources is used to obtain change data; The changed data is synchronized to the digital twin platform according to the mapping table, so that the digital twin platform updates the digital twin model.

2. The method according to claim 1, characterized in that, The process of mapping the standard state data using preset static mapping rules and intelligent learning algorithms to obtain the corresponding attribute data of the 3D model includes: The standard state data is mapped according to a preset static mapping rule to obtain basic mapping attribute data; Retrieve standard state data where the mapping result is empty; An intelligent learning algorithm is used to perform supplementary mapping processing on the standard state data whose mapping result is empty, so as to obtain supplementary mapping attribute data; The attribute data of the 3D model is obtained based on the basic mapping attribute data and the supplementary mapping attribute data.

3. The method according to claim 2, characterized in that, Before employing an intelligent learning algorithm to perform supplementary mapping processing on the standard state data where the mapping result is empty, in order to obtain supplementary mapping attribute data, the method further includes: Obtain the mapping relationship between historical standard state data and attribute data of the 3D model from the preset distributed file system; Obtain the preset initial machine learning algorithm; The initial machine learning algorithm is trained using the mapping relationship to obtain an intelligent learning algorithm.

4. The method according to claim 2, characterized in that, The step of obtaining the attribute data of the 3D model based on the basic mapping attribute data and the supplementary mapping attribute data includes: Identify sensitive data in the basic mapping attribute data and the supplementary mapping attribute data; According to the preset desensitization rules, desensitization tags are added to the sensitive data; Based on the base mapping attribute data and supplementary mapping attribute data containing the desensitized markers, the attribute data of the three-dimensional model is generated.

5. The method according to claim 4, characterized in that, Sending the attribute data to the digital twin platform includes: Parse the attribute data to obtain the desensitization tag; The attribute data is desensitized according to the desensitization marker to obtain desensitized attribute data; Send the de-identified attribute data to the digital twin platform.

6. The method according to any one of claims 1 to 5, characterized in that, The step of synchronizing the changed data to the digital twin platform according to the mapping table, so that the digital twin platform updates the digital twin model, includes: The changed data is mapped according to the mapping relationship table to obtain the corresponding changed attribute data; The changed attribute data is encrypted according to a preset encryption algorithm to obtain encrypted data; A two-way communication channel is established with the digital twin platform using a preset communication network protocol; The encrypted data is sent to the digital twin platform through the bidirectional communication channel, so that the digital twin platform can decrypt the encrypted data to obtain the changed attribute data and update the digital twin model according to the changed attribute data.

7. The method according to any one of claims 1 to 5, characterized in that, The standardization process for the original state data to obtain standard state data includes: The original state data is cleaned to obtain the first state data; The first state data is converted according to a preset data format and data unit to obtain the second state data; The second state data is standardized using a preset data standard to obtain standard state data.

8. A digital twin data mapping and synchronization device, characterized in that, include: The acquisition module is used to acquire raw state data of the target industrial entity from multiple heterogeneous data sources; The first processing module is used to standardize the original state data to obtain standard state data. The second processing module is used to map the standard state data through preset static mapping rules and intelligent learning algorithms to obtain the attribute data of the corresponding three-dimensional model, wherein the three-dimensional model is constructed by the digital twin platform based on the geometric structure data of the target industrial entity, and the attribute data includes static attribute data and dynamic attribute data. The generation module is used to generate a mapping relationship table based on the standard state data and the corresponding attribute data of the three-dimensional model, wherein the mapping relationship table includes the correspondence between the state data of the target industrial entity and the attribute data of the three-dimensional model; A sending module is used to send the attribute data to the digital twin platform, so that the digital twin platform maps the attribute data to the three-dimensional model to obtain a digital twin model; The monitoring module is used to monitor the real-time status data of the target industrial entity in the multiple heterogeneous data sources in order to obtain change data; The synchronization module is used to synchronize the changed data to the digital twin platform according to the mapping relationship table, so that the digital twin platform can update the digital twin model.

9. A digital twin data mapping and synchronization device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-7.

10. 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 1-7.

11. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-7.

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