Digital twin architecture for satellite-ground collaborative edge network

Abstract

The invention discloses a digital twin architecture for a satellite-ground collaborative edge network, and belongs to the field of network simulation and intelligent management in the satellite internet. According to the digital twin architecture of the satellite-ground collaborative edge network provided by the invention, the operation state, the resource state and the association state of terminal equipment such as an unmanned aerial vehicle and edge equipment such as a satellite are returned through a sliced feeder line, and a digital twin model integrating a data layer, a model layer and a decision layer is established at a gateway station side. Based on the architecture, a satellite-ground collaborative model training task is oriented, and model training and aggregation are realized by utilizing a block chain technology. According to the architecture, full-life-cycle management of the satellite-ground collaborative edge network is realized, through real-time simulation and optimization decision, the digital and intelligent level of the satellite network is improved, the simulation precision, real-time performance and resource utilization efficiency of the network are improved, and efficient and stable operation of the satellite network in a complex dynamic environment is ensured.

Description

Technical Field

[0001] This invention is applied to the field of satellite internet, particularly the application of digital twin (DT) technology. By utilizing digital twin technology to construct a virtual simulation environment highly consistent with the actual satellite-ground collaborative network, the operating status of the satellite network can be monitored and simulated in real time, and intelligent resource allocation decisions can be made. This improves the digitalization and intelligence level of the satellite network, and enhances the simulation accuracy, real-time performance, and resource utilization efficiency of the network. Background Technology

[0002] With the rapid development of satellite communication technology, satellite-ground collaborative edge networks have gradually become an important means of achieving global communication coverage and efficient data transmission. Satellite-ground collaborative networks utilize the coordinated work of satellites and ground equipment to provide extensive network coverage and flexible data processing capabilities. However, traditional network simulation techniques mainly rely on static models and offline calculations, making it difficult to reflect the dynamic changes and complex behaviors of the network in real time. This method cannot fully capture the complex dynamic characteristics of the network in different time and spatial dimensions, leading to significant deviations between simulation results and actual network conditions. Furthermore, traditional simulation techniques also have significant shortcomings in optimization decision-making, lacking the ability to process and respond to real-time data, and are unable to effectively cope with complex and ever-changing network environments and resource management needs, resulting in low network resource utilization efficiency.

[0003] To address the challenges of 5G networks, digital twin networks, which integrate with technologies such as the Internet of Things (IoT), cloud computing, and artificial intelligence (AI), have emerged. Digital twin networks connect physical systems to their digital models through real-time data acquisition, model building, and intelligent decision-making, achieving high interconnectivity and intelligent management. Unlike traditional network simulators, digital twin networks can monitor and analyze the state and behavior of physical entities, and perform real-time simulation and optimization through digital models, providing intelligent support for decision-making.

[0004] Digital twin technology has wide applications in industrial manufacturing, smart cities, energy management, and aerospace, improving efficiency, optimizing resource utilization, and enhancing decision-making. For example, in industrial manufacturing, digital twins are used for equipment condition monitoring, production process optimization, and predictive maintenance; in smart cities, they are used for traffic management, energy management, and environmental monitoring, improving urban operational efficiency and residents' quality of life; in energy management, they are used for equipment monitoring and fault diagnosis, optimizing energy allocation; and in aerospace, they are used for spacecraft and aircraft simulation and optimization, ensuring flight safety and performance optimization. These applications significantly improve operational efficiency and resource utilization in various fields. Therefore, introducing digital twin technology into satellite networks can dynamically simulate changes in satellite network nodes and links, complex spatiotemporal behaviors, and different types of services, thereby enabling full lifecycle management of satellite networks, optimizing satellite network configuration, improving the digitalization and intelligence level of satellites, and solving the challenges of remote health monitoring, condition assessment, and maintenance.

[0005] This invention integrates digital twin technology into the simulation and optimization of satellite-ground collaborative edge networks. By constructing a virtual simulation environment that closely mirrors the actual network, it reflects the dynamic changes of the physical network in real time. Combined with artificial intelligence algorithms, it performs network status analysis and resource optimization decisions, enabling full lifecycle management of satellite networks. It provides precise simulation support and optimization decisions at every stage, from design, deployment, operation to maintenance, significantly improving the digitalization and intelligence level of satellite networks. Summary of the Invention

[0006] This invention proposes a digital twin architecture for satellite-ground collaborative edge networks, aiming to overcome the limitations of traditional network simulation techniques in simulating dynamic changes and complex behaviors. A digital twin model of the satellite-ground collaborative edge network is designed, transmitting the operational, resource, and association states of terminal devices such as UAVs and edge devices such as satellites back via sliced ​​feeder lines. An integrated digital twin model of the data layer, model layer, and decision layer is established at the gateway station side to monitor the real-time network operation and make intelligent resource allocation decisions, achieving real-time interaction and resource optimization between physical and digital spaces. Through real-time simulation and optimization decision-making, the digitalization and intelligence level of the satellite network is improved, enhancing the network's simulation accuracy, real-time performance, and resource utilization efficiency.

[0007] (I) Problems to be solved

[0008] In the field of satellite internet, this invention addresses the shortcomings of traditional network simulation technologies, such as their inability to reflect dynamic network changes and complex behaviors in real time, and their insufficient optimization decision-making capabilities. It proposes a digital twin architecture for satellite-ground collaborative edge networks. Utilizing digital twin technology, it achieves accurate simulation and real-time optimization decision-making for the state of satellite-ground collaborative edge networks, significantly improving network simulation accuracy, real-time performance, and resource utilization efficiency, thereby enhancing the digitalization and intelligence level of satellite communication networks.

[0009] (II) Technical Solution Attached Figure Description

[0010] Figure 1 Digital Twin Architecture of Space-Ground Collaborative Edge Network

[0011] Figure 2 Global aggregation model based on blockchain technology

[0012] Figure 3 Digital Twin Network Functional Layered Architecture

[0013] Figure 4 Resource optimization strategies based on digital twin technology

[0014] like Figure 1 As shown, the digital twin architecture of the space-ground collaborative edge network includes physical space and digital space.

[0015] The physical space of the satellite-ground collaborative edge network digital twin architecture mainly comprises a terminal layer and an edge layer. The terminal layer consists of UAVs (Unmanned Aerial Vehicles), which collect task data, such as images and videos. Based on their association with satellites, UAVs transmit task data and their own digital twin data (such as computing resources, task data volume, number of channels, and transmission power) to associated satellites via sliced ​​satellite-ground links. The edge layer consists of satellites that provide edge computing services. For model training tasks, satellites need to complete two key tasks: edge computing and twin data synchronization. On one hand, the satellite processes the uploaded task data to complete the initial training of the local model. On the other hand, as a blockchain node, the satellite needs to propagate model parameters and perform cross-validation of blocks during the consensus process to complete the aggregation of the global model. This process is as follows: Figure 2 As shown. On the other hand, the satellite also transmits its own and UAVs' twin data to the gateway station to assist in the subsequent construction of digital twin models and resource allocation decisions, ensuring efficient use of resources and successful mission completion.

[0016] In the digital space of the space-ground collaborative edge network digital twin architecture, it is divided into three layers: from bottom to top, the digital twin data layer, the digital twin model layer, and the digital twin decision layer. The functional layered architecture of the digital twin network is as follows: Figure 3 As shown.

[0017] 1. Digital Twin Data Layer

[0018] The digital twin data layer receives real-time twin data from UAVs and satellites at the gateway station, including their operational status, resource status, and correlation status, and stores and manages this data. By acquiring data in physical space in real time, the digital twin data layer provides accurate foundational data for subsequent model and decision-making layers. The functions of the digital twin data layer can be divided into four aspects:

[0019] (1) Data Acquisition

[0020] Data acquisition completes the ETL (Extract, Transform, Load) process for network data, as well as cleaning and processing. First, key operational data from UAVs and satellites is extracted in real-time from the gateway station, including CPU utilization, communication link status, mission data volume, bandwidth utilization, transmission power, and the correlation status of UAVs and satellites, ensuring synchronization between physical equipment and the digital space. Second, data of different formats is standardized and converted before being loaded into a distributed storage system to ensure data consistency and compatibility. Finally, to guarantee data quality, the acquisition process includes data cleaning and processing to remove noise and outliers and fill in missing data, ensuring accuracy and completeness. The processing also involves format conversion and compression for easier subsequent storage and analysis.

[0021] (2) Data storage

[0022] The digital twin data layer employs multiple storage technologies to handle large-scale and diverse network data. For structured data (such as device status information), relational databases are used for storage; while for unstructured data (such as images and videos), a distributed file system is used. Simultaneously, to improve storage efficiency, the digital twin system categorizes data into "cold data" and "hot data." Hot data is stored in high-performance storage media to support real-time access, while cold data is stored in high-capacity, low-cost storage devices to conserve storage resources. This distributed storage architecture supports highly scalable storage for massive amounts of data, ensuring that the digital twin system maintains efficient storage capabilities even under a large influx of device data.

[0023] (3) Data services

[0024] In terms of data services, the digital twin data layer provides efficient data retrieval and processing capabilities for the model and decision-making layers. Through an optimized indexing mechanism, it offers rapid query and retrieval services for massive amounts of data, supporting fast multi-dimensional data access. Simultaneously, transaction management and locking mechanisms ensure data consistency and integrity are maintained even when multiple data service requests access concurrently. Furthermore, the data layer provides batch data processing capabilities and a unified interface, enabling different system modules to easily access data and perform operations such as task scheduling and resource optimization.

[0025] (4) Data Management

[0026] The digital twin data layer provides comprehensive data management capabilities, including data asset management, security management, quality management, and metadata management. Data asset management categorizes, labels, and archives collected data to ensure its traceability and manageability. Security management prevents data leakage and unauthorized access through encryption technologies and access controls, ensuring data security, especially when transmitting sensitive data. Quality management utilizes automated tools to check the accuracy, completeness, and consistency of data, and repairs or issues warnings for defective data. Simultaneously, metadata management generates detailed metadata for each data entry to support categorization, management, and rapid retrieval, improving data usability and management efficiency.

[0027] 2. Digital Twin Model Layer

[0028] The digital twin model layer constructs digital twin models of UAVs and satellites using twin data provided by the data layer. The UAV twin model includes operational status information such as flight altitude and position, resource status information such as computing and communication resources, and associated status information with the satellite. The satellite twin model includes operational status information such as orbit type and orbit parameters, resource status information such as computing, communication, and power resources, and twin model information of its associated UAVs. This status information collectively constitutes the digital twin model, providing data support for upper-level decision-making. The digital twin model layer primarily functions in three aspects:

[0029] (1) Model synchronization and real-time update

[0030] The core function of the digital twin model layer is to model the real-time status of UAVs and satellites and continuously update it through feedback from the digital twin data layer. The digital twin models of UAVs and satellites need to automatically adjust and update status parameters based on changes in actual physical space data to ensure that the model always remains synchronized with the actual situation of the physical equipment.

[0031] (2) Model Analysis and Optimization

[0032] To improve the accuracy and reliability of digital twin models, the virtual environment is validated and optimized. By comparing the digital twin model parameters with actual network operation data, deviations and deficiencies in the model are identified. Iterative optimization methods are then used to adjust model parameters and optimization algorithms, improving simulation accuracy and reliability and ensuring that the virtual simulation environment can reflect the dynamic changes and complex behaviors of the physical network in real time. Furthermore, with the dynamic changes in the physical network environment and the continuous input of new data, the digital twin model needs to be updated and maintained regularly to adapt to changes in the network environment and data updates, maintaining consistency between the simulation environment and the actual network.

[0033] (3) Model visualization

[0034] Network topology visualization: Using graphical visualization tools such as Graphviz or Gephi, an interactive network status display interface is generated to show the topology and node status of the satellite network. This interface intuitively presents the connections and communication links between satellites, UAVs, and gateway stations, displaying the real-time location, status, and performance metrics of each node. Users can view the operational status, signal strength, link quality, network resource utilization, and current task load of each node through the graphical interface, facilitating a comprehensive understanding of the overall network status and the operational status of specific nodes.

[0035] Real-time performance metric visualization: Integrated real-time monitoring tools display key network performance metrics. The interface uses dashboards and charts to show information such as bandwidth utilization, computing resource utilization, task processing latency, and link reliability. Users can set thresholds, and the system automatically detects abnormal states and alerts users via red alerts or pop-up windows. The status alarm function helps users promptly identify potential problems and bottlenecks in the network; users can click on alarm information to view detailed performance metrics and historical data records.

[0036] Dynamic Interactive Visualization: The network topology and data model of a digital twin network need to provide dynamic interactive functionality, supporting users to conduct in-depth analysis and exploration of the network status. This involves integrating various data visualization methods, such as bar charts, line charts, heatmaps, and statistical reports, using data analysis tools (e.g., Tableau, Power BI). Users can view detailed node information, link status, and historical data records through click and drag-and-drop operations. The interface supports parameter adjustment and optimization strategy configuration, allowing users to directly manage and control the network within the visual environment. Through interactive operations and data analysis tools, users can deeply analyze network performance and monitor the real-time operational status of the entire satellite-ground collaborative edge network.

[0037] 3. Digital Twin Decision-Making Level

[0038] The digital twin decision-making layer, based on the digital twin models of satellites and UAVs built in the digital twin model layer, optimizes the digital twin models using decision algorithms according to real-time mission status. The optimization results are transmitted to the satellites and UAVs via twin data slices from the gateway station, enabling command issuance. The digital twin decision-making layer mainly includes two aspects:

[0039] (1) Decision Algorithm

[0040] The digital twin decision-making layer can employ various optimization algorithms, such as deep reinforcement learning, to optimize multi-objective problems related to resource allocation and task offloading. Through continuous iteration, the decision-making layer can dynamically adjust its strategies to minimize costs such as latency and energy consumption during task execution. The decision-making layer constructs a multi-objective decision-making model that comprehensively considers multiple factors, including time constraints, energy consumption constraints, resource constraints, and relational constraints, ensuring that each decision meets the global requirements of the space-ground collaborative edge network.

[0041] (2) Instruction Issuance

[0042] The decision-making level generates specific instructions based on the optimization strategy, including operational instructions such as task unloading and resource allocation. Subsequently, these instructions are fed back to satellites and UAVs through twin data slices from the gateway station, ensuring that mission execution can be adjusted according to the latest optimization strategy.

[0043] Through real-time simulation and optimized decision-making, full lifecycle management of satellite networks is achieved. This includes all stages from network design, deployment, operation to maintenance. The digital twin model provides accurate simulation support during the network design phase, optimizing network structure and resource allocation. During network deployment and operation, the digital twin model is dynamically updated through real-time monitoring and data acquisition, reflecting the actual network and equipment status. Artificial intelligence algorithms are used for real-time optimization decisions, ensuring efficient utilization of network resources and optimal performance. During maintenance, simulation predicts potential problems and faults in the network, enabling preventative maintenance and reducing failure rates and maintenance costs. Throughout the process, a feedback mechanism continuously transmits actual operational data, constantly calibrating and optimizing the digital twin model, improving simulation accuracy and decision-making effectiveness. This achieves continuous optimization and efficient management of the satellite network, ensuring its long-term stable operation and performance optimization in complex and dynamic environments.

Claims

1. A digital twin architecture for satellite-ground collaborative edge networks, characterized by: A digital twin architecture for satellite-ground collaborative edge networks is proposed. The operating status, resource status, and association status of UAV terminal devices and satellite edge devices are transmitted back through sliced ​​feeder lines. A digital twin model integrating the digital twin data layer, digital twin model layer, and digital twin decision layer is established on the gateway side to monitor the real-time operating status of the network and make unloading decisions and resource allocation decisions. Aiming at the satellite-ground collaborative model training task, blockchain technology is used to realize the training and aggregation of digital twin models; In the digital twin data layer, the status data of terminal devices and edge devices are collected to provide basic data for the subsequent digital twin model layer and digital twin decision layer; In the digital twin model layer, digital twin models of UAVs and satellites are constructed based on the data in the digital twin data layer to provide data support for the decision-making layer. In the digital twin decision layer, the virtual models and data provided by the digital twin model layer are used to dynamically adjust bandwidth resource allocation, computing resource allocation, and task offloading strategies through deep reinforcement learning optimization algorithms to balance the latency and energy consumption of task execution in the physical network and maximize network resource utilization efficiency. The optimization decision is transmitted to satellites and UAVs through the twin data slices of the gateway station to implement the instruction issuance, and the status data of the actual network is transmitted back to the digital twin model through the feedback mechanism, and the digital twin model parameters are continuously calibrated and adjusted to improve the accuracy of the simulation results and the decision-making effect.

2. The digital twin architecture for satellite-ground collaborative edge network according to claim 1 is characterized in that: The digital twin architecture of the satellite-ground collaborative edge network includes physical and digital spaces; The physical space includes a terminal layer and an edge layer; the terminal layer is composed of UAVs equipment, and UAVs collect mission data; according to the association relationship with the satellite, UAVs transmit the mission data and its own digital twin data to the associated satellite through a sliced ​​satellite-to-ground link; the edge layer is composed of satellites and is used to provide edge computing services; In the digital space of the satellite-ground collaborative edge network digital twin architecture, there are three layers: from bottom to top, the digital twin data layer, the digital twin model layer, and the digital twin decision layer; The digital twin data layer receives real-time twin data from UAVs and satellites from the gateway, including their operating status, resource status, and association status, and stores and manages these twin data; by collecting data in the physical space in real time, the digital twin data layer provides basic data for the subsequent digital twin model layer and digital twin decision layer; The digital twin model layer constructs digital twin models of UAVs and satellites through the twin data provided by the digital twin data layer; the twin model of UAVs contains flight altitude and position operating status information, computing resources, communication resources status information, and associated status information with satellites; the twin model of satellites includes orbit type, orbit parameter operating status information, computing resources, communication resources, power resources status information, and twin model information of the UAVs associated with it; these status information together constitute the digital twin model, which provides data support for upper-level decision-making.

3. The digital twin architecture for satellite-ground collaborative edge network according to claim 2 is characterized in that: For the digital twin model training task, the satellite needs to complete two tasks: edge computing and twin data synchronization. The first task is that the satellite processes the uploaded mission data and completes the preliminary training of the local model. At the same time, as a blockchain node, the satellite needs to propagate the digital twin model parameters during the consensus process and cross-validate the blocks to complete the aggregation of the global digital twin model. The second task is that the satellite transmits its own and UAVs’ twin data to the gateway station to assist in the subsequent digital twin model construction and resource allocation decisions, ensuring resource utilization and smooth completion of the task.

4. The digital twin architecture for satellite-ground collaborative edge network according to claim 2 is characterized in that: The digital twin model layer functions include three aspects: (1) Digital twin model synchronization and real-time update; The core function of the digital twin model layer is to model the real-time status of UAVs and satellites and continuously update them through feedback from the digital twin data layer. The digital twin models of UAVs and satellites need to automatically adjust and update status parameters according to changes in actual physical space data to ensure that the models are always synchronized with the actual conditions of the physical devices. (2) Model analysis and optimization; In order to improve the accuracy and reliability of the digital twin model, the virtual environment is verified and optimized; by comparing the digital twin model parameters with the actual network operation data, the deviations and deficiencies in the digital twin model are identified, and the model parameters and optimization algorithms are adjusted using iterative optimization methods to improve the simulation accuracy and reliability, ensuring that the virtual simulation environment can reflect the dynamic changes and complex behaviors of the physical network in real time; with the dynamic changes of the physical network environment and the continuous input of new data, the digital twin model is regularly updated and maintained to cope with changes in the network environment and data updates, and to maintain the consistency between the simulation environment and the actual network; (3) Model visualization; Network topology visualization: Use Graphviz or Gephi graphical visualization tools to generate an interactive network status display interface to show the topology and node status of the satellite network; this interface can intuitively present the connection relationship and communication links between satellites, UAVs and gateways, and display the real-time location, status and performance indicators of each node; view the operation status, signal strength, link quality, network resource utilization and current task load of each node through the graphical interface; Real-time performance indicator visualization: integrated real-time monitoring tools display key performance indicators of the network; the interface displays bandwidth utilization, computing resource utilization, task processing delay and link reliability information through dashboards and charts; users set thresholds, automatically detect abnormal status and prompt users in the form of red alarms or pop-up windows on the interface; the status alarm function helps users to promptly discover potential problems and bottlenecks in the network, and users can view detailed performance indicators and historical data records by clicking on the alarm information; Dynamic interactive visualization: The network topology and data model of the digital twin network need to provide dynamic interactive functions to support users to conduct in-depth analysis and exploration of the network status; use data analysis tools to integrate multiple data visualization methods, including bar charts, line charts, heat maps and statistical reports; users can view detailed node information, link status and historical data records by clicking and dragging; the interface supports parameter adjustment and optimization strategy configuration, and users can directly manage and control the network in a visual environment; through interactive operations and data analysis tools, users can deeply analyze network performance and control the operating status of the entire satellite-ground collaborative edge network in real time.

5. The digital twin architecture for satellite-ground collaborative edge network according to claim 2 is characterized in that: The digital twin decision layer is based on the digital twin models of satellites and UAVs constructed in the digital twin model layer. It optimizes the digital twin models through decision algorithms according to the real-time mission status. The optimization results are transmitted to satellites and UAVs through twin data slices of the gateway station to implement command issuance. The functions of the digital twin decision layer include two aspects: (1) Decision-making algorithm; The digital twin decision layer uses a deep reinforcement learning algorithm to optimize the multi-objective problems of resource allocation and task offloading. Through continuous iteration, the decision layer dynamically adjusts the strategy to minimize delays and energy costs during task execution. The decision layer builds a multi-objective decision model, comprehensively considering multiple factors such as time constraints, energy constraints, resource constraints, and association constraints for task execution to ensure that each decision can meet the global needs of the satellite-ground collaborative edge network. (2) Instructions are given; The decision-making layer generates specific instructions based on the optimization strategy, including task offloading and resource allocation operation instructions; then, these instructions are fed back to satellites and UAVs through the twin data slices of the gateway station to ensure that task execution can be adjusted according to the latest optimization strategy.

6. The digital twin architecture for satellite-ground collaborative edge network according to claim 1, characterized in that: Through real-time simulation and optimized decision making, the satellite network can be managed throughout its entire life cycle, including all phases from network design, deployment, operation to maintenance. The digital twin model provides precise simulation support in the network design phase to optimize network structure and resource allocation. During network deployment and operation, the digital twin model is dynamically updated through real-time monitoring and data collection to reflect the actual network status and equipment status, and artificial intelligence algorithms are used to make real-time optimization decisions to ensure efficient utilization of network resources and optimal performance. During the maintenance phase, simulation is used to predict potential problems and failures in the network, and preventive maintenance is performed in advance to reduce failure rates and maintenance costs. Throughout the process, the feedback mechanism continuously transmits actual operation data, continuously calibrates and optimizes the digital twin model, and improves simulation accuracy and decision-making effects, thereby achieving continuous optimization and efficient management of satellite networks and ensuring long-term stable operation and performance optimization in complex dynamic environments.

7. The digital twin architecture for satellite-ground collaborative edge network according to claim 2 is characterized in that: The functions of the digital twin data layer are divided into four aspects: (1) Data collection; Data collection completes the ETL process of network data, as well as cleaning and processing; first, extract key operating data of UAVs and satellites from the gateway in real time, including CPU usage, communication link status, mission data volume, bandwidth utilization, transmission power, and associated status data of UAVs and satellites to ensure the synchronization of physical equipment and digital space status; second, standardize and convert data in different formats and load them into distributed storage systems to ensure data consistency and compatibility; finally, in order to ensure data quality, the collection process will clean and process the data, remove noise and outliers, and fill in missing data to ensure data accuracy and completeness; the processing process will also convert and compress the data format for subsequent storage and analysis; (2) Data storage; The digital twin data layer uses a variety of storage technologies to cope with large-scale and diverse network data; For structured data, a relational database is used for storage; for unstructured data, a distributed file system is used. At the same time, in order to improve storage efficiency, the digital twin system divides data into "cold data" and "hot data". Hot data is stored in high-performance storage media to support real-time access, and cold data is stored in large-capacity, low-cost storage devices to save storage resources. The distributed storage architecture supports highly scalable storage of massive data, ensuring that the digital twin system can maintain efficient storage capabilities even when large-scale device data is influx; (3) Data services; In terms of data services, the digital twin data layer provides efficient data retrieval and processing functions for the digital twin model layer and the digital twin decision layer. Through the optimized indexing mechanism, it provides fast query and retrieval services for massive data and supports fast data access in multiple dimensions. At the same time, it uses transaction management and locking mechanisms to ensure that data consistency and integrity can be maintained when multiple data service requests are accessed concurrently. The digital twin data layer also provides batch data processing capabilities and a unified interface, allowing different system modules to easily call data and implement task scheduling and resource optimization operations; (4) Data management; The digital twin data layer provides comprehensive data management functions, including data asset management, security management, quality management, and metadata management. Data asset management classifies, labels, and archives collected data to ensure its traceability and manageability. Security management uses encryption technology and permission control to prevent data leakage and unauthorized access, and ensure data security when transmitting sensitive data. Quality management uses automated tools to detect the accuracy, completeness, and consistency of data, and repairs or issues warnings for defective data; At the same time, metadata management generates metadata for each piece of data to support classification management and rapid retrieval, thereby improving data availability and management efficiency.

Images