Introduction to the application of data source layer in digital twin intelligent scenarios

The data source layer occupies a core position in the application of digital twin intelligent scenarios, especially when building and operating digital twin data lakes, its role is irreplaceable. It is like a stable bridge, closely connecting the complex and diverse professional data of various professional service companies with the needs of digital twin intelligent applications, and is the key to achieving efficient data collection, scientific management and reasonable application.

The functions of the data source layer mainly revolve around the following four key aspects:

Professional data collection management: This function is like the "information tentacles" of the digital twin data lake, which goes deep into various professional fields in all directions, and accurately collects various professional data such as mining rights, reserves, geophysical exploration, drilling, logging, well logging, oil and gas testing, downhole operations, oil and gas production, oil and gas production, surface engineering, gathering and processing, analysis and testing, geographic information data, etc. Each data contains important information in a professional field. For example, mining rights data is related to the ownership and development rights of resources, while oil and gas production data reflects the real-time status of the production process. By accurately collecting these data, rich and authentic original materials are provided for subsequent data analysis, model construction and application decision-making.

Data loading: This link is like a "porter" of data. With the help of professional data loading tools, the source data is safely and orderly transported to the intermediate area. For those data with large volume and good structure, such as standardized production records, financial statements, etc., they will be efficiently loaded into the intermediate temporary database. The temporary database is like an orderly "transit station" to facilitate subsequent data processing processes, such as data cleaning and integration. For large blocks of data, such as massive image data generated by geological exploration, and unstructured data, such as documents and reports, they will be loaded by file directory or physical submission. This flexible loading method ensures that all types of data can enter the system smoothly, laying the foundation for subsequent management and analysis.

Data verification: Due to the wide range of data sources, involving internal service teams and service teams of many external companies, it is almost impossible for all service providers to use the same system to manage professional data. Moreover, not all data has the same value or is suitable for entering the main database. Therefore, before the data enters the main database, the authoritative data in the basic entity database must be used as a reference standard to strictly verify the unique identifiers and names of the basic entity data to be submitted to the main database. This is like carefully checking the identity information of each piece of goods before entering the "high-level warehouse". At the same time, the data must be strictly filtered to remove those data that do not meet the standards and specifications and have low value. Only data that has been screened layer by layer, meets the standards and specifications and has high value is allowed to enter the main library, thereby ensuring the quality and reliability of the main library data.

Data entry: After the data passes the strict data verification link, it enters the data entry stage. This stage needs to determine the main library target to be loaded based on the type of data to be entered, such as structured data, semi-structured data, etc. Different types of data may be stored in different areas or table structures of the main library to achieve efficient storage and convenient call. At the same time, in order to ensure that the main library data can reflect the latest business conditions and data changes in a timely manner, the main library will be updated regularly. This regular update mechanism ensures the accuracy, standardization and timeliness of the main library data, and provides solid and reliable data support for various applications of digital twin intelligent scenarios, such as real-time simulation and intelligent decision-making.

Through these four closely related functions, the data source layer forms a complete data processing closed loop, effectively realizing the collection and storage management of production data and management data of the service company, and uploading high-quality data that has been carefully screened and processed to the main database as a data source, providing strong support for the smooth operation and continuous development of digital twin intelligent scenarios.