Design and Application of Database Architecture with Super Large-Scale for Marketing Service System of Energy Internet in Enterprise Digital Transformation

Design and Application of Database Architecture with Super Large-Scale for Marketing Service System of Energy Internet in Enterprise Digital Transformation

연구 분야: Databases

학회: 2024 3rd International Conference on Energy and Electrical Power Systems (ICEEPS)

The digital transformation of enterprises and the characteristics of power marketing business services put forward higher requirements for data storage, data flow, database optimization and other information system database architecture design. Aiming at the digital transformation of enterprises and the typical scenarios of power marketing such as power tariff calculation, business office payment, collection and payment, etc., we put forward data storage design methods such as two-level data distribution, logical database division, physical database division, data sharing and access, data flow methods, data interaction between processing and analysis domains, data interaction between internal and external networks, two-level data interaction, and data flow design methods such as database partitioning, database indexing, database querying, database optimization, and so on. It also proposes data flow design methods, data interaction between processing and analysis domains, data interaction between internal and external networks, two-level data interaction and other data flow design methods, as well as database optimization design contents such as database partitioning, database indexing and database query optimization. According to the actual commissioning system operation and typical business scenarios application test results, the total calculation time of billing scenario is 2 hours, the data drop success rate is 98.84%, the average response time of billing scenario is 2.084 seconds, the transaction success rate is 99.9%, and the main core indexes satisfy the requirements of electric power marketing business on the performance of the system database.