Distributed Computing and Data Processing Technology under the Power Data Middleware Architecture

With the establishment and development of integrated monitoring platforms and communication information platforms for intelligent substations, the data volume of the power system is showing explosive growth. However, existing data centers have poor processing capabilities for massive amounts of data, making it difficult to meet the multiple service quality requirements of users. How to effectively manage massive and complex data, fully utilize the value of data, improve service quality, reduce operational costs, and achieve rapid response to users is an urgent problem to be solved. Therefore, based on the actual needs of power enterprises, this article proposed a cloud computing power data middleware architecture based on distributed technology, which effectively managed, cleaned, converted, and evaluated power data sources through cloud computing platforms. This article mainly analyzed the shortcomings of traditional power data processing platforms and the feasibility of cloud computing based power data platforms. Finally, through load balancing bias comparison and pressure testing experiments under different task numbers, it was proven that cloud computing systems outperformed traditional power data processing systems in terms of utility value, completion time, and load balancing. Moreover, in the pressure testing experiment, under the condition of 10 concurrent users and 673 system request transactions, 91.3% of the interface response time in the cloud computing system was within 50ms. Therefore, cloud computing systems have certain advantages in processing power data.