Research And Implementation Of Scheduling Data Network Monitoring Platform Based On Flow Computation

Because of any network failures in the power dispatching network,extremely serious accidents may occur.Therefore,it has extremely high reliability and security requirements.At the same time,with the advent of the era of power big data,the current traditional network monitoring system cannot meet the needs of its real-time processing capabilities and expansion capabilities in the face of large amounts of data.Therefore,the analysis and processing of large-scale data generated in real time requires a special real-time data analysis platform.This paper combines the characteristics of power dispatching information network and the requirements of monitoring accuracy and real-time performance,and studies the main key technologies related to flow calculation and the main methods for implementing network monitoring.The main achievement is to build a data processing and analysis platform based on flow calculation,design and implement the overall framework of the platform and data network anomaly monitoring model,and use open source flow computing framework represented by Spark Streaming in Apache Spark to join such as Kafka distributed Components such as Message Queue and Redis In-Memory Database provide stable and efficient data sources and data service interfaces for the data analysis platform,thus realizing the real-time analysis and processing of various types of massive data suitable for power dispatching networks to complete traffic anomaly monitoring scenarios,and adding network anomalies at the same time.Based on the detection model,a clustering-based network anomaly partitioning and network-based anomaly recognition based on classification algorithms are proposed to implement network learning based on machine learning.Through experimental testing and analysis,the results prove the availability of the platform.At the same time,through experimental comparison with traditional computing methods such as traditional cloud computing and batch computing,the experiment verifies that the platform has better throughput and real-time performance,and the data network monitoring model has a higher level.The accuracy rate can effectively achieve network monitoring and ensure the safe operation of the dispatching data network.