Surveillance Information Processing Strategy of Distribution Network Under Power Supply Reliability

In order to solve problems of computer delay and low information batch storage load of the intelligent distribution network system, a machine learning-based distribution network monitoring information batch processing method is proposed. The method uses the HDFS distributed file system to measure the data transmission records, builds the distribution network monitoring information scheduling cluster, measure the latency of the computer system, and fills system delay time with the data response task. It plans the processing mode of the monitoring information interface, divides the large-scale flow calculation of machine learning, and builds the monitoring information batch processing model. It increases and counts the information batch accumulation nodes, and expands the distribution network to develop the learning path of intelligent analysis machine through the cloud computing service level. It calculates the telemetry information and effective event information, plans the layout of telemetry coordination nodes through HDFS. It divides the storage space of monitoring information, monitors the storage space with remote control metabase, and completes the batch processing and storage of accumulation V002 distribution network monitoring information with the manager. The experimental results show that the highest storage load rate of the information cluster processing is 95%, and the batch processing effect of the distribution network monitoring information is guaranteed.