Telemetry Data Compression Algorithm Based on Balanced Cyclic Neural Network and Deep Learning

With the rapid development of telemetry and telecontrol technology, the amount of information for acquiring telemetry data is increasing, which requires a large amount of storage space when storing, a large bandwidth when transmitting, and data processing as fast as possible. Therefore, data compression technology is needed to solve this problem. The redundancy of telemetry data is also very large. Statistics show that the redundancy of data transmitted in the standard telemetry system reaches more than 90%. In order to reduce the signal space and relieve the pressure of data processing, it is necessary and important to use data compression technology in telemetry system. Considering the space performance and time performance of the lossless compression algorithm, several commonly used lossless compression algorithms are compared for telemetry noise data. The compression space performance of each algorithm still needs to be improved. Considering that the measured engineering data is distributed in a typical balanced way, we studied the anomaly detection problem of time series data with inter class balance, and proposed an anomaly detection problem based on recurrent neural network (RNN). Finally, the overall test of the developed data acquisition and compression system is carried out. The test results show that the system has completed the real-time acquisition and compression of six channels of rapidly varying parameters, and the compression ratio is more than 1.8:1. All indicators meet the design requirements.