Method and device for elastically transmitting telemetry data based on machine learning

Abstract

The invention discloses a method and a device for elastically transmitting telemetry data based on machine learning. The method comprises the following steps of: performing data acquisition and training learning, acquiring the telemetering data in real time by a sending end and sending the telemetering data to a receiving end, meanwhile performing training on a neural network of the sending end until the deviation of a training result and the telemetering data is lower than a preset threshold; performing parameters transmitting and data simulation, transmitting neural network parameters whichcomplete a training target to the receiving end by a transmitting end, constructing an isomorphism neural network according to the neural network parameters by the receiving end and generating telemetry simulation data by utilizing allowed prediction indication information sent by the sending end; and performing prediction comparison and outliers elimination, acquiring the telemetering data and comparing the telemetry data with an output result of the neural network by the sending end, and generating indication information in combination with flight control prior information for subsequent processing. According to the method for elastically transmitting the telemetry data based on machine learning, the time varying characteristics of the information entropy of the telemetry parameter can be dynamically adapted, the transmission capacity of the telemetry data is greatly reduced, and the reliability and the flexibility of end-to-end information transmission of the space wireless link areimproved.

Description

technical field [0001] The invention relates to the technical field of spatial information transmission, in particular to a method and device for elastic transmission of telemetry data based on machine learning. Background technique [0002] Deep space ultra-long-distance communication is facing technical challenges such as limited transmission power, long transmission distance, and weak received signals. Reducing the amount of data transmitted by deep space channels is an important technical difficulty that deep space communication must solve. At present, lossy compression methods are widely used at home and abroad to reduce the data volume of scientific application data transmission, especially optical detection data; for telemetry data, some literatures have proposed a real-time online compression method with a compression rate of about 40%, but this method is only It can be applied to telemetry data frames with a fixed length and has insufficient anti-channel error perfo...

AI Technical Summary

Problems solved by technology

[0009] From the above analysis, it can be seen that the related research and application of machine learning technology in aerospace telemetry has not yet involved the field of space information transmission technology.

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