Research Of The Compressed Sensing Application Technology Based On Hierarchical Trees Model

At present, the information redundancy is the most obvious defect of the traditional methods for data collection.The resource distribution in data acquisition equipment is irrationality, with the enlargement of data, convenient universal query method will be more highly focused. The Compress Sensing theory, proposed in2006, can be successfully reconstructed from much fewer numbers of linear measurements than needed by the tradition sampling theory.While, until now, a couple of undesirable characteristics, including excessive quantity of measurement data, poor reconstruction performance with high compressive ratio, and the unclear inherent structural characteristics of sparse signal, still need to be further improved.For efficient and consistent signal compressive sampling and reconstruction, with the analysis to the sparsity of pipeline signal and its structural in wavelet space, a new Compressive Sensing method was developed based on the multi-level tree model of wavelet. Combined with the established reconstruction algorithm, the effectiveness of the proposed Compressive Sensing method was evaluated.Pipeline transport is an important measurement in energy transport, with the general application to microprocessors, advanced the remote leakage monitoring system for oil and gas pipelines in traditional process to a new stage. While, how to store and transmit huge detected data is the main concern in the industrial monitoring and control network. Present the Compressed Sensing pipeline leak research is placed in the early stage in the, the theories study is basically placed in the blank appearance. This paper combined the implementation of pipeline leak compression sampling with improved Compressed Sensing theory, which provide better performation than the traditional method.