Ice buoy heterogeneous measurement data compression and reconstruction method and system

CN122394569APending Publication Date: 2026-07-14POLAR RES INST OF CHINA
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
POLAR RES INST OF CHINA
Filing Date
2026-06-15
Publication Date
2026-07-14

AI Technical Summary

Technical Problem

In polar ocean observations, the efficient transmission and accurate processing of ice buoy data are limited by extreme environments and communication bandwidth. Existing compression technologies are insufficient to meet the requirements of high-fidelity, continuous three-dimensional data fields, and the reconstruction results often suffer from distortion and error accumulation.

Method used

An adaptive spatiotemporal prediction and quantization coding method based on polar environment state characteristics is adopted, combined with Golomb-Rice coding and reverse control signaling, to achieve efficient data compression and reconstruction, construct an edge-cloud collaborative closed-loop system, and dynamically adjust the coding strategy to adapt to changes in the polar environment.

Benefits of technology

It enables efficient backhaul and accurate three-dimensional physical field reconstruction of heterogeneous multi-source polar ocean data under extreme environments, enhances the long-term adaptive operation capability of the polar ocean monitoring network, and overcomes communication bottlenecks and systematic error accumulation problems.

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Abstract

The application discloses an ice buoy heterogeneous measurement data compression and reconstruction method and system, and belongs to the technical field of polar ocean observation data processing. The drift speed and the observation parameter change rate of the ice buoy are extracted at the compression end to construct a polar environment state feature, and a dynamic spatio-temporal observation window is reconstructed according to the feature to perform adaptive spatio-temporal prediction. The feature is used as a feedforward penalty term to adjust a quantization step and Golomb-Rice coding parameters, and high compression ratio packaging and transmission of data under limited bandwidth are realized. After decoding and spatio-temporal registration at the reconstruction end, a trust coefficient based on the environment state feature is introduced for multi-source weighted fusion. A continuous three-dimensional data field is constructed by using a physical guided neural network, error attribution is finally calculated, and reverse control signaling is generated and issued to update the front-end control hyperparameters. The application overcomes the bottleneck of narrowband communication in the polar region, realizes high-fidelity reconstruction and long-term closed-loop self-correction.
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