Adaptive Observation Methods for Temperature and Salinity Data Based on the Dolphin Deep-sea Profiling Buoy

LIU Chu; HOU Fei; YANG Zerong; ZHOU Lingxiao; ZHU Xinke

doi:10.11993/j.issn.2096-3920.2025-0025

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LIU Chu, HOU Fei, YANG Zerong, ZHOU Lingxiao, ZHU Xinke. Adaptive Observation Methods for Temperature and Salinity Data Based on the Dolphin Deep-sea Profiling Buoy[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0025

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LIU Chu, HOU Fei, YANG Zerong, ZHOU Lingxiao, ZHU Xinke. Adaptive Observation Methods for Temperature and Salinity Data Based on the Dolphin Deep-sea Profiling Buoy[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0025

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Adaptive Observation Methods for Temperature and Salinity Data Based on the Dolphin Deep-sea Profiling Buoy

doi: 10.11993/j.issn.2096-3920.2025-0025

LIU Chu^{1, 2
,},
HOU Fei^{1, 2
,},
YANG Zerong^3
,,
ZHOU Lingxiao^3
,,
ZHU Xinke^{1, 2
,}

1.
State Key Laboratory of Submarine Geoscience, Hangzhou 310012, China
2.
Second Institute of Oceanography, MNR, Hangzhou 310012, China
3.
Guodian Electric Power Zhejiang Zhoushan Offshore WindPower Development Co., Ltd, Zhoushan 316000, China

Received Date: 2025-02-18
Accepted Date: 2025-03-18
Rev Recd Date: 2025-03-17

Available Online: 2025-07-01

Abstract

Abstract

During the profiling observation process of deep-sea profiling buoys, appropriate sensor sampling frequency must be implemented to reduce power consumption in data acquisition and transmission due to low-power design requirements. Based on the independently developed "Dolphin" deep-sea profiling buoy, this study establishes an optimized model for temperature-salinity data collection strategy using adaptive genetic algorithm through simulation analysis combining peripheral device power consumption specifications and conductivity, temperature, depth(CTD) profile data from Argo program. Leveraging the strong correlation characteristics between adjacent profile data, the proposed method plans new-round sampling schemes by analyzing historical profile data features, while adaptively adjusting sampling frequency according to data variation trends during acquisition. The simulation analysis based on real-world data demonstrates that the profile data acquired through this observation method exhibits minimal deviation from actual measurements, while significantly reducing the overall power consumption of the profiling buoy compared to the pre-programmed deep-layer observation scheme.
- conductivity, temperature, depth,
- data collection,
- deep-sea,
- buoy,
- adaptive genetic algorithm

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References(18)

References

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