基于“海豚”深海剖面浮标的CTD数据自适应观测方法

刘楚; 侯斐; 杨泽荣; 周凌霄; 朱心科

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

基于“海豚”深海剖面浮标的CTD数据自适应观测方法

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

刘楚^{1, 2,},
侯斐^{1, 2,},
杨泽荣^3,,
周凌霄^3,,
朱心科^{1, 2,}

1.
海底科学与划界全国重点实验室, 浙江杭州, 310012
2.
自然资源部第二海洋研究所, 浙江杭州, 310012
3.
国电电力浙江舟山海上风电开发有限公司, 浙江舟山, 316000

详细信息

作者简介:
刘楚：刘　楚(1996-), 男, 硕士研究生, 研究方向为智能浮标控制

中图分类号: U674.941; TJ630
计量
- 文章访问数: 0
- HTML全文浏览量: 1
- PDF下载量: 0
- 被引次数: 0
出版历程
- 收稿日期: 2025-02-18
- 修回日期: 2025-03-17
- 录用日期: 2025-03-18
- 网络出版日期: 2025-07-01

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

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

摘要

摘要: 在深海剖面浮标的剖面观测过程中, 出于浮标的低功耗设计需求, 需要采取合适的传感器采集频率以降低数据采集和传输过程中的功耗。基于自主研发的“海豚”深海剖面浮标, 结合搭载外设的具体功耗以及Argo计划中获取的温盐深(CTD)剖面数据进行仿真分析, 提出了一种基于自适应遗传算法的温盐数据采集策略优化模型, 根据相邻剖面数据具有强相关性的特点, 通过分析历史剖面数据特征规划新一轮剖面的采样方案, 在采样过程中根据数据的变化趋势适应性调整采样频率。基于实际数据的仿真分析结果表明, 通过该观测方法获取的剖面数据与实际数据相差较小, 同时相较于预编程的深度分层观测方案进一步降低了剖面浮标的整体功耗。
- 温盐深 /
- 数据采集 /
- 深海 /
- 浮标 /
- 自适应遗传算法
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

HTML全文

图 1 “海豚”深海剖面浮标

Figure 1. Dolphin deep sea profiling float

下载: 全尺寸图片幻灯片

图 2 “海豚”深海剖面浮标工作模式

Figure 2. Dolphin deep sea profiling float operation mode

下载: 全尺寸图片幻灯片

图 3 铱星数据传输过程功耗情况

Figure 3. Power consumption during Iridium data transmission process

下载: 全尺寸图片幻灯片

图 4 剖面浮标工作海域及漂移轨迹

Figure 4. Operating area and drift trajectory of the profiling float

下载: 全尺寸图片幻灯片

图 5 剖面浮标月度温盐变化

Figure 5. Monthly temperature and salinity variations of profiling float

下载: 全尺寸图片幻灯片

图 6 剖面浮标连续剖面温盐数据皮尔逊相关系数

Figure 6. Pearson correlation coefficient of continuous profile temperature and salinity data from profiling float

下载: 全尺寸图片幻灯片

图 7 不同采样间隔下剖面数据变化情况

Figure 7. Variations in profile data under different sampling intervals

下载: 全尺寸图片幻灯片

图 8 算法主要流程

Figure 8. Main flow of the algorithm

下载: 全尺寸图片幻灯片

图 9 温盐剖面优化效果

Figure 9. Optimization effect of temperature and salinity profiles

下载: 全尺寸图片幻灯片

图 10 温盐剖面数据连续优化误差

Figure 10. Continuous optimization error of temperature and salinity profile data

下载: 全尺寸图片幻灯片

图 11 温盐剖面连续优化相关性变化

Figure 11. Correlation changes in continuous optimization of temperature and salinity profiles

下载: 全尺寸图片幻灯片

图 12 功耗优化情况

Figure 12. Power consumption optimization

下载: 全尺寸图片幻灯片

图 13 温度主动、被动采样方案效果对比

Figure 13. Comparison of active and passive temperature sampling schemes

下载: 全尺寸图片幻灯片

表 1 电导率传感器功耗情况

Table 1. Power consumption of conductivity sensor

工作阶段	工作电流/mA	功耗/mWs
采样	34.22	410.64
休眠唤醒	3.9	46.8
待机	7.75	93
休眠	0.07	0.84

下载: 导出CSV

表 2 剖面浮标采样策略

Table 2. Profiling float sampling strategy

2902901
深度区间/m	采样间隔/m
0~10	2
10~600	10
600~800	20-25-30
800~6 000	50

下载: 导出CSV

表 3 连续优化剖面数据量变化情况

Table 3. Changes in data volume of continuously optimized profiles

剖面序号	温度总数据量	温度优化效率/%	盐度总数据量	盐度优化效率/%
1	127	19.1	112	28.7
2	118	24.8	108	31.2
3	111	29.3	127	19.1
4	110	29.9	148	5.7
5	138	12.1	158	−0.64
6	131	16.6	135	14.0
7	120	23.6	114	27.4
8	124	21.0	108	31.2
9	115	26.8	108	31.2
10	113	28.0	117	25.5
11	108	31.2	108	31.2
12	109	30.6	126	19.7
13	111	29.2	121	22.9
14	115	26.8	113	28.0

下载: 导出CSV

参考文献(18)

[1]	王波, 李民, 刘世萱, 等. 海洋资料浮标观测技术应用现状及发展趋势[J]. 仪器仪表学报, 2014, 35(11): 2401-14. WANG B, LI M, LIU S X, et al. Current status and trend of ocean data buoy observation technology applications[J]. Chinese Journal of Scientific Instrument, 2014, 35(11): 2401-14.
[2]	陈鹿, 潘彬彬, 曹正良, 等. 自动剖面浮标研究现状及展望[J]. 海洋技术学报, 2017, 36(2): 1-9. CHEN L, PAN B B, CAO Z L, et al. Research Status and prospects of automatic profiling floats[J]. Journal of Ocean Technology, 2017, 36(2): 1-9.
[3]	PU Y, SAMSON G, SHI C, et al. Blackghost: An ultra-low-power all-in-one 28 nm CMOS SoC for Internet-of-Things[C]//2017 IEEE Symposium in Low-Power and High-Speed Chips (COOL CHIPS). Yokohama, Japan: IEEE, 2017: 1-3.
[4]	Shafiee N, Tewari S, Calhoun B, et al. Infrastructure circuits for lifetime improvement of ultra-low power IoT devices[J]. IEEE Transactions on Circuits and Systems I: Regular Papers, 2017, 64(9): 2598-610. doi: 10.1109/TCSI.2017.2693181
[5]	ZHU W B, ZHANG Y F, YAZDI N. An ultra-low power switch array of temperature and humidity sensors with direct digital output[C]//2013 Transducers & Eurosensors XXVII: The 17th International Conference on Solid-State Sensors, Actuators and Microsystems. Barcelona, Spain: IEEE, 2013: 108-111.
[6]	LEE C, KIM S. The sampling period estimation based adaptive sampling algorithm for a self-sustainable disaster monitoring system[C]// 2020 European Conference on Networks and Communications. Dubrovnik, Croatia: IEEE, 2020: 165-169.
[7]	CAI W, ZHANG M. Spatiotemporal correlation-based adaptive sampling algorithm for clustered wireless sensor networks[J]. International Journal of Distributed Sensor Networks, 2018, 14(8).
[8]	FATTOUM M, JELLALI Z, ATALLAH L N. Adaptive sampling approach exploiting spatio-temporal correlation and residual energy in periodic wireless sensor networks[J]. IEEE Access, 2023, 11: 7670-81. doi: 10.1109/ACCESS.2023.3237024
[9]	SUN Y, YUAN Y, LI X, et al. An adaptive sampling algorithm for target tracking in underwater wireless sensor networks[J]. IEEE Access, 2018, 6: 68324-36. doi: 10.1109/ACCESS.2018.2879536
[10]	ALGABROUN H, HÅKANSSON L. Parametric machine learning-based adaptive sampling algorithm for efficient IoT data collection in environmental monitoring[J]. Journal of Network and Systems Management, 2024, 33: 5.
[11]	HEO S, MAYER P, MAGNO M. Predictive energy-aware adaptive sampling with deep reinforcement learning[C]// 2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS). Glasgow, UK: IEEE, 2022: 1-4.
[12]	GIORDANO M, CORTESI S, MEKIKIS P, et al. Energy-aware adaptive sampling for self-sustainability in resource-constrained IoT devices[C]//In Proceedings of the 11th International Workshop on Energy Harvesting & Energy-Neutral Sensing Systems. New York, USA, 2023: 65-71.
[13]	CHEN F, XU S Z, ZHAO Y, et al. An adaptive genetic algorithm of adjusting sensor acquisition frequency[J]. Sensors, 2020, 20: 990. doi: 10.3390/s20040990
[14]	SHU T X, XIA M, CHEN J H, et al. An energy efficient adaptive sampling algorithm in a sensor network for automated water quality monitoring[J]. Sensors, 2017, 17: 2551. doi: 10.3390/s17112551
[15]	ZHOU H, ZENG Z, LIAN L. Adaptive re-planning of AUVs for environmental sampling missions: A fuzzy decision support system based on multi-objective particle swarm optimization[J]. International Journal of Fuzzy Systems, 2017, 20: 650-671.
[16]	KATOCH S, CHAUHAN S S, KUMAR V. A review on genetic algorithm: past, present, and future[J]. Multimed Tools Appl, 2021, 80: 8091-126. doi: 10.1007/s11042-020-10139-6
[17]	ALHIJAWI B, AWAJAN A. Genetic algorithms: theory, genetic operators, solutions, and applications[J]. Evol. Intel, 2024, 17: 1245-56. doi: 10.1007/s12065-023-00822-6
[18]	李昆鹏, 刘腾博, 李文莉. 改进自适应遗传算法求解“货到人”拣选系统订单分批问题[J]. 机械工程学报, 2023, 59(4): 308-317. doi: 10.3901/JME.2023.04.308 LI K P, LIU T B, LI W L. Improved adaptive genetic algorithm for order batching of “part-to-picker” picking system[J]. Journal of Mechanical Engineering, 2023, 59(4): 308-317. doi: 10.3901/JME.2023.04.308