HU Canglong,LI Hanqi,WANG Jiamin,et al.Research on the Measurement Method of Wind Direction Sensor[J].Journal of Chengdu University of Information Technology,2026,41(02):154-159.[doi:10.16836/j.cnki.jcuit.2026.02.003]
风向传感器计量方法研究
- Title:
- Research on the Measurement Method of Wind Direction Sensor
- 文章编号:
- 2096-1618(2026)02-0154-06
- 分类号:
- TP183
- 文献标志码:
- A
- 摘要:
- 随着气象现代化建设的深入开展,大量观测设备投入并应用到气象预报和防灾减灾的服务中。为确保这些观测设备数据的可靠性和准确性,实现计量业务工作的信息化管理,提高计量业务的实时性和计量数据的可用性,通过建立指数函数回归模型和深度学习的方法,对不同运行时长的风速计所记录的启动风速进行统计,以建立启动风速与运行时长之间的相关数学模型并进行对比。结果表明,指数函数回归模型和自行设计的BP神经网络模型均能胜任最大使用年限的回归任务,且相对于回归模型,BP神经网络不需要分别对各区域数据分别建立模型,对风向传感器的计量检定具有重要意义。
- Abstract:
- With the deepening of meteorological modernization, a large amount of observational equipment has been invested in and applied to meteorological forecasting and disaster prevention and reduction services. To ensure the reliability and accuracy of data from these observation devices, achieve information-based management of measurement business work, and improve the real-time performance of measurement business and the availability of measurement data. This article establishes an exponential regression model and deep learning methods to statistically analyze the start-up wind speed recorded by anemometers with different operating hours, to establish a mathematical model of the correlation between start-up wind speed and operating hours and compare them. The results indicate that both the exponential function regression model and the self-designed BP neural network model are capable of performing the regression task for the maximum service life. Compared to the regression model, the BP neural network does not require separate models for each region's data, which is significant for the metrological calibration of wind direction sensors.
参考文献/References:
[1] 魏明明,刘禹杉,章允昊.区域自动气象站风向风速传感器检测仪设计与应用[J].气象科技,2018,46(4):659-664.
[2] 李明香,宋长青,姚文,等.区域自动气象站资料报表制作与共享系统[J].气象与环境学报,2009,25(2):50-53.
[3] 王明辉,陈冰怀,黄海(王莹),等.自动气象站常规传感器现场检测系统设计[J].气象科技,2019,47(3):402-407.
[4] 孙哲,邢程,李帅,等.地市级移动气象计量检定系统风传感器核查方法[J].现代化农业,2022(4):85-86.
[5] 胡苍龙,李晓红.自动气象站雨量传感器现场校准方法计量性能要求合理性探讨[J].计量与测试技术,2019,46(5):79-80.
[6] 张朝明.基于格雷码编码的EL15-2C型风向传感器维护方法及分析探讨[J].自动化技术与应用,2021,40(7):112-115.
[7] 张国翊,胡铮.改进BP神经网络模型及其稳定性分析[J].中南大学学报(自然科学版),2011,42(1):115-124.
[8] Yang Zhen,Zhang Husheng,et al.Uncertainty Evaluation of Wind Speed Sensor in Automatic Weather Station[J].IOP Conference Series:Earth and Environmental Science,2021,768(1).
[9] 宋中玲,张静.新型自动气象站传感器工作原理及测试方法研究[J].自动化仪表,2018,39(7):78-81.
[10] Zhang Can,Shao Yichuan,et al.The WuC-Adam algorithm based on joint improvement of Warmup and cosine annealing algorithms[J].Mathematical biosciences and engineering:MBE,2024,21(1):1270-1285.
[11] Cheng Wenchang,Hsiao Hungchou,Li Lihua.Deep Learning Mask Face Recognition with Annealing Mechanism[J].Applied Sciences,2023,13(2):732.
[12] Ting Xie.Improved single target identification tracking algorithm based on IPSO-BP neural network[J].Applied Mathematics and Nonlinear Sciences,2024,9(1).
[13] Yao Kai,Li Xinglong,Lu Zhaoxu.Study on ultrasonic quantitative evaluation technique based on BP neural network and D-S evidence theory[J].Ultrasonics,2023,138:107235.
[14] Li X,Xu J,Chinese University of Hong Kong,et al.The Establishment Of Self-Adapting Algorithm Of BP Neural Network And Its Application[C].International Conference on Information and Management Sciences.School of Business and Administration,Sichuan University,Chengdu,China,2002.
[15] Loshchilov I,Hutter F.SGDR:Stochastic Gradient Descent with Warm Restarts[J].arXiv e-prints,2016.
备注/Memo
收稿日期:2024-07-26
基金项目:四川省科技计划重点研发资助项目(2023YFG0170); 高原与盆地暴雨旱涝灾害四川省重点实验室科技发展资助项目(SCQXKJYJXMS202319)