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 GEP  Vol.7 No.6 , June 2019
Human Comfort Instrument Design Based on Embedded
Abstract:
The traditional human comfort meter has the following defects: the interface is not uniform; the operation is cumbersome and complicated; the interface is unfriendly, and the stability and adaptability are poor. This paper presents a design scheme for human comfort instrument based on embedded system, using S3C2440 embedded development board and the sensors to collect the real-time temperature, relative humidity and wind speed data and to process the collecting data; then obtaining the human body comfort value according to the basic algorithm of human body comfort instrument; giving the human comfort conclusion according to the diastolic index range of human comfort, and showing the temperature and humidity, wind speed, comfort value and conclusion through writing the Qt graphical user interface program. At the same time, the human comfort instrument has the data storage function. The human comfort instrument is high in integration, strong in real time, high in sensitivity, stable and reliable, and it meets the development goals of the intelligent meteorological service, and meets the demand of the meteorological service that is closer to life, and it has broad development prospect.
Cite this paper: Chen, S. , Shi, J. , Li, X. , Cui, M. and Su, L. (2019) Human Comfort Instrument Design Based on Embedded. Journal of Geoscience and Environment Protection, 7, 115-124. doi: 10.4236/gep.2019.76010.
References

[1]   Chen, L., Wu, Z., & Liu, Y. (2013). Porting of Embedded Operating System on TMS320F28335 Platform. Computer Technology and Development, 23, 7-11.

[2]   Guo, S., Feng, H., & Zhou, S. (2016). Design of Portable Instrument Using Microcontroller Operating System. Automation Instrumentation, 37, 93-95.

[3]   Jin, F., & Cui, P. (2014). Research on Signal and Slot Mechanism in Embedded Qt. Electronic Design Engineering, 22, 168-170.

[4]   Kai, Z., Kan, Z., & Zhang, F. (2014). Evaluating Bus Transit Performance of Chinese Cities: Developing an Overall Bus Comfort Model. Transportation Research Part A: Policy & Practice, 69, 105-112.
https://doi.org/10.1016/j.tra.2014.08.020

[5]   Li, J., Wang, Y., Peng, N. et al. (2016). Analysis of Tourism Meteorological Conditions in Shilin County. Anhui Agricultural Sciences, 44, 200-201.

[6]   Li, L., & Shi, W. (2011). Design and Implementation of Intelligent Temperature and Humidity Control System in Greenhouses. Hunan Agricultural Sciences, 21, 135-138.

[7]   Ma, D., Wang, W., Jiang, Q. et al. (2013). On-Line Temperature and Humidity Monitoring System Based on RS485 Bus. Chinese Journal of Agricultural Mechanization, 34, 121-126.

[8]   Mostafavi Tehrani, S. S., Taylor, R. A., Nithyanandam, K., & Shafiei Ghazani, A. (2017). Annual Comparative Performance and Cost Analysis of High Temperature, Sensible Thermal Energy Storage Systems Integrated with a Concentrated Solar Power Plant. Solar Energy, 153, 153-172.
https://doi.org/10.1016/j.solener.2017.05.044

[9]   Moustris, K., Tsiros, I. X., Tseliou, A., & Nastos, P. (2018). Development and Application of Artificial Neural Network Models to Estimate Values of a Complex Human Thermal Comfort Index Associated with Urban Heat and Cool Island Patterns Using Air Temperature Data from a Standard Meteorological Station. International Journal of Biometeorology, 4, 1-10.
https://doi.org/10.1007/s00484-018-1531-5

[10]   Peng, J., Zong, Z., Huang, X. et al. (2011). Evaluation of Climate Comfort and Prediction Equation for Rafting in Mengdong River, Hunan Province. Meteorology, 37, 771-776.

[11]   Slater, L. J., Villarini, G., & Bradley, A. A. (2017). Evaluation of the Skill of North-American Multi-Model Ensemble (NMME) Global Climate Models in Predicting Average and Extreme Precipitation and Temperature over the Continental USA. Climate Dynamics, 46, 1-16.
https://doi.org/10.1007/s00382-016-3286-1

[12]   Takaya, Y., Yasuda, T., Fujii, Y., Matsumoto, S., Soga, T., Mori, H. et al. (2017). Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System Version 1 (jma/mri-cps1) for Operational Seasonal Forecasting. Climate Dynamics, 48, 313-333.
https://doi.org/10.1007/s00382-016-3076-9

[13]   Wallace, Z. C., & Hill, A. A. (2017). Forecaster and Emergency Manager Perspectives on Coordination and Communication with the Weather-Warned Public. Papers in Applied Geography, 3, 157-170.
https://doi.org/10.1080/23754931.2017.1299036

[14]   Wang, B., Bai, X., Zhang, C. et al. (2015). Embedded Software Test Case Generation Based on Interface Automata and Symbolic Execution. Chinese Journal of Computers, 38, 2125-2144.

[15]   Xia, J., & Niu, C. (2011). Research and Implementation of Linux Porting Based on S3C2440A Processor. Computer and Digital Engineering, 39, 77-80.

[16]   Zhang, H., Gao, L., & Song, C. (2012). Research and Implementation of Embedded Linux Cross-Compilation Environment Based on ARM. Computer and Digital Engineering, 40, 151-153.

[17]   Zhu, M., Yin, X., Qu, X. et al. (2014). The Temporal and Spatial Distribution Trend of Summer Human Body Comfort Index in Hulunbeier City. Inner Mongolia Agricultural Science and Technology, 3, 94-96.

 
 
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