基于PMV的室内环境智能系统设计

发布时间：2018-11-26 09:28

【摘要】：近年来,随着人们生活水平的不断提高以及计算机技术和网络技术的进步,出现了越来越多的智能家居产品。它们不仅具有很多传统家居所具有的功能,而且更关注用户的个人体验,使人们生活的更舒适、便捷。目前,主流的智能家居产品是以物联网技术为基础的各种监控产品,针对室内热舒适度的产品却几乎没有。原因是PMV(PredictedMeanVote)热舒适度指标的参数计算都是比较复杂的,涉及的环境因素多且数据采集非常不方便。并且现有的研究中针对每个用户的自身情况考虑的也比较少,所以往往实用效果都不太理想。为了解决热舒适度计算的问题,本文仿真研究了 PMV热舒适性指标的计算方法,针对标准粒子群算法寻优时对变量缺乏约束,提出了一种带约束的改进粒子群算法,利用带约束的改进粒子群算法实现PMV方程的求解。其次,设计了一个基于智能手机的室内热舒适度系统。包括Android应用和基于nRF51822低功耗蓝牙芯片的数据采集端。系统通过蓝牙将环境数据发送至Android端。在Android应用中,通过记录人单位时间内的走路的步数,估计人体的新陈代谢率,通过用户在应用中的设置得到衣服热阻。将粒子群算法移植到Android应用中,完成PMV计算和控制决策。为了使室内环境满足人体对舒适度的要求,以PMV等于零为目标,求解出使人感到最舒适的空气温度和空气流速给定值,用红外发送给空调设备实现调整控制。本系统在个性化、实用性方面效果显著。
[Abstract]:In recent years, with the continuous improvement of people's living standards and the progress of computer technology and network technology, more and more smart home products have emerged. They not only have many functions of traditional home, but also pay more attention to the personal experience of users, so that people live more comfortable and convenient. At present, the mainstream smart home products are based on the Internet of things technology as a variety of monitoring products, for indoor thermal comfort products are almost no. The reason is that the calculation of the parameters of PMV (PredictedMeanVote) thermal comfort index is complicated, the environmental factors involved are many and the data collection is very inconvenient. And the existing research for each user's own situation is also relatively few, so often the practical effect is not ideal. In order to solve the problem of thermal comfort calculation, this paper simulates and studies the calculation method of PMV thermal comfort index, and proposes an improved particle swarm optimization algorithm with constraints, aiming at the lack of constraints on variables in the optimization of standard particle swarm optimization (PSO). An improved particle swarm optimization algorithm with constraints is used to solve the PMV equation. Secondly, a indoor thermal comfort system based on smart phone is designed. It includes Android application and data acquisition terminal based on nRF51822 low power Bluetooth chip. The system sends the environment data to Android through Bluetooth. In Android application, the metabolism rate of human body is estimated by recording the number of walking steps per unit time, and the thermal resistance of clothes is obtained by setting the user in the application. Particle swarm optimization (PSO) is transplanted to Android application to complete PMV calculation and control decision. In order to make the indoor environment satisfy the human body's requirement for comfort, taking the PMV equal to zero as the goal, solving the given value of air temperature and air velocity which makes people feel the most comfortable, the adjustment control is realized by sending infrared to the air conditioning equipment. This system has remarkable effect in personalization and practicability.
【学位授予单位】：北方工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TU855;TP391.44;TN929.5

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