【摘要】：近年來,隨著人們生活水平的不斷提高以及計(jì)算機(jī)技術(shù)和網(wǎng)絡(luò)技術(shù)的進(jìn)步,出現(xiàn)了越來越多的智能家居產(chǎn)品。它們不僅具有很多傳統(tǒng)家居所具有的功能,而且更關(guān)注用戶的個(gè)人體驗(yàn),使人們生活的更舒適、便捷。目前,主流的智能家居產(chǎn)品是以物聯(lián)網(wǎng)技術(shù)為基礎(chǔ)的各種監(jiān)控產(chǎn)品,針對(duì)室內(nèi)熱舒適度的產(chǎn)品卻幾乎沒有。原因是PMV(PredictedMeanVote)熱舒適度指標(biāo)的參數(shù)計(jì)算都是比較復(fù)雜的,涉及的環(huán)境因素多且數(shù)據(jù)采集非常不方便。并且現(xiàn)有的研究中針對(duì)每個(gè)用戶的自身情況考慮的也比較少,所以往往實(shí)用效果都不太理想。為了解決熱舒適度計(jì)算的問題,本文仿真研究了 PMV熱舒適性指標(biāo)的計(jì)算方法,針對(duì)標(biāo)準(zhǔn)粒子群算法尋優(yōu)時(shí)對(duì)變量缺乏約束,提出了一種帶約束的改進(jìn)粒子群算法,利用帶約束的改進(jìn)粒子群算法實(shí)現(xiàn)PMV方程的求解。其次,設(shè)計(jì)了一個(gè)基于智能手機(jī)的室內(nèi)熱舒適度系統(tǒng)。包括Android應(yīng)用和基于nRF51822低功耗藍(lán)牙芯片的數(shù)據(jù)采集端。系統(tǒng)通過藍(lán)牙將環(huán)境數(shù)據(jù)發(fā)送至Android端。在Android應(yīng)用中,通過記錄人單位時(shí)間內(nèi)的走路的步數(shù),估計(jì)人體的新陳代謝率,通過用戶在應(yīng)用中的設(shè)置得到衣服熱阻。將粒子群算法移植到Android應(yīng)用中,完成PMV計(jì)算和控制決策。為了使室內(nèi)環(huán)境滿足人體對(duì)舒適度的要求,以PMV等于零為目標(biāo),求解出使人感到最舒適的空氣溫度和空氣流速給定值,用紅外發(fā)送給空調(diào)設(shè)備實(shí)現(xiàn)調(diào)整控制。本系統(tǒng)在個(gè)性化、實(shí)用性方面效果顯著。
[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.
【學(xué)位授予單位】：北方工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TU855;TP391.44;TN929.5

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