智能家居环境数据监测系统的研究

发布时间：2018-10-21 18:12

【摘要】：随着现代科学技术的迅猛发展和人们对生活品质要求的提升,室内居住环境的安全、健康以及舒适等问题受到人们越来越多的关注,人们的健康状况最直接的影响因子就是室内居住环境质量的好坏。本文根据智能家居的概念,分析了室内影响人们安全和健康的环境因子。比如,由于人们粗心而导致火灾而引起的安全问题和不适宜的室内光照度造成人体不适的健康问题等。针对以上的家居环境问题,本文设计了有助于人们实时了解家居环境数据信息的监测系统,采用了集神经网络和模糊系统两者优点的模糊神经网络模型对家居环境进行等级评价。本文主要进行了以下几点的研究:(1)结合家居环境存在的问题,分析出系统的总体需求:实时监测家居环境数据进行环境评价,为改善家居环境打下基础;并提出相应的解决方案:第一步:对从传感器采集的数据进行数据清洗、归一化处理;第二步:T-S模糊神经网络模型对处理好的数据进行训练和有效性检验,得到环境评价等级,实现对家居环境的评价。(2)研究了T-S模糊神经网络。模糊系统具有定性或者模糊表达知识的优点,神经网络具有自适应的学习能力和容错能力,本文结合两者的优点,建立了T-S模糊神经网络家居环境评价模型,并进行了网络训练,实验表明,T-S模糊神经网络模型比BP神经网络模型、模糊理论模型更优。最后用一个实例证明了T-S模糊神经网络模型的有效性。(3)实现了智能家居环境数据监测和评价。根据家居环境影响因子,进行传感器、协调器等硬件设计;然后又设计了系统的软件,并用VB开发了环境数据监测界面。在界面中,用户可以实时监测到各个环境因子的采样值、家居环境评价等级。
[Abstract]:With the rapid development of modern science and technology and the improvement of people's quality of life, people pay more and more attention to the safety, health and comfort of indoor living environment. The most direct influence factor of people's health condition is the quality of indoor living environment. Based on the concept of smart home, this paper analyzes the indoor environmental factors that affect people's safety and health. For example, the safety problems caused by fire caused by carelessness and the health problems caused by inappropriate indoor illumination caused by human discomfort and so on. In view of the above problems of home environment, this paper designs a monitoring system that can help people to understand the household environment data information in real time. The fuzzy neural network model, which combines the advantages of neural network and fuzzy system, is used to evaluate the household environment. This paper mainly carried out the following research: (1) combined with the problems existing in the home environment, analyze the overall needs of the system: real-time monitoring of the household environment data for environmental evaluation, for the improvement of the home environment lay the foundation; At the same time, the corresponding solutions are put forward: the first step: data cleaning and normalized processing of the data collected from the sensor; the second step: the T-S fuzzy neural network model trains and validates the processed data, and obtains the environmental evaluation grade. The evaluation of home environment is realized. (2) T-S fuzzy neural network is studied. Fuzzy system has the advantages of qualitative or fuzzy expression knowledge, and neural network has adaptive learning ability and fault-tolerant ability. In this paper, a T-S fuzzy neural network home environment evaluation model is established. The experiment shows that the T-S fuzzy neural network model is better than the BP neural network model and the fuzzy theory model is better than the T-S fuzzy neural network model. Finally, an example is given to prove the validity of T-S fuzzy neural network model. (3) the intelligent home environment data monitoring and evaluation are realized. According to the influence factors of home environment, the hardware design of sensor and coordinator is carried out, and the software of the system is designed, and the environmental data monitoring interface is developed with VB. In the interface, the user can monitor the sampling value of each environmental factor in real time, and the level of home environment evaluation.
【学位授予单位】：杭州电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP274;TU855

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