云平台下智能实验室环境温度智能控制仿真

Simulation Research on Intelligent Temperature Control of Intelligent Laboratory Environment under Cloud Platform

云平台下智能实验室环境温度智能控制方法在温度控制方面的研究中具重要意义。根据实验室环境温度的初始时刻、停止时刻等条件建立实验室环境温度的控制模型,并对其控制模型中室外温度对实验室温度的影响系数、室外环境对实验室环境温度的影响因子和实验室环境控制机构修正系数的值进行分析和计算。利用验室环境温度的任意值对环境一个阶段的平均温进行计算,其平均温度的值处于实验室环境上限温度和下限温度的阈值之间,按照细分的天数进一步对实验室环境平均温度进行计算,得到实验室内部的能量变化,并计算实验室环境每日期望平均温度的值,再将每日时间划分为多个时间片来计算实验室环境期望平均温度值,并分析是否需要对实验室进行加温,最终实现对云平台下实验室环境温度的智能控制。实验结果表明,实验室升温时产生的能量消耗较小,提出方法能够有效且准确的对实验室环境温度进行智能控制。

The intelligent control for laboratory environmental temperature based on the cloud platform is very important on the research of temperature control. According to the initial time and stopping time of laboratory environment temperature, the control model for laboratory environmental temperature was built. And then, the influence coefficient of outdoor temperature on laboratory temperature and the influence factor of outdoor environment on the laboratory environment temperature and the value of correction coefficient of laboratory environmental control mechanism were analyzed and calculated. On this basis, arbitrary value of laboratory environmental temperature was used to calculate the average temperature of one stage of the environment. The value of average temperature was between the threshold of upper limit temperature and the threshold of lower limit temperature of the laboratory environment. According to the number of subdivided days, the average temperature of laboratory environment was further calculated to obtain the energy change inside the laboratory. Meanwhile, the daily expected average temperature value of laboratory environment was calculated, and the time of a day was divided into many time slices to calculate the expected average temperature value of laboratory environment. Finally, we analyzed whether it was necessary to warm the laboratory, so as to achieve intelligent control for the laboratory environment temperature based on the cloud platform. Simulation results show that the energy consumption in temperature rising is sm all, and the proposed method can effectively, accurately and intelligently control the laboratory environment temperature.