Optimization of Temperature and Humidity Monitoring for Warehouse in the same Geographical Environment

Abstract

The purpose of this study is to solve the problem: in the warehouse temperature and humidity monitoring, due to the wide area and large number of warehouses, a large number of monitoring systems and equipment are arranged, which need to reduce investment and maintenance costs. Considering the same geographical environment, select 1 or 2 typical warehouses to represent the temperature and humidity of this region, so as to reduce the investment of monitoring equipment. We experimented with two regions separately, with 5 warehouses in each region. Temperature and humidity were collected for a period of time, and cluster analysis was carried out based on their maximum value, average value or comprehensive statistics of the two regions. The results show: (1) When clustering according to the maximum temperature and humidity, there are 2 types of significant differences warehouses in the same geographical environment. (2) When clustering according to the mean value of temperature and humidity, there are 2 or 3 types of significant differences warehouses in the same geographical environment, and the clustering result is different from that of clustering according to the maximum value. (3) When clustering according to the maximum value and average value of temperature and humidity, there is no significant difference warehouses in the same geographical environment, which can be classified as the same warehouse. No matter considering the maximum value, average value or the combination of the two statistics, the typical warehouse can be selected to represent the same geographical environment, so as to reduce the monitoring equipment investment and storage management workload.