猴头菇生长大棚内环境稳定性监测数据
收藏资源简介：
通过对猴头菇栽培环境大数据的收集，分析在2025年10月1日到26日室外菌房猴头菇生长大棚内的湿度、温度、CO2每一小时,的变换情况，通过变异系数计算，直观展现环境稳定性状况。通过变异系数算法，对温度、湿度、CO₂浓度进行3小时滚动分析，实时评估环境波动情况,，生成稳定性预警系统，提供预防性维护建议。该系统通过数据驱动决策，实现猴头菇栽培环境的精准化、智能化管理，在保障高产优质的同时，显著提升资源利用效率，推动食用菌产业向现代化、可持续化方向发展。1.数据采集：本数据搜集猴头菇生长大棚内环境变化数据，记录了室外菌房从2025年10月1日到26日，每隔一小时湿度，温度，四个角度的CO2浓度的变化情况，总共625条数据，数据按需更新，数据段包括序号、时间、左下CO2/ppm、左上CO2/ppm、右下CO2/ppm、右上CO2/ppm、湿度/%、温度/℃。2.数据处理：数据清洗，去除异常值影响。将四个方向上的CO2浓度计算平均值，储存在平均CO2/ppm这个字段当中。3.算法加工：分别计算湿度、温度、CO2浓度的平均值，这里采用3小时滚动计算，即在t时刻湿度的平均值，是通过t、t-1、t-2时刻的湿度进行计算的平均值。同样的方法计算湿度的标准差、温度和CO2浓度的平均值和标准差；计算变异系数，把湿度、稳定和CO2的浓度的标准差除以平均值代表三个变量的变异系数，用以衡量环境的稳定性。实时评估大棚内环境的稳定性，为精准化栽培管理提供数据支持。
By collecting big data on the cultivation environment of Hericium erinaceus, this study analyzes the hourly changes in humidity, temperature, and CO₂ concentration in the outdoor mushroom cultivation room's Hericium erinaceus growing greenhouse from October 1st to 26th, 2025, and intuitively presents the environmental stability status via coefficient of variation calculation. A 3-hour rolling analysis is conducted on temperature, humidity, and CO₂ concentration using the coefficient of variation algorithm to real-time evaluate environmental fluctuations, generate a stability early warning system, and provide preventive maintenance recommendations. This system realizes precise and intelligent management of the Hericium erinaceus cultivation environment through data-driven decision-making, significantly improving resource utilization efficiency while ensuring high yield and quality, and promoting the modernization and sustainable development of the edible mushroom industry. 1. Data Collection: This dataset collects environmental change data inside the Hericium erinaceus growing greenhouse, recording the hourly changes in humidity, temperature, and CO₂ concentration at four positions (lower left, upper left, lower right, upper right) in the outdoor mushroom cultivation room from October 1st to 26th, 2025, totaling 625 data entries. The data is updated as needed. The dataset fields include serial number, time, lower-left CO₂/ppm, upper-left CO₂/ppm, lower-right CO₂/ppm, upper-right CO₂/ppm, humidity/%, and temperature/℃. 2. Data Processing: Data cleaning is performed to eliminate the impact of outliers. The average CO₂ concentration of the four positions is calculated and stored in the field "average CO₂/ppm". 3. Algorithm Processing: The mean values of humidity, temperature, and CO₂ concentration are calculated separately using a 3-hour rolling calculation method. Specifically, the mean humidity at time t is calculated using the humidity data at times t, t-1, and t-2. The same method is applied to calculate the standard deviation of humidity, as well as the mean values and standard deviations of temperature and CO₂ concentration. The coefficient of variation is then calculated as the standard deviation divided by the mean for the three variables (humidity, temperature, and CO₂ concentration) to measure environmental stability. This enables real-time evaluation of the environmental stability inside the greenhouse, providing data support for precise cultivation management.
