智能水肥机控制和实时运行数据

通过智能水肥机的实时控制数据和运行上报数据可以远程查看和控制水肥机的运行状态、运行数据、运行记录、累计用量等数据，辅助农户对农作物进行灌溉施肥管理、种植数据记录和种植方案研判等农事活动。主要应用于蔬菜、水果等农产品的大棚种植场景，通过智能水肥一体化首部和末端，进行精准地配肥、施肥、喷药、酸碱调解、灌水等工作。1.数据采集：通过GPRS和Wifi通讯技术、嵌入式软件系统、Lora通讯技术、及PH计、EC计、涡轮流量计等传感器技术，获取智能水肥机的运行模式、实时浇水量、累计浇水量、实时施肥量、累计施肥量、运行时间、施肥比例、施肥PH、施肥EC、轮罐区状态等信息。 2.数据处理：对采集到的原始数据进行处理，去除重复和缺失等异常数据后，将数据以一定数据结构和记录时间存储起来。 3.数据分析：通过对历史浇水量和施肥量数据进行时间序列分析，来预测未来浇水量和施肥量。未来浇水量和施肥量的预测方法为：未来浇水量 = k* 当前累计浇水量 + (1 - k) * 上期预测浇水量；未来施肥量 = k* 当前累计施肥量 + (1 - k) * 上期预测施肥量，其中，k为平滑系数，范围在0到1之间。上期预测浇水量和上期预测施肥量分别为根据上期实际浇水量和施肥量通过平滑计算得到的预测值。4.数据应用：基于水肥机的实时数据，提醒农户水肥机的运行状态并提供远程运行控制，帮助农民更高效进行农事管理；帮助农户制定更精准、更科学的作物种植方案；基于施肥的PH和EC数据辅助农户调节土地的酸碱性和肥力，以适应不同农作物的精确种植

This dataset is constructed using real-time control data and operation report data from intelligent water and fertilizer machines, allowing remote viewing and control of the machines' operating status, operational data, operation records, cumulative usage and other related metrics, to assist farmers in conducting agricultural activities such as crop irrigation and fertilization management, planting data recording and planting plan deliberation. It is primarily applied to greenhouse planting scenarios for agricultural products like vegetables and fruits, enabling precise operations including fertilizer formulation, fertilization, pesticide spraying, pH adjustment and irrigation via intelligent integrated water and fertilizer front-end and terminal devices. 1. Data Collection: Information such as the operating mode, real-time irrigation water volume, cumulative irrigation water volume, real-time fertilizer application amount, cumulative fertilizer application amount, operating duration, fertilizer application ratio, fertilizer application pH, fertilizer application EC and tank rotation zone status of the intelligent water and fertilizer machines is collected through communication technologies including GPRS, Wi-Fi and LoRa, embedded software systems, and sensor technologies such as pH meters, EC meters and turbine flowmeters. 2. Data Processing: The collected raw data is processed, with abnormal data such as duplicates and missing values removed, and then stored in a predefined data structure along with recording timestamps. 3. Data Analysis: Time series analysis is performed on historical irrigation water and fertilizer application data to predict future irrigation water demand and fertilizer application amount. The prediction formulas are: Future irrigation water demand = k * current cumulative irrigation water volume + (1 - k) * previous predicted irrigation water demand; Future fertilizer application amount = k * current cumulative fertilizer application amount + (1 - k) * previous predicted fertilizer application amount, where k is the smoothing coefficient ranging from 0 to 1. The previous predicted irrigation water demand and fertilizer application amount are the predicted values calculated via smoothing based on the actual irrigation water volume and fertilizer application amount of the previous period respectively. 4. Data Application: Based on the real-time data of the water and fertilizer machines, farmers are alerted to the machines' operating status and provided with remote operation control, helping them carry out agricultural management more efficiently; assisting farmers in developing more precise and scientific crop planting plans; and aiding farmers in adjusting soil pH and fertility using the pH and EC data from fertilizer application to meet the precise planting requirements of different crops.