How to Build a Greenhouse Auto‑Control System in Python

In the wave of smart‑application development, the ability to translate real‑world requirements into executable code is essential. The article presents a typical programming task: creating a greenhouse automatic control system that processes temperature and humidity data, stores them in a fixed‑size queue, and triggers adjustments when thresholds are exceeded.

Problem Description

The system must accept a sequence of (temperature, humidity) tuples, keep the most recent values in a queue (removing the oldest when a new entry arrives), and output “需要调节” ("needs adjustment") whenever temperature > 30°C or humidity > 80%.

Solution Overview

The author chooses Python’s collections.deque as the underlying data structure because it provides O(1) append and pop operations at both ends, making it ideal for a sliding window of sensor readings. An object‑oriented design encapsulates all functionality in a GreenhouseControlSystem class.

Code Implementation

from collections import deque

class GreenhouseControlSystem:
    def __init__(self, max_length=5):
        self.data_queue = deque(maxlen=max_length)

    def add_data(self, temperature, humidity):
        self.data_queue.append((temperature, humidity))
        print(f"添加数据：温度={temperature}，湿度={humidity}")
        self.check_control(temperature, humidity)

    def check_control(self, temperature, humidity):
        if temperature > 30 or humidity > 80:
            print("需要调节")
        else:
            print("不需要调节")

    def simulate_process(self, data_list):
        for temperature, humidity in data_list:
            self.add_data(temperature, humidity)

data_list = [(28, 70), (32, 65), (29, 85), (25, 60), (33, 78)]
control_system = GreenhouseControlSystem()
control_system.simulate_process(data_list)

Code Walk‑through

Data Structure Choice : Using deque enables efficient addition and automatic removal of the oldest entry when the maximum length is reached.

Class Design : GreenhouseControlSystem groups related behavior, illustrating object‑oriented principles.

Method Breakdown : __init__: Initializes the queue with a configurable max_length. add_data: Appends a new reading, prints the received values, and calls check_control. check_control: Implements the threshold logic (temperature > 30 °C or humidity > 80 %). simulate_process: Iterates over a list of readings to demonstrate batch processing.

Control Logic : A simple conditional decides whether adjustment is needed, directly reflecting the problem’s requirements.

Simulation Process : The simulate_process method shows how the system would handle a stream of sensor data in practice.

Programming Tips

Parameterized Design : The queue length is exposed via the max_length parameter, allowing easy adaptation to different sensor buffer sizes.

Method Decoupling : Separating data insertion from control checking improves readability and maintainability.

Informative Output : Print statements provide immediate feedback on system state, useful for debugging and monitoring.

Solving smart‑application programming problems like this requires a blend of fundamental programming knowledge, appropriate data‑structure selection, algorithmic thinking, and an understanding of the real‑world scenario being modeled.