嵌入式Python实践：在Linux平台上开发环境监测应用

摘要

本教程详细讲解如何基于Python在Linux平台开发环境监测应用，涵盖传感器数据采集、实时处理、可视化展示等全流程，帮助开发者快速构建嵌入式环境监测系统。

1. 系统架构设计

2. 开发环境搭建

2.1 系统要求

• Linux发行版：Raspberry Pi OS
• Python版本：3.8+
• 硬件：Raspberry Pi 4B + DHT11传感器

2.2 安装依赖库

# 安装必要驱动和库sudoapt-getinstallpython3-dev python3-pipsudopip3installAdafruit_DHT matplotlib pandas numpy smtplib

3. 硬件连接

传感器接线图

DHT11 Raspberry Pi VCC → 3.3V (Pin 1) DATA → GPIO4 (Pin 7) GND → GND (Pin 9)

4. 核心代码实现

4.1 数据采集模块 (sensor_reader.py)

importAdafruit_DHTimporttimeimportjsonclassSensorReader:def__init__(self,sensor_type=Adafruit_DHT.DHT11,pin=4):self.sensor=sensor_type self.pin=pin self.data_history=[]defread_data(self):humidity,temperature=Adafruit_DHT.read_retry(self.sensor,self.pin)ifhumidityisnotNoneandtemperatureisnotNone:timestamp=time.strftime("%Y-%m-%d %H:%M:%S")data={"timestamp":timestamp,"temperature":round(temperature,1),"humidity":round(humidity,1)}self.data_history.append(data)returndatareturnNone# 测试代码if__name__=="__main__":reader=SensorReader()for_inrange(3):print(reader.read_data())time.sleep(2)

4.2 数据存储引擎 (data_storage.py)

importsqlite3fromdatetimeimportdatetimeclassDataStorage:def__init__(self,db_path="environment.db"):self.conn=sqlite3.connect(db_path)self._create_table()def_create_table(self):cursor=self.conn.cursor()cursor.execute(''' CREATE TABLE IF NOT EXISTS sensor_data ( id INTEGER PRIMARY KEY, timestamp DATETIME DEFAULT CURRENT_TIMESTAMP, temperature REAL NOT NULL, humidity REAL NOT NULL ) ''')self.conn.commit()definsert_data(self,temperature,humidity):cursor=self.conn.cursor()cursor.execute(''' INSERT INTO sensor_data (timestamp, temperature, humidity) VALUES (datetime('now'), ?, ?) ''',(temperature,humidity))self.conn.commit()defget_last_24h(self):cursor=self.conn.cursor()cursor.execute(''' SELECT timestamp, temperature, humidity FROM sensor_data WHERE timestamp > datetime('now', '-1 day') ''')returncursor.fetchall()# 示例用法if__name__=="__main__":storage=DataStorage()storage.insert_data(25.3,45.2)print(storage.get_last_24h())

4.3 实时可视化 (visualization.py)

importmatplotlib.pyplotaspltimportmatplotlib.animationasanimationfromdata_storageimportDataStorage plt.style.use('dark_background')classLivePlotter:def__init__(self,storage):self.storage=storage self.fig,(self.ax1,self.ax2)=plt.subplots(2,figsize=(10,8))self.fig.suptitle('环境监测实时数据',fontsize=16)defupdate_plot(self,frame):data=self.storage.get_last_24h()timestamps=[row[0]forrowindata]temps=[row[1]forrowindata]humids=[row[2]forrowindata]self.ax1.clear()self.ax2.clear()# 温度图表self.ax1.plot(timestamps,temps,'r-o',linewidth=2)self.ax1.set_ylabel('温度 (°C)',color='white')self.ax1.tick_params(axis='y',labelcolor='white')self.ax1.set_title('温度变化趋势')# 湿度图表self.ax2.plot(timestamps,humids,'b-s',linewidth=2)self.ax2.set_ylabel('湿度 (%)',color='white')self.ax2.tick_params(axis='y',labelcolor='white')self.ax2.set_title('湿度变化趋势')# 格式化X轴foraxin[self.ax1,self.ax2]:ax.tick_params(axis='x',rotation=45)ax.grid(True,linestyle='--',alpha=0.6)plt.tight_layout()defstart(self):ani=animation.FuncAnimation(self.fig,self.update_plot,interval=10000# 10秒更新一次)plt.show()# 启动可视化if__name__=="__main__":storage=DataStorage()plotter=LivePlotter(storage)plotter.start()

5. 系统部署流程

6. 常见问题解决

7. 完整技术图谱