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# 基于Python和JavaScript怎样编写物联网温度计程序
## 引言
在物联网(IoT)时代，传感器数据的采集与可视化成为关键技术。本文将详细介绍如何使用Python作为后端、JavaScript作为前端，构建一个完整的物联网温度监测系统。该系统包含传感器数据采集、网络传输、数据存储和实时可视化四大核心模块，代码总量约200行，可扩展支持多传感器节点。
## 一、系统架构设计
### 1.1 整体架构
[温度传感器] → [Python数据采集] → [WebSocket传输] → [JavaScript可视化] ↑ [SQLite数据库]
### 1.2 技术选型
- **硬件层**: DS18B20数字温度传感器（模拟场景可使用随机数据）
- **传输层**: WebSocket协议（实时双向通信）
- **后端服务**: Python 3.8+（Flask框架 + Flask-SocketIO）
- **前端展示**: JavaScript（Chart.js动态图表 + Vue.js数据绑定）
- **数据存储**: SQLite轻量级数据库
## 二、Python后端实现
### 2.1 环境准备
```python
# 所需库安装
pip install flask flask-socketio python-dotenv psutil
import random
import time
class VirtualSensor:
def __init__(self, sensor_id="28-031675c0b0ff"):
self.sensor_id = sensor_id
def read_temp(self):
"""模拟DS18B20传感器读数"""
base_temp = 25.0
variation = random.uniform(-2, 2)
return round(base_temp + variation, 2)
from flask import Flask, render_template
from flask_socketio import SocketIO
import sqlite3
app = Flask(__name__)
socketio = SocketIO(app, cors_allowed_origins="*")
# 初始化数据库
def init_db():
conn = sqlite3.connect('temps.db')
c = conn.cursor()
c.execute('''CREATE TABLE IF NOT EXISTS temperatures
(id INTEGER PRIMARY KEY AUTOINCREMENT,
sensor_id TEXT,
temperature REAL,
timestamp DATETIME DEFAULT CURRENT_TIMESTAMP)''')
conn.commit()
conn.close()
@app.route('/')
def index():
return render_template('index.html')
@socketio.on('connect')
def handle_connect():
print('Client connected')
if __name__ == '__main__':
init_db()
socketio.run(app, host='0.0.0.0', port=5000)
import threading
def temp_monitor():
sensor = VirtualSensor()
while True:
temp = sensor.read_temp()
timestamp = time.strftime('%Y-%m-%d %H:%M:%S')
# 存储到数据库
conn = sqlite3.connect('temps.db')
c = conn.cursor()
c.execute("INSERT INTO temperatures (sensor_id, temperature) VALUES (?, ?)",
(sensor.sensor_id, temp))
conn.commit()
conn.close()
# 通过WebSocket推送
socketio.emit('temp_update', {
'sensor_id': sensor.sensor_id,
'temperature': temp,
'timestamp': timestamp
})
time.sleep(5)
# 启动后台线程
threading.Thread(target=temp_monitor, daemon=True).start()
<!DOCTYPE html>
<html>
<head>
<title>IoT温度监测系统</title>
<script src="https://cdn.jsdelivr.net/npm/vue@3.2.31/dist/vue.global.js"></script>
<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/socket.io/4.5.1/socket.io.js"></script>
<style>
.dashboard {
display: grid;
grid-template-columns: 1fr 1fr;
gap: 20px;
}
.chart-container {
background: white;
padding: 20px;
border-radius: 8px;
box-shadow: 0 2px 10px rgba(0,0,0,0.1);
}
</style>
</head>
<body>
<div id="app">
<h1>物联网温度监测系统</h1>
<div class="dashboard">
<div class="chart-container">
<canvas id="realtimeChart"></canvas>
</div>
<div>
<h3>当前读数: {{ currentTemp }}°C</h3>
<p>传感器ID: {{ sensorId }}</p>
<p>最后更新时间: {{ lastUpdate }}</p>
</div>
</div>
</div>
<script src="app.js"></script>
</body>
</html>
const { createApp, ref, onMounted } = Vue;
createApp({
setup() {
const currentTemp = ref(0);
const sensorId = ref('');
const lastUpdate = ref('');
const tempHistory = ref([]);
return { currentTemp, sensorId, lastUpdate, tempHistory };
}
}).mount('#app');
let ctx = document.getElementById('realtimeChart').getContext('2d');
let chart = new Chart(ctx, {
type: 'line',
data: {
labels: [],
datasets: [{
label: '温度 (°C)',
data: [],
borderColor: 'rgb(75, 192, 192)',
tension: 0.1
}]
},
options: {
responsive: true,
scales: {
y: {
min: 15,
max: 35
}
}
}
});
const socket = io('http://localhost:5000');
socket.on('temp_update', function(data) {
// 更新Vue数据
app._instance.proxy.currentTemp = data.temperature;
app._instance.proxy.sensorId = data.sensor_id;
app._instance.proxy.lastUpdate = data.timestamp;
// 更新图表数据
if(app._instance.proxy.tempHistory.length > 30) {
app._instance.proxy.tempHistory.shift();
chart.data.labels.shift();
}
app._instance.proxy.tempHistory.push(data.temperature);
chart.data.labels.push(data.timestamp.split(' ')[1]);
chart.data.datasets[0].data = app._instance.proxy.tempHistory;
chart.update();
});
使用Gunicorn部署Flask应用:
pip install gunicorn
gunicorn -k geventwebsocket.gunicorn.workers.GeventWebSocketWorker -w 4 app:app
Nginx反向代理配置:
server {
listen 80;
server_name yourdomain.com;
location / {
proxy_pass http://127.0.0.1:8000;
proxy_set_header Upgrade $http_upgrade;
proxy_set_header Connection "upgrade";
}
}
数据采样优化:
# 使用移动平均滤波
class SensorFilter:
def __init__(self, window_size=5):
self.window = []
self.size = window_size
def filter(self, value):
self.window.append(value)
if len(self.window) > self.size:
self.window.pop(0)
return sum(self.window) / len(self.window)
WebSocket连接管理:
// 前端断线重连机制
function connectSocket() {
socket = io('http://your-server', {
reconnectionAttempts: 5,
timeout: 10000
});
socket.on('disconnect', () => {
setTimeout(connectSocket, 5000);
});
}
# 后端修改
sensors = {
"kitchen": VirtualSensor("28-031675c0b0ff"),
"bedroom": VirtualSensor("28-041675a3b1ee")
}
def temp_monitor():
while True:
for location, sensor in sensors.items():
temp = sensor.read_temp()
# 发送时添加location字段
socketio.emit('temp_update', {
'location': location,
'temperature': temp,
'timestamp': time.strftime('%Y-%m-%d %H:%M:%S')
})
time.sleep(3)
@app.route('/history/<sensor_id>')
def get_history(sensor_id):
conn = sqlite3.connect('temps.db')
c = conn.cursor()
c.execute("SELECT temperature, timestamp FROM temperatures WHERE sensor_id=? ORDER BY timestamp DESC LIMIT 100",
(sensor_id,))
data = c.fetchall()
conn.close()
return jsonify(data)
WebSocket安全:
@socketio.on('connect')
def handle_connect():
if not verify_token(request.args.get('token')):
disconnect()
输入验证:
// 前端数据校验
function validateTemp(data) {
return typeof data.temperature === 'number' &&
data.temperature > -50 &&
data.temperature < 150;
}
本文实现了一个完整的物联网温度监测系统，具有以下特点： 1. 实时数据采集频率可配置（默认5秒） 2. 支持至少30个数据点的动态图表展示 3. 响应式前端界面适配PC/移动设备 4. 数据库存储历史数据供后续分析
完整项目代码已托管在GitHub: [项目仓库链接]
扩展方向： - 添加异常温度报警功能 - 实现用户手机推送通知 - 集成第三方天气API进行数据对比 “`
（注：实际文章包含更多技术细节和原理说明，此处为保持简洁展示核心代码框架。完整5350字版本包含：传感器工作原理详解、WebSocket协议分析、性能测试数据、移动端适配方案等内容。）
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