复杂的使用先进算法的天气预报播报系统完整代码
时间: 2024-03-18 19:45:45 浏览: 32
以下是一个使用深度学习算法和OpenWeatherMap API实现的天气预报播报系统的完整代码,使用了PyTorch和pyttsx3库:
```python
import requests
import time
import torch
import torch.nn as nn
import numpy as np
import pyttsx3
# 设置OpenWeatherMap API key和城市名
api_key = 'your_api_key'
city = 'Beijing'
# 创建语音合成对象
engine = pyttsx3.init()
# 设置语音合成参数
rate = engine.getProperty('rate') # 语速
engine.setProperty('rate', rate-50)
volume = engine.getProperty('volume') # 音量
engine.setProperty('volume', volume+0.25)
voice = engine.getProperty('voice') # 发音人
engine.setProperty('voice', voice[1].id)
# 定义深度学习模型
class WeatherModel(nn.Module):
def __init__(self):
super(WeatherModel, self).__init__()
self.fc1 = nn.Linear(6, 64)
self.fc2 = nn.Linear(64, 64)
self.fc3 = nn.Linear(64, 1)
self.relu = nn.ReLU()
self.dropout = nn.Dropout(p=0.2)
def forward(self, x):
x = self.fc1(x)
x = self.relu(x)
x = self.dropout(x)
x = self.fc2(x)
x = self.relu(x)
x = self.dropout(x)
x = self.fc3(x)
return x
# 加载模型参数
model = WeatherModel()
model.load_state_dict(torch.load('model.pt'))
model.eval()
# 获取天气预报信息
def get_weather_info(api_key, city):
url = f'http://api.openweathermap.org/data/2.5/weather?q={city}&appid={api_key}'
res = requests.get(url)
data = res.json()
# 解析天气预报信息
weather = data['weather'][0]['description']
temp = data['main']['temp'] - 273.15 # 转换为摄氏度
pressure = data['main']['pressure']
humidity = data['main']['humidity']
wind_speed = data['wind']['speed']
sunrise = time.strftime('%H:%M:%S', time.localtime(data['sys']['sunrise']))
sunset = time.strftime('%H:%M:%S', time.localtime(data['sys']['sunset']))
# 构造天气预报信息数据
data = np.array([[temp, pressure, humidity, wind_speed, 0, 0]])
# 预测PM2.5浓度
with torch.no_grad():
inputs = torch.from_numpy(data).float()
output = model(inputs)
pm25 = output.item()
# 构造天气预报信息字符串
info = f'今天{city}的天气为{weather},气温{temp:.1f}摄氏度,气压{pressure}帕,湿度{humidity}%,风速{wind_speed}米每秒,PM2.5浓度{pm25:.1f}微克每立方米,日出时间{sunrise},日落时间{sunset}'
return info
# 循环播放天气预报信息
while True:
# 获取天气预报信息
weather_info = get_weather_info(api_key, city)
# 播报天气预报信息
engine.say(weather_info)
engine.runAndWait()
# 暂停5分钟
time.sleep(300)
```
需要先安装pyttsx3库、requests库和PyTorch库,可以使用以下命令进行安装:
```
pip install pyttsx3 requests torch
```
同时,需要替换`api_key`为你自己的OpenWeatherMap API key,并根据需要修改`city`和语音合成参数。此外,还需要将训练好的模型参数保存为`model.pt`文件,并确保模型参数与代码在同一目录下。需要注意的是,深度学习模型需要训练好并且具有一定的准确性才能得到有效的天气预报信息。