236 lines
7.5 KiB
Python
236 lines
7.5 KiB
Python
import pandas as pd
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import pymysql
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from datetime import datetime, timedelta
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class DatabaseConnector:
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def __init__(self, rtk_host='localhost', rtk_user='root', rtk_password='root', rtk_database='rtk_data',
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rain_host='8.134.185.53', rain_user='remote', rain_password='root', rain_database='rain_db'):
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"""
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初始化数据库连接器
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"""
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# RTK数据库配置
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self.rtk_host = 'localhost'
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self.rtk_user = 'root'
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self.rtk_password = 'root'
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self.rtk_database = 'rtk_data'
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self.rtk_connection = None
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# 气象数据库配置
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self.rain_host = '8.134.185.53'
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self.rain_user = 'remote'
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self.rain_password = 'root'
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self.rain_database = 'rain_db'
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self.rain_connection = None
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def connect_rtk(self):
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"""
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建立RTK数据库连接
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"""
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try:
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self.rtk_connection = pymysql.connect(
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host=self.rtk_host,
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user=self.rtk_user,
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password=self.rtk_password,
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database=self.rtk_database
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)
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return True
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except Exception as e:
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print(f"RTK数据库连接失败: {str(e)}")
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return False
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def connect_rain(self):
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"""
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建立气象数据库连接
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"""
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try:
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self.rain_connection = pymysql.connect(
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host=self.rain_host,
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user=self.rain_user,
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password=self.rain_password,
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database=self.rain_database
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)
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return True
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except Exception as e:
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print(f"气象数据库连接失败: {str(e)}")
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return False
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def disconnect(self):
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"""
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关闭所有数据库连接
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"""
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if self.rtk_connection:
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self.rtk_connection.close()
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self.rtk_connection = None
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if self.rain_connection:
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self.rain_connection.close()
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self.rain_connection = None
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def get_latest_weather_data(self):
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"""
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获取最新的气象数据，合并RTK和气象数据的最新记录
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使用RTK数据的时间作为基准时间
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"""
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# 获取RTK数据
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if not self.rtk_connection:
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if not self.connect_rtk():
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return None
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try:
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# 获取RTK数据最新记录
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rtk_query = """
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SELECT
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timestamp,
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pwv,
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ztd,
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ztd_gradient_east,
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ztd_gradient_north
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FROM rtk_data
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ORDER BY timestamp DESC
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LIMIT 1
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"""
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rtk_df = pd.read_sql(rtk_query, self.rtk_connection)
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if rtk_df.empty:
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print("RTK数据库中没有数据")
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return None
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# 获取气象数据最新记录
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if not self.rain_connection:
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if not self.connect_rain():
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print("无法连接到气象数据库")
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return rtk_df
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rain_query = """
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SELECT
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wind_speed,
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wind_force,
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wind_direction_360 as wind_direction,
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humidity,
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temperature,
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atm_pressure,
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solar_radiation,
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rainfall
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FROM sensor_data
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ORDER BY timestamp DESC
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LIMIT 1
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"""
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rain_df = pd.read_sql(rain_query, self.rain_connection)
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if rain_df.empty:
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print("气象数据库中没有数据")
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return rtk_df
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# 重命名RTK数据列
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rtk_df = rtk_df.rename(columns={
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'timestamp': 'datetime',
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'pwv': 'PWV',
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'ztd': 'ZTD'
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})
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# 重命名气象数据列
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rain_df = rain_df.rename(columns={
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'humidity': 'rh',
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'temperature': 'Temp',
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'atm_pressure': 'Press',
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'wind_speed': 'ws'
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})
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# 将RTK的时间戳转换为datetime类型
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rtk_df['datetime'] = pd.to_datetime(rtk_df['datetime'])
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# 合并数据，使用RTK的时间
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merged_df = pd.concat([rtk_df, rain_df], axis=1)
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# 删除重复的列（如果有的话）
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merged_df = merged_df.loc[:,~merged_df.columns.duplicated()]
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return merged_df
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except Exception as e:
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print(f"获取数据失败: {str(e)}")
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return None
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def get_historical_data(self, start_date, end_date):
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"""
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获取历史数据，合并RTK和气象数据
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"""
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# 获取RTK数据
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if not self.rtk_connection:
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if not self.connect_rtk():
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return None
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try:
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# 获取RTK数据
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rtk_query = """
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SELECT
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timestamp,
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pwv,
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ztd,
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ztd_gradient_east,
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ztd_gradient_north
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FROM rtk_data
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WHERE timestamp BETWEEN %s AND %s
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ORDER BY timestamp
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"""
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rtk_df = pd.read_sql(rtk_query, self.rtk_connection, params=[start_date, end_date])
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# 重命名RTK数据列
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rtk_df = rtk_df.rename(columns={
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'timestamp': 'datetime',
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'pwv': 'PWV',
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'ztd': 'ZTD'
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})
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# 获取气象数据
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if not self.rain_connection:
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if not self.connect_rain():
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return rtk_df # 如果气象数据库连接失败，返回RTK数据
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rain_query = """
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SELECT
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timestamp,
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wind_speed,
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wind_force,
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wind_direction_360 as wind_direction,
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humidity,
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temperature,
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atm_pressure,
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solar_radiation,
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rainfall
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FROM sensor_data
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WHERE timestamp BETWEEN %s AND %s
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ORDER BY timestamp
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"""
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rain_df = pd.read_sql(rain_query, self.rain_connection, params=[start_date, end_date])
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# 重命名气象数据列
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rain_df = rain_df.rename(columns={
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'timestamp': 'datetime',
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'humidity': 'rh',
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'temperature': 'Temp',
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'atm_pressure': 'Press',
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'wind_speed': 'ws'
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})
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# 合并数据
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if not rain_df.empty:
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# 将时间戳转换为datetime类型
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rtk_df['datetime'] = pd.to_datetime(rtk_df['datetime'])
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rain_df['datetime'] = pd.to_datetime(rain_df['datetime'])
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# 合并数据
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merged_df = pd.merge(rtk_df, rain_df, on='datetime', how='outer')
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# 按时间排序
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merged_df = merged_df.sort_values('datetime')
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# 填充缺失值
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merged_df = merged_df.fillna(method='ffill')
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return merged_df
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else:
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return rtk_df
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except Exception as e:
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print(f"获取历史数据失败: {str(e)}")
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return None |