import pandas as pd
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from functools import lru_cache
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from app.database.database import DatabaseConnection
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class ExtruderService:
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def __init__(self):
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self.db = DatabaseConnection()
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def get_extruder_data(self, start_date, end_date):
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"""
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查询挤出机数据
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:param start_date: 开始日期
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:param end_date: 结束日期
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:return: 包含挤出机数据的数据框
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"""
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try:
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# 连接数据库
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if not self.db.is_connected():
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if not self.db.connect():
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return None
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connection = self.db.get_connection()
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# SQL查询语句
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query = """
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SELECT
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time,
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compound_code,
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screw_speed_set,
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screw_speed_actual,
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extruder_current,
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head_pressure,
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machine_head_speed,
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compound_code_ref,
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compound_ratio,
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die_ratio,
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color_line,
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buffer_compound,
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spec_name,
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metered_weight
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FROM
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public.aics_extruder_data
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WHERE
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time BETWEEN %s AND %s
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ORDER BY
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time ASC
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"""
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# 执行查询并转换为DataFrame
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df = pd.read_sql(query, connection, params=(start_date, end_date))
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return df
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except Exception as e:
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print(f"查询数据失败: {e}")
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return None
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finally:
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# 注意：这里不关闭连接，以便后续查询复用
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pass
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def detect_batch_changes(self, df):
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"""
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基于胶料号变更检测换批事件
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:param df: 挤出机数据框
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:return: 包含换批事件的数据框
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"""
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if df is None or df.empty:
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return pd.DataFrame()
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try:
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# 复制数据框
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batch_df = df.copy()
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# 检测compound_code变更
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# 将 compound_code 列整体向下偏移一行，用于比较当前行与前一行是否相同
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batch_df['compound_code_shift'] = batch_df['compound_code'].shift(1)
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# 若当前行 compound_code 与前一行不同，则标记为换批（1），否则为 0 ，第一行特殊处理为 0
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batch_df['is_batch_change'] = (batch_df['compound_code'] != batch_df['compound_code_shift']).astype(int)
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batch_df['is_batch_change'].iloc[0] = 0
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# 打印batch_df
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print(batch_df)
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# 提取所有换批事件的索引
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change_indices = batch_df[batch_df['is_batch_change'] == 1].index.tolist()
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# 计算批次信息
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batch_events = []
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# 处理第一个批次（从数据开始到第一次换批）
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if not change_indices:
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# 没有换批事件，整个数据集是一个批次
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if not batch_df.empty:
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start_time = batch_df['time'].iloc[0]
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end_time = batch_df['time'].iloc[-1]
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compound_code = batch_df['compound_code'].iloc[0]
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duration = (end_time - start_time).total_seconds() / 60 # 转换为分钟
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production = batch_df['metered_weight'].sum()
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batch_events.append({
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'batch_id': start_time.strftime('%Y%m%d%H%M%S'),
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'compound_code': compound_code,
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'start_time': start_time,
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'end_time': end_time,
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'duration_minutes': round(duration, 2)
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})
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else:
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# 处理第一个批次
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first_batch_data = batch_df.iloc[:change_indices[0] + 1]
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if not first_batch_data.empty:
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start_time = first_batch_data['time'].iloc[0]
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end_time = first_batch_data['time'].iloc[-1]
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compound_code = first_batch_data['compound_code'].iloc[0]
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duration = (end_time - start_time).total_seconds() / 60 # 转换为分钟
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production = first_batch_data['metered_weight'].sum()
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batch_events.append({
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'batch_id': start_time.strftime('%Y%m%d%H%M%S'),
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'compound_code': compound_code,
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'start_time': start_time,
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'end_time': end_time,
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'duration_minutes': round(duration, 2)
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})
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# 处理中间批次
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for i in range(len(change_indices) - 1):
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batch_data = batch_df.iloc[change_indices[i]:change_indices[i + 1] + 1]
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if not batch_data.empty:
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start_time = batch_data['time'].iloc[0]
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end_time = batch_data['time'].iloc[-1]
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compound_code = batch_data['compound_code'].iloc[0]
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duration = (end_time - start_time).total_seconds() / 60 # 转换为分钟
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production = batch_data['metered_weight'].sum()
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batch_events.append({
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'batch_id': start_time.strftime('%Y%m%d%H%M%S'),
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'compound_code': compound_code,
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'start_time': start_time,
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'end_time': end_time,
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'duration_minutes': round(duration, 2),
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'production_kg': round(production, 2)
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})
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# 处理最后一个批次
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last_batch_data = batch_df.iloc[change_indices[-1]:]
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if not last_batch_data.empty:
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start_time = last_batch_data['time'].iloc[0]
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end_time = last_batch_data['time'].iloc[-1]
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compound_code = last_batch_data['compound_code'].iloc[0]
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duration = (end_time - start_time).total_seconds() / 60 # 转换为分钟
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production = last_batch_data['metered_weight'].sum()
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batch_events.append({
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'batch_id': start_time.strftime('%Y%m%d%H%M%S'),
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'compound_code': compound_code,
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'start_time': start_time,
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'end_time': end_time,
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'duration_minutes': round(duration, 2),
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'production_kg': round(production, 2)
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})
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return pd.DataFrame(batch_events)
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except Exception as e:
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print(f"检测换批事件失败: {e}")
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return pd.DataFrame()
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def analyze_parameter_trends(self, df):
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"""
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分析参数变化趋势
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:param df: 挤出机数据框
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:return: 包含趋势分析结果的字典
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"""
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if df is None or df.empty:
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return {}
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try:
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trends = {
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'screw_speed': {
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'set': df['screw_speed_set'].describe(),
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'actual': df['screw_speed_actual'].describe()
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},
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'extruder_current': df['extruder_current'].describe(),
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'head_pressure': df['head_pressure'].describe(),
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'metered_weight': df['metered_weight'].describe()
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}
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return trends
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except Exception as e:
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print(f"分析参数趋势失败: {e}")
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return {}
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def close_connection(self):
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"""关闭数据库连接"""
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self.db.disconnect()
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