import streamlit as st
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import plotly.express as px
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import plotly.graph_objects as go
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from datetime import datetime, timedelta
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from app.services.extruder_service import ExtruderService
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from app.services.data_processing_service import DataProcessingService
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# 初始化服务
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extruder_service = ExtruderService()
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processing_service = DataProcessingService()
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def run_extruder_dashboard():
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# 页面标题
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st.title("挤出机数据分析仪表盘")
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# 侧边栏：日期范围选择
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st.sidebar.header("查询配置")
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start_date = st.sidebar.date_input("开始日期", datetime.now() - timedelta(days=7))
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end_date = st.sidebar.date_input("结束日期", datetime.now())
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# 转换为datetime对象（包含时间）
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start_datetime = datetime.combine(start_date, datetime.min.time())
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end_datetime = datetime.combine(end_date, datetime.max.time())
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# 查询按钮
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if st.sidebar.button("查询数据"):
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# 验证日期范围
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if start_datetime > end_datetime:
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st.error("开始日期不能晚于结束日期！")
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else:
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# 显示加载状态
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with st.spinner("正在查询数据..."):
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# 查询数据
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raw_data = extruder_service.get_extruder_data(start_datetime, end_datetime)
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if raw_data is None or raw_data.empty:
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st.warning("未查询到数据，请检查日期范围或数据库连接！")
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else:
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# 清洗数据
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cleaned_data = processing_service.clean_data(raw_data)
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# 检测换批事件
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batch_changes = extruder_service.detect_batch_changes(cleaned_data)
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# 分析参数趋势
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trends = extruder_service.analyze_parameter_trends(cleaned_data)
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# 检测异常
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anomalies = extruder_service.detect_anomalies(cleaned_data)
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# 显示数据概览
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st.subheader("数据概览")
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col1, col2, col3, col4 = st.columns(4)
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with col1:
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st.metric("总记录数", len(cleaned_data))
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with col2:
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st.metric("换批次数", len(batch_changes))
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with col3:
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st.metric("数据时间范围", f"{cleaned_data['time'].min()} 至 {cleaned_data['time'].max()}")
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with col4:
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st.metric("异常数量", len(anomalies))
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# 显示换批分析
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st.subheader("换批分析")
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if not batch_changes.empty:
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# 准备展示数据
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batch_display = batch_changes[['spec_name', 'start_time', 'end_time', 'duration_minutes', 'production_kg']].copy()
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batch_display.columns = ['SPEC编号', '开始时间', '结束时间', '持续时长(分钟)', '生产数量(kg)']
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# 格式化时间
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batch_display['开始时间'] = batch_display['开始时间'].dt.strftime('%Y-%m-%d %H:%M:%S')
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batch_display['结束时间'] = batch_display['结束时间'].dt.strftime('%Y-%m-%d %H:%M:%S')
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# 显示数据表格
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st.dataframe(batch_display, use_container_width=True)
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else:
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st.warning("未检测到换批事件")
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# 显示参数趋势图
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st.subheader("参数趋势图")
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# 螺杆转速趋势
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st.subheader("螺杆转速趋势")
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fig1 = px.line(
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cleaned_data,
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x='time',
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y=['screw_speed_set', 'screw_speed_actual'],
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labels={
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'time': '时间',
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'value': '转速(rpm)',
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'variable': '参数'
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},
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title='螺杆转速趋势',
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color_discrete_map={
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'screw_speed_set': 'blue',
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'screw_speed_actual': 'green'
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}
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)
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fig1.for_each_trace(lambda t: t.update(name={
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'screw_speed_set': '设定转速',
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'screw_speed_actual': '实际转速'
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}[t.name]))
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st.plotly_chart(fig1, use_container_width=True)
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# 挤出机电流趋势
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st.subheader("挤出机电流趋势")
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fig2 = px.line(
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cleaned_data,
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x='time',
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y='extruder_current',
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labels={
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'time': '时间',
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'extruder_current': '电流(A)'
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},
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title='挤出机电流趋势'
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)
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st.plotly_chart(fig2, use_container_width=True)
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# 机头压力趋势
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st.subheader("机头压力趋势")
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fig3 = px.line(
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cleaned_data,
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x='time',
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y='head_pressure',
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labels={
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'time': '时间',
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'head_pressure': '压力(MPa)'
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},
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title='机头压力趋势'
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)
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st.plotly_chart(fig3, use_container_width=True)
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# 米称重量趋势
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st.subheader("米称重量趋势")
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fig4 = px.line(
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cleaned_data,
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x='time',
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y='metered_weight',
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labels={
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'time': '时间',
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'metered_weight': '重量(kg)'
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},
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title='米称重量趋势'
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)
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st.plotly_chart(fig4, use_container_width=True)
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# 显示原始数据
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st.subheader("原始数据")
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st.dataframe(cleaned_data, use_container_width=True)
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# 关闭数据库连接
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def on_app_close():
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extruder_service.close_connection()
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# 注册应用关闭回调
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st.session_state['app_close'] = on_app_close
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