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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import pandas as pd
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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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def show_extruder_dashboard():
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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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# 页面标题
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st.title("挤出机数据分析")
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# 初始化会话状态用于日期同步
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if 'extruder_start_date' not in st.session_state:
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st.session_state['extruder_start_date'] = datetime.now().date() - timedelta(days=7)
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if 'extruder_end_date' not in st.session_state:
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st.session_state['extruder_end_date'] = datetime.now().date()
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if 'extruder_quick_select' not in st.session_state:
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st.session_state['extruder_quick_select'] = "最近7天"
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# 定义回调函数
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def update_dates(qs):
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st.session_state['extruder_quick_select'] = qs
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today = datetime.now().date()
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if qs == "今天":
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st.session_state['extruder_start_date'] = today
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st.session_state['extruder_end_date'] = today
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elif qs == "最近3天":
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st.session_state['extruder_start_date'] = today - timedelta(days=3)
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st.session_state['extruder_end_date'] = today
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elif qs == "最近7天":
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st.session_state['extruder_start_date'] = today - timedelta(days=7)
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st.session_state['extruder_end_date'] = today
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elif qs == "最近30天":
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st.session_state['extruder_start_date'] = today - timedelta(days=30)
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st.session_state['extruder_end_date'] = today
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def on_date_change():
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st.session_state['extruder_quick_select'] = "自定义"
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# 查询条件区域
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with st.expander("🔍 查询配置", expanded=True):
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# 添加自定义 CSS 实现响应式换行
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st.markdown("""
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<style>
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/* 强制列容器换行 */
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[data-testid="stExpander"] [data-testid="column"] {
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flex: 1 1 120px !important;
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min-width: 120px !important;
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}
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/* 针对日期输入框列稍微加宽一点 */
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@media (min-width: 768px) {
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[data-testid="stExpander"] [data-testid="column"]:nth-child(6),
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[data-testid="stExpander"] [data-testid="column"]:nth-child(7) {
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flex: 2 1 180px !important;
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min-width: 180px !important;
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}
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}
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</style>
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""", unsafe_allow_html=True)
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# 创建布局
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cols = st.columns([1, 1, 1, 1, 1, 1.5, 1.5, 1])
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options = ["今天", "最近3天", "最近7天", "最近30天", "自定义"]
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for i, option in enumerate(options):
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with cols[i]:
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# 根据当前选择状态决定按钮类型
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button_type = "primary" if st.session_state['extruder_quick_select'] == option else "secondary"
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if st.button(option, key=f"btn_extruder_{option}", width='stretch', type=button_type):
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update_dates(option)
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st.rerun()
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with cols[5]:
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start_date = st.date_input(
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"开始日期",
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label_visibility="collapsed",
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key="extruder_start_date",
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on_change=on_date_change
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)
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with cols[6]:
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end_date = st.date_input(
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"结束日期",
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label_visibility="collapsed",
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key="extruder_end_date",
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on_change=on_date_change
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)
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with cols[7]:
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query_button = st.button("🚀 查询", key="extruder_query", width='stretch')
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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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# 查询按钮处理
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if query_button:
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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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st.session_state['extruder_results'] = None
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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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# 缓存结果
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st.session_state['extruder_results'] = {
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'cleaned_data': cleaned_data,
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'batch_changes': batch_changes,
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}
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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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# 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[['batch_id', 'compound_code', 'start_time', 'end_time', 'duration_minutes']].copy()
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# 格式化时间
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batch_display['start_time'] = batch_display['start_time'].dt.strftime('%Y-%m-%d %H:%M:%S')
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batch_display['end_time'] = batch_display['end_time'].dt.strftime('%Y-%m-%d %H:%M:%S')
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# 修改列名
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batch_display.columns = ['批号', '胶料号', '开始时间', '结束时间', '持续时长(分钟)']
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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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# 显示换料操作可视化图表
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st.subheader("换料操作可视化")
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if not batch_changes.empty:
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# 创建换料操作可视化图表
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fig = go.Figure()
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# 添加关键参数趋势线
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fig.add_trace(go.Scatter(
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x=cleaned_data['time'],
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y=cleaned_data['screw_speed_actual'],
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name='实际转速',
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line=dict(color='blue', width=2),
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opacity=0.8
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))
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fig.add_trace(go.Scatter(
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x=cleaned_data['time'],
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y=cleaned_data['head_pressure'],
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name='机头压力',
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line=dict(color='red', width=2),
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opacity=0.8,
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yaxis='y2'
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))
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fig.add_trace(go.Scatter(
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x=cleaned_data['time'],
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y=cleaned_data['extruder_current'],
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name='挤出机电流',
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line=dict(color='green', width=2),
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opacity=0.8,
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yaxis='y3'
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))
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fig.add_trace(go.Scatter(
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x=cleaned_data['time'],
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y=cleaned_data['metered_weight'],
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name='米重',
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line=dict(color='orange', width=2),
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opacity=0.8,
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yaxis='y4'
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))
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# 添加换料事件标记
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for i, row in batch_changes.iterrows():
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# 添加垂直线
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fig.add_shape(
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type="line",
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x0=row['start_time'],
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y0=0,
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x1=row['start_time'],
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y1=1,
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yref="paper",
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line=dict(color="purple", width=2, dash="dash")
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)
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# 添加注释
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fig.add_annotation(
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x=row['start_time'],
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y=1,
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yref='paper',
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text=f'换料: {row["compound_code"]}\n批号: {row["batch_id"]}',
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showarrow=True,
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arrowhead=1,
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ax=0,
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ay=-60
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)
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# 配置图表布局
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fig.update_layout(
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title='换料操作关键参数变化趋势',
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xaxis_title='时间',
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xaxis=dict(
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rangeslider=dict(visible=True),
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type='date'
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),
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yaxis_title='实际转速 (rpm)',
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yaxis2=dict(
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title='机头压力 (MPa)',
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overlaying='y',
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side='right',
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position=0.85
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),
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yaxis3=dict(
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title='挤出机电流 (A)',
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overlaying='y',
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side='right',
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position=0.92
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),
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yaxis4=dict(
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title='米重 (kg)',
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overlaying='y',
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side='right',
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position=1
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),
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legend=dict(
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orientation="h",
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yanchor="bottom",
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y=1.02,
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xanchor="right",
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x=1
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),
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hovermode='x unified',
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height=700
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)
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# 显示图表
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st.plotly_chart(fig, width='stretch', config={'scrollZoom': True})
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# 添加数据导出功能
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import io
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# 准备导出数据
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export_data = []
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for i, row in batch_changes.iterrows():
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# 获取换料前后的数据
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before_change = cleaned_data[cleaned_data['time'] < row['start_time']].tail(5)
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after_change = cleaned_data[cleaned_data['time'] >= row['start_time']].head(5)
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# 添加换料事件记录
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export_data.append({
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'event_type': '换料事件',
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'batch_id': row['batch_id'],
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'compound_code': row['compound_code'],
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'time': row['start_time'],
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'screw_speed': '',
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'head_pressure': '',
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'extruder_current': '',
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'metered_weight': ''
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})
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# 添加换料前数据
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for _, before_row in before_change.iterrows():
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export_data.append({
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'event_type': '换料前',
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'batch_id': row['batch_id'],
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'compound_code': row['compound_code'],
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'time': before_row['time'],
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'screw_speed': before_row['screw_speed_actual'],
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'head_pressure': before_row['head_pressure'],
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'extruder_current': before_row['extruder_current'],
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'metered_weight': before_row['metered_weight']
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})
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# 添加换料后数据
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for _, after_row in after_change.iterrows():
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export_data.append({
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'event_type': '换料后',
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'batch_id': row['batch_id'],
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'compound_code': row['compound_code'],
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'time': after_row['time'],
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'screw_speed': after_row['screw_speed_actual'],
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'head_pressure': after_row['head_pressure'],
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'extruder_current': after_row['extruder_current'],
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'metered_weight': after_row['metered_weight']
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})
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# 转换为DataFrame
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export_df = pd.DataFrame(export_data)
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# 创建CSV数据
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csv_buffer = io.StringIO()
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export_df.to_csv(csv_buffer, index=False, encoding='utf-8-sig')
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csv_data = csv_buffer.getvalue()
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# 添加下载按钮
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st.download_button(
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label="下载换料操作分析数据",
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data=csv_data,
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file_name=f"换料操作分析_{start_date}_{end_date}.csv",
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mime="text/csv"
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)
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else:
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st.warning("未检测到换批事件，无法生成换料操作图表")
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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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# 数据库连接状态
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st.sidebar.subheader("数据库状态")
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if extruder_service.db.is_connected():
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st.sidebar.success("数据库连接正常")
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else:
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st.sidebar.warning("数据库未连接")
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if __name__ == "__main__":
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show_extruder_dashboard()
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