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.data_query_service import DataQueryService
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from app.services.extruder_service import ExtruderService
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from app.services.main_process_service import MainProcessService
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def show_comprehensive_dashboard():
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# 初始化服务
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sorting_service = DataQueryService()
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extruder_service = ExtruderService()
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main_process_service = MainProcessService()
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# 页面标题
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st.title("条重综合分析")
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# 初始化会话状态用于日期同步
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if 'comp_start_date' not in st.session_state:
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st.session_state['comp_start_date'] = datetime.now().date() - timedelta(days=7)
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if 'comp_end_date' not in st.session_state:
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st.session_state['comp_end_date'] = datetime.now().date()
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if 'comp_quick_select' not in st.session_state:
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st.session_state['comp_quick_select'] = "最近7天"
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if 'time_offset' not in st.session_state:
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st.session_state['time_offset'] = 0
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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['comp_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['comp_start_date'] = today
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st.session_state['comp_end_date'] = today
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elif qs == "最近3天":
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st.session_state['comp_start_date'] = today - timedelta(days=3)
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st.session_state['comp_end_date'] = today
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elif qs == "最近7天":
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st.session_state['comp_start_date'] = today - timedelta(days=7)
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st.session_state['comp_end_date'] = today
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elif qs == "最近30天":
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st.session_state['comp_start_date'] = today - timedelta(days=30)
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st.session_state['comp_end_date'] = today
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def on_date_change():
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st.session_state['comp_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['comp_quick_select'] == option else "secondary"
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if st.button(option, key=f"btn_comp_{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="comp_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="comp_end_date",
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on_change=on_date_change
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)
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# 在第二行添加时间偏移配置
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st.markdown("---")
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offset_cols = st.columns([2, 4, 2])
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with offset_cols[0]:
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st.write("⏱️ **生产对齐配置**")
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with offset_cols[1]:
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time_offset = st.slider(
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"挤出/主流程数据向后偏移 (分钟)",
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min_value=0,
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max_value=60,
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value=st.session_state['time_offset'],
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help="由于胎面从挤出到分拣需要时间，将上游数据向后移动，使其与分拣磅秤上的重量数据在时间轴上对齐。"
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)
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st.session_state['time_offset'] = time_offset
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with offset_cols[2]:
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query_button = st.button("🚀 开始分析", key="comp_query", width='stretch')
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# 转换为datetime对象
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start_dt = datetime.combine(start_date, datetime.min.time())
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end_dt = datetime.combine(end_date, datetime.max.time())
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# 查询处理
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if query_button:
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with st.spinner("正在聚合多源数据..."):
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# 获取偏移量
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offset_delta = timedelta(minutes=st.session_state['time_offset'])
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# 1. 获取分拣磅秤数据 (作为基准，不偏移)
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df_sorting = sorting_service.get_sorting_scale_data(start_dt, end_dt)
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# 2. 获取挤出机数据 (应用偏移)
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df_extruder = extruder_service.get_extruder_data(start_dt, end_dt)
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if df_extruder is not None and not df_extruder.empty:
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df_extruder['time'] = df_extruder['time'] + offset_delta
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# 3. 获取主流程控制数据 (应用偏移)
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df_main_speed = main_process_service.get_cutting_setting_data(start_dt, end_dt)
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if df_main_speed is not None and not df_main_speed.empty:
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df_main_speed['time'] = df_main_speed['time'] + offset_delta
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df_temp = main_process_service.get_temperature_control_data(start_dt, end_dt)
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if df_temp is not None and not df_temp.empty:
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df_temp['time'] = df_temp['time'] + offset_delta
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# 检查是否有数据
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has_data = any([
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df_sorting is not None and not df_sorting.empty,
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df_extruder is not None and not df_extruder.empty,
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df_main_speed is not None and not df_main_speed.empty,
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df_temp is not None and not df_temp.empty
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])
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if not has_data:
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st.warning("所选时间段内未找到任何数据，请尝试调整查询条件。")
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return
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# 创建综合分析图表
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fig = go.Figure()
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# 添加分拣磅秤重量 (基图)
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if df_sorting is not None and not df_sorting.empty:
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# 检查是否包含阈值相关字段
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has_thresholds = all(col in df_sorting.columns for col in ['over_difference', 'under_difference'])
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if has_thresholds:
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# 分离正常和异常数据点
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# 复制数据以避免修改原始数据，并处理可能的零值/空值
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plot_df = df_sorting.copy()
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is_out_of_range = (plot_df['weight'] > plot_df['over_difference']) | (plot_df['weight'] < plot_df['under_difference'])
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normal_points = plot_df[~is_out_of_range]
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anomaly_points = plot_df[is_out_of_range]
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# 1.1 正常重量以蓝色数据点显示
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if not normal_points.empty:
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fig.add_trace(go.Scatter(
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x=normal_points['time'],
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y=normal_points['weight'],
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name='分拣重量 (正常)',
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mode='markers',
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marker=dict(size=5, color='blue', opacity=0.7)
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))
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# 1.2 异常重量以红色三角显示
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if not anomaly_points.empty:
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fig.add_trace(go.Scatter(
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x=anomaly_points['time'],
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y=anomaly_points['weight'],
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name='分拣重量 (异常)',
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mode='markers',
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marker=dict(
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size=7,
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color='red',
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symbol='triangle-up',
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line=dict(width=1, color='darkred')
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)
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))
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else:
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# 如果没有阈值，则全部显示为普通蓝色点
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fig.add_trace(go.Scatter(
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x=df_sorting['time'],
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y=df_sorting['weight'],
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name='分拣重量 (kg)',
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mode='markers',
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marker=dict(size=5, color='blue', opacity=0.7)
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))
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# 2. 显示上下限和标准值的曲线
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if 'baseline_value' in df_sorting.columns:
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fig.add_trace(go.Scatter(
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x=df_sorting['time'],
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y=df_sorting['baseline_value'],
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name='基准值',
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mode='lines',
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line=dict(color='green', width=2),
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opacity=0.6
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))
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if 'over_difference' in df_sorting.columns:
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fig.add_trace(go.Scatter(
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x=df_sorting['time'],
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y=df_sorting['over_difference'],
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name='上限阈值',
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mode='lines',
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line=dict(color='red', width=1.5),
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opacity=0.5
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))
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if 'under_difference' in df_sorting.columns:
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fig.add_trace(go.Scatter(
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x=df_sorting['time'],
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y=df_sorting['under_difference'],
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name='下限阈值',
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mode='lines',
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line=dict(color='orange', width=1.5),
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opacity=0.5
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))
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# 添加挤出机米重
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if df_extruder is not None and not df_extruder.empty:
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# fig.add_trace(go.Scatter(
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# x=df_extruder['time'],
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# y=df_extruder['metered_weight'],
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# name='挤出机米重 (Kg/m)',
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# mode='lines',
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# line=dict(color='green', width=1.5),
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# yaxis='y2'
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# ))
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# 添加挤出机实际转速
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fig.add_trace(go.Scatter(
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x=df_extruder['time'],
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y=df_extruder['screw_speed_actual'],
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name='挤出机实际转速 (RPM)',
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mode='lines',
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line=dict(color='orange', width=1.5),
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yaxis='y3'
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))
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# 添加流程主速
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if df_main_speed is not None and not df_main_speed.empty:
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fig.add_trace(go.Scatter(
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x=df_main_speed['time'],
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y=df_main_speed['process_main_speed'],
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name='流程主速 (M/Min)',
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mode='lines',
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line=dict(color='red', width=1.5),
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yaxis='y3' # 共用速度轴
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))
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# 添加裁切计数
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if 'cutting_count' in df_main_speed.columns:
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fig.add_trace(go.Scatter(
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x=df_main_speed['time'],
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y=df_main_speed['cutting_count'],
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name='裁切计数',
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mode='lines',
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line=dict(color='purple', width=1.5),
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yaxis='y5'
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))
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# 添加温度设定值
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if df_temp is not None and not df_temp.empty:
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temp_fields = {
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'nakata_extruder_screw_display_temp': '螺杆显示 (°C)',
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'nakata_extruder_rear_barrel_display_temp': '后机筒显示 (°C)',
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'nakata_extruder_front_barrel_display_temp': '前机筒显示 (°C)',
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'nakata_extruder_head_display_temp': '机头显示 (°C)'
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}
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colors = ['#FF4B4B', '#FF8C00', '#FFD700', '#DA70D6']
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for i, (field, label) in enumerate(temp_fields.items()):
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fig.add_trace(go.Scatter(
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x=df_temp['time'],
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y=df_temp[field],
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name=label,
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mode='lines',
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line=dict(width=1),
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yaxis='y4'
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))
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# 设置多坐标轴布局
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fig.update_layout(
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title='条重综合趋势分析',
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xaxis=dict(
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title='时间',
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rangeslider=dict(visible=True),
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type='date'
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),
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yaxis=dict(
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title='重量 (kg)',
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title_font=dict(color='blue'),
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tickfont=dict(color='blue')
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),
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yaxis2=dict(
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title='米重 (Kg/m)',
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title_font=dict(color='green'),
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tickfont=dict(color='green'),
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overlaying='y',
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side='right'
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),
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yaxis3=dict(
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title='速度 (RPM / M/Min)',
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title_font=dict(color='red'),
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tickfont=dict(color='red'),
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overlaying='y',
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side='right',
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anchor='free',
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position=0.85
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),
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yaxis4=dict(
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title='温度 (°C)',
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title_font=dict(color='purple'),
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tickfont=dict(color='purple'),
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overlaying='y',
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side='left',
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anchor='free',
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position=0.15
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),
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yaxis5=dict(
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title='裁切计数',
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title_font=dict(color='purple'),
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tickfont=dict(color='purple'),
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overlaying='y',
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side='right',
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anchor='free',
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position=0.7
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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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height=700,
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margin=dict(l=100, r=100, t=100, b=100),
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hovermode='x unified'
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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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# 数据摘要
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# st.subheader("📊 数据摘要")
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# summary_cols = st.columns(4)
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# with summary_cols[0]:
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# if df_sorting is not None and not df_sorting.empty:
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# st.metric("平均重量", f"{df_sorting['weight'].mean():.2f} kg")
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# with summary_cols[1]:
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# if df_extruder is not None and not df_extruder.empty:
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# st.metric("平均米重", f"{df_extruder['metered_weight'].mean():.2f} Kg/m")
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# with summary_cols[2]:
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# if df_main_speed is not None and not df_main_speed.empty:
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# st.metric("平均主速", f"{df_main_speed['process_main_speed'].mean():.2f} M/Min")
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# with summary_cols[3]:
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# if df_temp is not None and not df_temp.empty:
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# st.metric("平均螺杆温控", f"{df_temp['nakata_extruder_screw_set_temp'].mean():.1f} °C")
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