import streamlit as st
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import plotly.express as px
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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.data_processing_service import DataProcessingService
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from app.services.extruder_service import ExtruderService
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# 设置页面配置
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st.set_page_config(
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page_title="数据分析系统",
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page_icon="📊",
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layout="wide"
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)
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# 左侧菜单导航
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st.sidebar.title("系统导航")
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menu = st.sidebar.radio(
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"选择分析模块",
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["分拣磅秤", "挤出机"],
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index=0,
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key="main_menu"
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)
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# 右侧内容区域
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if menu == "分拣磅秤":
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# 初始化服务
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query_service = DataQueryService()
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processing_service = DataProcessingService()
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# 页面标题
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st.title("分拣磅秤数据分析")
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# 查询条件区域（迁移到右侧顶部）
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st.subheader("查询配置")
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col1, col2, col3 = st.columns([2, 2, 1])
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with col1:
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start_date = st.date_input("开始日期", datetime.now() - timedelta(days=7), key="sorting_start_date")
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with col2:
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end_date = st.date_input("结束日期", datetime.now(), key="sorting_end_date")
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with col3:
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st.write("") # 占位
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query_button = st.button("查询数据", key="sorting_query")
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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 = query_service.get_sorting_scale_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['query_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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# 计算统计信息
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stats = processing_service.calculate_statistics(cleaned_data)
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# 分析极值点
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extreme_analysis = processing_service.analyze_extreme_points(cleaned_data)
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extreme_points = extreme_analysis['extreme_points']
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phase_maxima = extreme_analysis['phase_maxima']
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overall_pass_rate = extreme_analysis['overall_pass_rate']
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# 缓存结果
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st.session_state['query_results'] = {
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'cleaned_data': cleaned_data,
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'stats': stats,
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'extreme_points': extreme_points,
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'phase_maxima': phase_maxima,
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'overall_pass_rate': overall_pass_rate
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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", stats.get('total_records', 0))
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with col2:
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st.metric("平均合格数", round(stats.get('count_in_range', {}).get('mean', 0), 2))
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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("整体合格率", f"{overall_pass_rate}%")
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# 显示趋势图
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st.subheader("数据趋势图")
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fig = px.line(
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cleaned_data,
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x='time',
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y=['count_under', 'count_in_range', 'count_over'],
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labels={
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'time': '时间',
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'value': '数量',
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'variable': '数据类型'
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},
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title='分拣磅秤数据趋势',
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color_discrete_map={
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'count_under': 'red',
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'count_in_range': 'green',
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'count_over': 'blue'
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}
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)
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# 自定义图例
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fig.for_each_trace(lambda t: t.update(name={
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'count_under': '低于标准',
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'count_in_range': '在标准范围内',
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'count_over': '高于标准'
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}[t.name]))
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# 配置图表缩放功能
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fig.update_layout(
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xaxis=dict(
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fixedrange=False
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),
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yaxis=dict(
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fixedrange=False
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),
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dragmode='zoom'
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)
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# 配置图表参数
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config = {
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'scrollZoom': True
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}
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# 显示图表
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st.plotly_chart(fig, use_container_width=True, config=config)
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# 显示极值点分析
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st.subheader("极值点分析")
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if not extreme_points.empty:
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# 准备展示数据
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display_df = extreme_points[['time', 'count_under', 'count_in_range', 'count_over', 'pass_rate']].copy()
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# 格式化时间戳
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display_df['time'] = display_df['time'].dt.strftime('%Y-%m-%d %H:%M:%S')
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# 修改列名
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display_df.columns = ['时间戳', '超下限数值', '范围内数值', '超上限数值', '合格率(%)']
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# 显示数据表格
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st.dataframe(display_df, use_container_width=True)
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# 显示极值点数量
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st.info(f"共识别到 {len(extreme_points)} 个极值点")
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# 添加导出功能
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import csv
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import io
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# 创建CSV数据
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csv_buffer = io.StringIO()
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display_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="下载极值点分析结果",
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data=csv_data,
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file_name=f"极值点分析_{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("未识别到极值点")
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# 显示重量趋势图
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st.subheader("重量趋势图")
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if 'weight' in cleaned_data.columns:
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# 创建重量趋势图
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import plotly.graph_objects as go
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# 创建图表
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weight_fig = go.Figure()
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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 cleaned_data.columns for col in ['baseline_value', 'over_difference', 'under_difference'])
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# 计算动态阈值
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if has_thresholds:
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# 复制数据以避免修改原始数据
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threshold_data = cleaned_data.copy()
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# 处理零值
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for col in ['baseline_value', 'over_difference', 'under_difference']:
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threshold_data[col] = threshold_data[col].replace(0, pd.NA)
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# 向前填充缺失值
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threshold_data = threshold_data.ffill()
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# 计算上下限阈值
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threshold_data['upper_threshold'] = threshold_data['over_difference']
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threshold_data['lower_threshold'] = threshold_data['under_difference']
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# 标记超出阈值的点
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threshold_data['is_out_of_range'] = (threshold_data['weight'] > threshold_data['upper_threshold']) | (threshold_data['weight'] < threshold_data['lower_threshold'])
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# 添加基准值线（动态）
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weight_fig.add_trace(go.Scatter(
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x=threshold_data['time'],
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y=threshold_data['baseline_value'],
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name='基准值',
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line=dict(color='green', width=2),
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opacity=0.7
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))
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# 添加上限阈值线（动态）
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weight_fig.add_trace(go.Scatter(
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x=threshold_data['time'],
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y=threshold_data['upper_threshold'],
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name='上限阈值',
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line=dict(color='red', width=2),
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opacity=0.7
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))
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# 添加下限阈值线（动态）
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weight_fig.add_trace(go.Scatter(
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x=threshold_data['time'],
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y=threshold_data['lower_threshold'],
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name='下限阈值',
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line=dict(color='orange', width=2),
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opacity=0.7
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))
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# 分离正常和异常数据点
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normal_data = threshold_data[~threshold_data['is_out_of_range']]
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out_of_range_data = threshold_data[threshold_data['is_out_of_range']]
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# 添加正常重量点
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weight_fig.add_trace(go.Scatter(
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x=normal_data['time'],
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y=normal_data['weight'],
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name='重量 (正常)',
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opacity=0.8,
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mode='markers',
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marker=dict(
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size=4,
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color='blue',
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symbol='circle',
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line=dict(
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width=0,
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color='blue'
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)
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)
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))
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# 添加异常重量点
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if not out_of_range_data.empty:
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weight_fig.add_trace(go.Scatter(
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x=out_of_range_data['time'],
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y=out_of_range_data['weight'],
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name='重量 (异常)',
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opacity=0.8,
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mode='markers',
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marker=dict(
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size=4,
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color='red',
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symbol='triangle-up',
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line=dict(
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width=2,
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color='darkred'
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)
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)
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))
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else:
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# 没有阈值数据，只显示重量趋势
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weight_fig.add_trace(go.Scatter(
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x=cleaned_data['time'],
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y=cleaned_data['weight'],
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name='重量',
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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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st.warning("数据中不包含阈值相关字段，无法显示阈值线和异常警示！")
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# 配置图表布局
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weight_fig.update_layout(
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title='重量随时间变化趋势',
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xaxis_title='时间',
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yaxis_title='重量',
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xaxis=dict(
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rangeslider=dict(
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visible=True
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),
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type='date',
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fixedrange=False
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),
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yaxis=dict(
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fixedrange=False
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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=600,
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dragmode='zoom',
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# 添加自定义工具栏按钮
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updatemenus=[
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dict(
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type="buttons",
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direction="left",
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buttons=list([
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dict(
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args=["visible", [True, True, True, True, True]],
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label="显示全部",
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method="restyle"
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),
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dict(
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args=["visible", [False, False, False, True, True]],
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label="仅显示重量",
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method="restyle"
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),
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dict(
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args=["visible", [True, True, True, False, False]],
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label="仅显示阈值",
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method="restyle"
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),
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dict(
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args=["visible", [True, True, True, True, False]],
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label="显示正常重量",
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method="restyle"
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),
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dict(
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args=["visible", [True, True, True, False, True]],
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label="显示异常重量",
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method="restyle"
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)
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]),
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pad={"r": 10, "t": 10},
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showactive=True,
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x=0.1,
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xanchor="left",
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y=1.1,
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yanchor="top"
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),
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]
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)
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# 配置图表参数
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config = {
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'scrollZoom': True,
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'toImageButtonOptions': {
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'format': 'png',
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'filename': '重量趋势图',
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'height': 600,
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'width': 1000,
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'scale': 1
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}
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}
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# 显示图表
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st.plotly_chart(weight_fig, use_container_width=True, config=config)
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# 显示数据表格
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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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# 显示详细统计信息
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if stats:
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st.subheader("详细统计信息")
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with st.expander("查看详细统计"):
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col_stats1, col_stats2, col_stats3 = st.columns(3)
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with col_stats1:
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st.write("**低于标准**")
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st.write(f"平均值: {round(stats['count_under']['mean'], 2)}")
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st.write(f"总和: {stats['count_under']['sum']}")
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st.write(f"最大值: {stats['count_under']['max']}")
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st.write(f"最小值: {stats['count_under']['min']}")
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with col_stats2:
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st.write("**在标准范围内**")
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st.write(f"平均值: {round(stats['count_in_range']['mean'], 2)}")
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st.write(f"总和: {stats['count_in_range']['sum']}")
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st.write(f"最大值: {stats['count_in_range']['max']}")
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st.write(f"最小值: {stats['count_in_range']['min']}")
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with col_stats3:
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st.write("**高于标准**")
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st.write(f"平均值: {round(stats['count_over']['mean'], 2)}")
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st.write(f"总和: {stats['count_over']['sum']}")
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st.write(f"最大值: {stats['count_over']['max']}")
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st.write(f"最小值: {stats['count_over']['min']}")
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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 'query_service' in locals() and query_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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elif menu == "挤出机":
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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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# 查询条件区域
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st.subheader("查询配置")
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col1, col2, col3 = st.columns([2, 2, 1])
|
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with col1:
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start_date = st.date_input("开始日期", datetime.now() - timedelta(days=7), key="extruder_start_date")
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with col2:
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end_date = st.date_input("结束日期", datetime.now(), key="extruder_end_date")
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with col3:
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st.write("") # 占位
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query_button = st.button("查询数据", key="extruder_query")
|
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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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# 分析参数趋势
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# trends = extruder_service.analyze_parameter_trends(cleaned_data)
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|
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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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# 'trends': trends,
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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()
|
|
# 格式化时间
|
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')
|
|
# 修改列名
|
batch_display.columns = ['批号', '胶料号', '开始时间', '结束时间', '持续时长(分钟)']
|
# 显示数据表格
|
st.dataframe(batch_display, use_container_width=True)
|
else:
|
st.warning("未检测到换批事件")
|
|
# 显示换料操作可视化图表
|
st.subheader("换料操作可视化")
|
if not batch_changes.empty:
|
# 创建换料操作可视化图表
|
import plotly.graph_objects as go
|
|
# 准备图表数据
|
fig = go.Figure()
|
|
# 添加关键参数趋势线
|
fig.add_trace(go.Scatter(
|
x=cleaned_data['time'],
|
y=cleaned_data['screw_speed_actual'],
|
name='实际转速',
|
line=dict(color='blue', width=2),
|
opacity=0.8
|
))
|
|
fig.add_trace(go.Scatter(
|
x=cleaned_data['time'],
|
y=cleaned_data['head_pressure'],
|
name='机头压力',
|
line=dict(color='red', width=2),
|
opacity=0.8,
|
yaxis='y2'
|
))
|
|
fig.add_trace(go.Scatter(
|
x=cleaned_data['time'],
|
y=cleaned_data['extruder_current'],
|
name='挤出机电流',
|
line=dict(color='green', width=2),
|
opacity=0.8,
|
yaxis='y3'
|
))
|
|
fig.add_trace(go.Scatter(
|
x=cleaned_data['time'],
|
y=cleaned_data['metered_weight'],
|
name='米重',
|
line=dict(color='orange', width=2),
|
opacity=0.8,
|
yaxis='y4'
|
))
|
|
# 添加换料事件标记
|
for i, row in batch_changes.iterrows():
|
# 添加垂直线
|
fig.add_shape(
|
type="line",
|
x0=row['start_time'],
|
y0=0,
|
x1=row['start_time'],
|
y1=1,
|
yref="paper",
|
line=dict(
|
color="purple",
|
width=2,
|
dash="dash"
|
)
|
)
|
|
# 添加注释
|
fig.add_annotation(
|
x=row['start_time'],
|
y=1,
|
yref='paper',
|
text=f'换料: {row["compound_code"]}\n批号: {row["batch_id"]}',
|
showarrow=True,
|
arrowhead=1,
|
ax=0,
|
ay=-60
|
)
|
|
# 配置图表布局
|
fig.update_layout(
|
title='换料操作关键参数变化趋势',
|
xaxis_title='时间',
|
xaxis=dict(
|
rangeslider=dict(
|
visible=True
|
),
|
type='date'
|
),
|
yaxis_title='实际转速 (rpm)',
|
yaxis2=dict(
|
title='机头压力 (MPa)',
|
overlaying='y',
|
side='right',
|
position=0.85
|
),
|
yaxis3=dict(
|
title='挤出机电流 (A)',
|
overlaying='y',
|
side='right',
|
position=0.92
|
),
|
yaxis4=dict(
|
title='米重 (kg)',
|
overlaying='y',
|
side='right',
|
position=1
|
),
|
legend=dict(
|
orientation="h",
|
yanchor="bottom",
|
y=1.02,
|
xanchor="right",
|
x=1
|
),
|
hovermode='x unified',
|
height=700
|
)
|
|
# 显示图表
|
st.plotly_chart(fig, use_container_width=True)
|
|
# 添加数据导出功能
|
import csv
|
import io
|
import pandas as pd
|
|
# 准备导出数据
|
export_data = []
|
for i, row in batch_changes.iterrows():
|
# 获取换料前后的数据
|
before_change = cleaned_data[cleaned_data['time'] < row['start_time']].tail(5)
|
after_change = cleaned_data[cleaned_data['time'] >= row['start_time']].head(5)
|
|
# 添加换料事件记录
|
export_data.append({
|
'event_type': '换料事件',
|
'batch_id': row['batch_id'],
|
'compound_code': row['compound_code'],
|
'time': row['start_time'],
|
'screw_speed': '',
|
'head_pressure': '',
|
'extruder_current': '',
|
'metered_weight': ''
|
})
|
|
# 添加换料前数据
|
for _, before_row in before_change.iterrows():
|
export_data.append({
|
'event_type': '换料前',
|
'batch_id': row['batch_id'],
|
'compound_code': row['compound_code'],
|
'time': before_row['time'],
|
'screw_speed': before_row['screw_speed_actual'],
|
'head_pressure': before_row['head_pressure'],
|
'extruder_current': before_row['extruder_current'],
|
'metered_weight': before_row['metered_weight']
|
})
|
|
# 添加换料后数据
|
for _, after_row in after_change.iterrows():
|
export_data.append({
|
'event_type': '换料后',
|
'batch_id': row['batch_id'],
|
'compound_code': row['compound_code'],
|
'time': after_row['time'],
|
'screw_speed': after_row['screw_speed_actual'],
|
'head_pressure': after_row['head_pressure'],
|
'extruder_current': after_row['extruder_current'],
|
'metered_weight': after_row['metered_weight']
|
})
|
|
# 转换为DataFrame
|
export_df = pd.DataFrame(export_data)
|
|
# 创建CSV数据
|
csv_buffer = io.StringIO()
|
export_df.to_csv(csv_buffer, index=False, encoding='utf-8-sig')
|
csv_data = csv_buffer.getvalue()
|
|
# 添加下载按钮
|
st.download_button(
|
label="下载换料操作分析数据",
|
data=csv_data,
|
file_name=f"换料操作分析_{start_date}_{end_date}.csv",
|
mime="text/csv"
|
)
|
else:
|
st.warning("未检测到换批事件，无法生成换料操作图表")
|
|
# 显示原始数据
|
st.subheader("原始数据")
|
st.dataframe(cleaned_data, use_container_width=True)
|
|
# 数据库连接状态
|
st.sidebar.subheader("数据库状态")
|
if 'extruder_service' in locals() and extruder_service.db.is_connected():
|
st.sidebar.success("数据库连接正常")
|
else:
|
st.sidebar.warning("数据库未连接")
|
|
# 页脚
|
st.sidebar.markdown("---")
|
st.sidebar.markdown("© 2026 数据分析系统")
|
|
# 缓存清理机制
|
def clear_cache():
|
"""清理会话缓存，当切换菜单或更新查询条件时调用"""
|
if 'query_results' in st.session_state:
|
del st.session_state['query_results']
|
if 'extruder_results' in st.session_state:
|
del st.session_state['extruder_results']
|
|
# 初始化会话状态
|
if 'query_results' not in st.session_state:
|
st.session_state['query_results'] = None
|
if 'extruder_results' not in st.session_state:
|
st.session_state['extruder_results'] = None
|
|
# 监听菜单切换，清理缓存
|
if 'previous_menu' not in st.session_state:
|
st.session_state['previous_menu'] = menu
|
elif st.session_state['previous_menu'] != menu:
|
clear_cache()
|
st.session_state['previous_menu'] = menu
|
|
# 关闭数据库连接（当应用结束时）
|
def on_app_close():
|
if 'query_service' in locals():
|
query_service.close_connection()
|
if 'extruder_service' in locals():
|
extruder_service.close_connection()
|
|
# 注册应用关闭回调
|
st.session_state['app_close'] = on_app_close
|
