190 lines
7.3 KiB
Python
190 lines
7.3 KiB
Python
from __future__ import annotations
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import json
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from datetime import datetime
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import plotly.express as px
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import streamlit as st
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from services.hotspot import (
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analyze_hotspot_frequency,
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calculate_hotspot_risk_score,
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generate_hotspot_strategies,
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prepare_hotspot_dataset,
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serialise_datetime_columns,
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)
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@st.cache_data(show_spinner=False)
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def _prepare_hotspot_data(df):
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return prepare_hotspot_dataset(df)
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def render_hotspot(accident_records, accident_source_name: str | None) -> None:
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st.header("📍 事故多发路口分析")
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st.markdown("独立分析事故数据，识别高风险路口并生成针对性策略。")
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if accident_records is None:
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st.info("请在左侧上传事故数据并点击“应用数据与筛选”后再执行热点分析。")
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st.markdown(
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"""
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### 📝 支持的数据格式要求：
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- **文件格式**：Excel (.xlsx)
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- **必要字段**：
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- `事故时间`
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- `事故类型`
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- `事故具体地点`
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- `所在街道`
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- `道路类型`
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- `路口路段类型`
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"""
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)
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return
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with st.spinner("正在准备事故热点数据…"):
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hotspot_data = _prepare_hotspot_data(accident_records)
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st.success(f"✅ 成功加载数据：{len(hotspot_data)} 条事故记录")
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metric_cols = st.columns(3)
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with metric_cols[0]:
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st.metric(
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"数据时间范围",
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f"{hotspot_data['事故时间'].min().strftime('%Y-%m-%d')} 至 {hotspot_data['事故时间'].max().strftime('%Y-%m-%d')}",
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)
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with metric_cols[1]:
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st.metric(
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"事故类型分布",
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f"财损: {len(hotspot_data[hotspot_data['事故类型'] == '财损'])}起",
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)
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with metric_cols[2]:
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st.metric("涉及区域", f"{hotspot_data['所在街道'].nunique()}个街道")
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st.subheader("🔧 分析参数设置")
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settings_cols = st.columns(3)
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with settings_cols[0]:
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time_window = st.selectbox(
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"统计时间窗口",
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options=["7D", "15D", "30D"],
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index=0,
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key="hotspot_window",
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)
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with settings_cols[1]:
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min_accidents = st.number_input(
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"最小事故数", min_value=1, max_value=50, value=3, key="hotspot_min_accidents"
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)
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with settings_cols[2]:
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top_n = st.slider("显示热点数量", min_value=3, max_value=20, value=8, key="hotspot_top_n")
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if not st.button("🚀 开始热点分析", type="primary"):
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return
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with st.spinner("正在分析事故热点分布…"):
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hotspots = analyze_hotspot_frequency(hotspot_data, time_window=time_window)
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hotspots = hotspots[hotspots["accident_count"] >= min_accidents]
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if hotspots.empty:
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st.warning("⚠️ 未找到符合条件的事故热点数据，请调整筛选参数。")
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return
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hotspots_with_risk = calculate_hotspot_risk_score(hotspots.head(top_n * 3))
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top_hotspots = hotspots_with_risk.head(top_n)
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st.subheader("📊 事故多发路口排名（前{0}个）".format(top_n))
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display_columns = {
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"accident_count": "累计事故数",
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"recent_count": "近期事故数",
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"trend_ratio": "趋势比例",
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"main_accident_type": "主要类型",
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"main_intersection_type": "路口类型",
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"risk_score": "风险评分",
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"risk_level": "风险等级",
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}
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display_df = top_hotspots[list(display_columns.keys())].rename(columns=display_columns)
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styled_df = display_df.style.format({"趋势比例": "{:.2f}", "风险评分": "{:.1f}"}).background_gradient(
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subset=["风险评分"], cmap="Reds"
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)
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st.dataframe(styled_df, use_container_width=True)
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st.subheader("🎯 针对性策略建议")
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strategies = generate_hotspot_strategies(top_hotspots, time_period="本周")
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for index, strategy_info in enumerate(strategies, start=1):
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message = f"**{index}. {strategy_info['strategy']}**"
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risk_level = strategy_info["risk_level"]
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if risk_level == "高风险":
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st.error(f"🚨 {message}")
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elif risk_level == "中风险":
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st.warning(f"⚠️ {message}")
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else:
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st.info(f"✅ {message}")
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st.subheader("📈 数据分析可视化")
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chart_cols = st.columns(2)
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with chart_cols[0]:
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fig_hotspots = px.bar(
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top_hotspots.head(10),
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x=top_hotspots.head(10).index,
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y=["accident_count", "recent_count"],
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title="事故频次TOP10分布",
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labels={"value": "事故数量", "variable": "类型", "index": "路口名称"},
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barmode="group",
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)
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fig_hotspots.update_layout(xaxis_tickangle=-45)
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st.plotly_chart(fig_hotspots, use_container_width=True)
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with chart_cols[1]:
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risk_distribution = top_hotspots["risk_level"].value_counts()
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fig_risk = px.pie(
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values=risk_distribution.values,
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names=risk_distribution.index,
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title="风险等级分布",
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color_discrete_map={"高风险": "red", "中风险": "orange", "低风险": "green"},
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)
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st.plotly_chart(fig_risk, use_container_width=True)
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st.subheader("💾 数据导出")
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download_cols = st.columns(2)
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with download_cols[0]:
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hotspot_csv = top_hotspots.to_csv().encode("utf-8-sig")
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st.download_button(
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"📥 下载热点数据CSV",
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data=hotspot_csv,
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file_name=f"accident_hotspots_{datetime.now().strftime('%Y%m%d')}.csv",
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mime="text/csv",
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)
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with download_cols[1]:
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serializable = serialise_datetime_columns(
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top_hotspots.reset_index(),
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columns=[col for col in top_hotspots.columns if "time" in col or "date" in col],
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)
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report_payload = {
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"analysis_time": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
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"time_window": time_window,
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"data_source": accident_source_name or "事故数据",
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"total_records": int(len(hotspot_data)),
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"analysis_parameters": {"min_accidents": int(min_accidents), "top_n": int(top_n)},
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"top_hotspots": serializable.to_dict("records"),
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"recommended_strategies": strategies,
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"summary": {
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"high_risk_count": int((top_hotspots["risk_level"] == "高风险").sum()),
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"medium_risk_count": int((top_hotspots["risk_level"] == "中风险").sum()),
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"total_analyzed_locations": int(len(hotspots)),
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"most_dangerous_location": top_hotspots.index[0]
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if len(top_hotspots)
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else "无",
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},
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}
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st.download_button(
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"📄 下载完整分析报告",
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data=json.dumps(report_payload, ensure_ascii=False, indent=2),
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file_name=f"hotspot_analysis_report_{datetime.now().strftime('%Y%m%d_%H%M')}.json",
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mime="application/json",
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)
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with st.expander("📋 查看原始数据预览"):
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preview_cols = ["事故时间", "所在街道", "事故类型", "事故具体地点", "道路类型"]
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preview_df = hotspot_data[preview_cols].copy()
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st.dataframe(preview_df.head(10), use_container_width=True)
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