diff --git a/.DS_Store b/.DS_Store
new file mode 100644
index 0000000..129a0d4
Binary files /dev/null and b/.DS_Store differ
diff --git a/app.py b/app.py
index f9eef83..2c3905b 100644
--- a/app.py
+++ b/app.py
@@ -548,6 +548,177 @@ def evaluate_models(series: pd.Series,
.round(3))
+import re
+from collections import Counter
+import jieba
+
+def parse_and_standardize_locations(accident_data):
+ """解析和标准化事故地点"""
+ df = accident_data.copy()
+
+ # 提取关键路段信息
+ def extract_road_info(location):
+ if pd.isna(location):
+ return "未知路段"
+
+ location = str(location)
+
+ # 常见路段关键词
+ road_keywords = ['路', '道', '街', '巷', '路口', '交叉口', '大道', '公路']
+ area_keywords = ['新城', '临城', '千岛', '翁山', '海天', '海宇', '定沈', '滨海', '港岛', '体育', '长升', '金岛', '桃湾']
+
+ # 提取包含关键词的路段
+ for keyword in road_keywords + area_keywords:
+ if keyword in location:
+ # 提取以该关键词为中心的路段名称
+ pattern = f'[^，。]*{keyword}[^，。]*'
+ matches = re.findall(pattern, location)
+ if matches:
+ return matches[0].strip()
+
+ return location
+
+ df['standardized_location'] = df['事故具体地点'].apply(extract_road_info)
+
+ # 进一步清理和标准化
+ location_mapping = {
+ '新城千岛路': '千岛路',
+ '千岛路海天大道': '千岛路海天大道口',
+ '海天大道千岛路': '千岛路海天大道口',
+ '新城翁山路': '翁山路',
+ '翁山路金岛路': '翁山路金岛路口',
+ # 添加更多标准化映射...
+ }
+
+ df['standardized_location'] = df['standardized_location'].replace(location_mapping)
+
+ return df
+
+def analyze_location_frequency(accident_data, time_window='7D'):
+ """分析地点事故频次"""
+ df = parse_and_standardize_locations(accident_data)
+
+ # 计算时间窗口
+ recent_cutoff = df['事故时间'].max() - pd.Timedelta(time_window)
+
+ # 总体统计
+ overall_stats = df.groupby('standardized_location').agg({
+ '事故时间': ['count', 'max'], # 事故总数和最近时间
+ '事故类型': lambda x: x.mode()[0] if len(x.mode()) > 0 else '财损',
+ '道路类型': lambda x: x.mode()[0] if len(x.mode()) > 0 else '城区道路',
+ '路口路段类型': lambda x: x.mode()[0] if len(x.mode()) > 0 else '普通路段'
+ })
+
+ # 扁平化列名
+ overall_stats.columns = ['accident_count', 'last_accident', 'main_accident_type', 'main_road_type', 'main_intersection_type']
+
+ # 近期统计
+ recent_accidents = df[df['事故时间'] >= recent_cutoff]
+ recent_stats = recent_accidents.groupby('standardized_location').agg({
+ '事故时间': 'count',
+ '事故类型': lambda x: x.mode()[0] if len(x.mode()) > 0 else '财损'
+ }).rename(columns={'事故时间': 'recent_count', '事故类型': 'recent_accident_type'})
+
+ # 合并数据
+ result = overall_stats.merge(recent_stats, left_index=True, right_index=True, how='left').fillna(0)
+ result['recent_count'] = result['recent_count'].astype(int)
+
+ # 计算趋势指标
+ result['trend_ratio'] = result['recent_count'] / result['accident_count']
+ result['days_since_last'] = (df['事故时间'].max() - result['last_accident']).dt.days
+
+ return result.sort_values(['recent_count', 'accident_count'], ascending=False)
+
+
+def generate_intelligent_strategies(hotspot_df, time_period='本周'):
+ """生成智能针对性策略"""
+ strategies = []
+
+ for location_name, location_data in hotspot_df.iterrows():
+ accident_count = location_data['accident_count']
+ recent_count = location_data['recent_count']
+ accident_type = location_data['main_accident_type']
+ road_type = location_data['main_road_type']
+ intersection_type = location_data['main_intersection_type']
+ trend_ratio = location_data['trend_ratio']
+
+ # 基础信息
+ base_info = f"{time_period}对【{location_name}】"
+ data_support = f"（近期{int(recent_count)}起，累计{int(accident_count)}起，{accident_type}为主）"
+
+ # 智能策略生成
+ strategy_parts = []
+
+ # 基于事故类型
+ if accident_type == '财损':
+ strategy_parts.append("加强违法查处")
+ if '信号灯' in intersection_type:
+ strategy_parts.append("整治闯红灯、不按规定让行")
+ else:
+ strategy_parts.append("整治违法变道、超速行驶")
+ elif accident_type == '伤人':
+ strategy_parts.append("优化交通组织")
+ strategy_parts.append("增设安全设施")
+ if recent_count >= 2:
+ strategy_parts.append("开展专项整治")
+
+ # 基于路口类型
+ if intersection_type == '信号灯路口':
+ strategy_parts.append("优化信号配时")
+ elif intersection_type == '非信号灯路口':
+ strategy_parts.append("完善让行标志")
+ elif intersection_type == '普通路段':
+ if trend_ratio > 0.3: # 近期事故占比高
+ strategy_parts.append("加强巡逻管控")
+
+ # 基于趋势
+ if trend_ratio > 0.5:
+ strategy_parts.append("列为重点管控路段")
+ if location_data['days_since_last'] <= 3:
+ strategy_parts.append("近期需重点关注")
+
+ # 组合策略
+ if strategy_parts:
+ strategy = base_info + "，" + "，".join(strategy_parts) + data_support
+ else:
+ strategy = base_info + "分析事故成因，制定综合整治方案" + data_support
+
+ strategies.append(strategy)
+
+ return strategies
+
+def calculate_location_risk_score(hotspot_df):
+ """计算路口风险评分"""
+ df = hotspot_df.copy()
+
+ # 事故频次得分 (0-40分)
+ df['frequency_score'] = (df['accident_count'] / df['accident_count'].max() * 40).clip(0, 40)
+
+ # 近期趋势得分 (0-30分)
+ df['trend_score'] = (df['trend_ratio'] * 30).clip(0, 30)
+
+ # 事故严重度得分 (0-20分)
+ severity_map = {'财损': 5, '伤人': 15, '亡人': 20}
+ df['severity_score'] = df['main_accident_type'].map(severity_map).fillna(5)
+
+ # 时间紧迫度得分 (0-10分)
+ df['urgency_score'] = ((30 - df['days_since_last']) / 30 * 10).clip(0, 10)
+
+ # 总分
+ df['risk_score'] = df['frequency_score'] + df['trend_score'] + df['severity_score'] + df['urgency_score']
+
+ # 风险等级
+ conditions = [
+ df['risk_score'] >= 70,
+ df['risk_score'] >= 50,
+ df['risk_score'] >= 30
+ ]
+ choices = ['高风险', '中风险', '低风险']
+ df['risk_level'] = np.select(conditions, choices, default='一般风险')
+
+ return df.sort_values('risk_score', ascending=False)
+
+
# =======================
# 4. App
# =======================
@@ -701,10 +872,398 @@ def run_streamlit_app():
st.caption(f"🕒 最近刷新：{last_refresh.strftime('%Y-%m-%d %H:%M:%S')}")
# Tabs (add new tab for GPT analysis)
- tab_dash, tab_pred, tab_eval, tab_anom, tab_strat, tab_comp, tab_sim, tab_gpt = st.tabs(
- ["🏠 总览", "📈 预测模型", "📊 模型评估", "⚠️ 异常检测", "📝 策略评估", "⚖️ 策略对比", "🧪 情景模拟", "🔍 GPT 分析"]
+ tab_dash, tab_pred, tab_eval, tab_anom, tab_strat, tab_comp, tab_sim, tab_gpt, tab_hotspot = st.tabs(
+ ["🏠 总览", "📈 预测模型", "📊 模型评估", "⚠️ 异常检测", "📝 策略评估", "⚖️ 策略对比", "🧪 情景模拟", "🔍 GPT 分析", "📍 事故热点"]
)
+
+ with tab_hotspot:
+ st.header("📍 事故多发路口分析")
+ 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")
+
+ # 独立文件上传
+ st.subheader("📁 数据上传")
+ hotspot_file = st.file_uploader("上传事故数据文件", type=['xlsx'], key="hotspot_uploader")
+
+ if hotspot_file is not None:
+ try:
+ # 加载数据
+ @st.cache_data(show_spinner=False)
+ def load_hotspot_data(uploaded_file):
+ """独立加载事故热点分析数据"""
+ df = pd.read_excel(uploaded_file, sheet_name=None)
+ accident_data = pd.concat(df.values(), ignore_index=True)
+
+ # 数据清洗和预处理
+ accident_data['事故时间'] = pd.to_datetime(accident_data['事故时间'])
+ accident_data = accident_data.dropna(subset=['事故时间', '所在街道', '事故类型', '事故具体地点'])
+
+ # 添加严重度评分
+ severity_map = {'财损': 1, '伤人': 2, '亡人': 4}
+ accident_data['severity'] = accident_data['事故类型'].map(severity_map).fillna(1)
+
+ return accident_data
+
+ with st.spinner("正在加载数据..."):
+ accident_data = load_hotspot_data(hotspot_file)
+
+ # 显示数据概览
+ st.success(f"✅ 成功加载数据：{len(accident_data)} 条事故记录")
+
+ col1, col2, col3 = st.columns(3)
+ with col1:
+ st.metric("数据时间范围",
+ f"{accident_data['事故时间'].min().strftime('%Y-%m-%d')} 至 {accident_data['事故时间'].max().strftime('%Y-%m-%d')}")
+ with col2:
+ st.metric("事故类型分布",
+ f"财损: {len(accident_data[accident_data['事故类型']=='财损'])}起")
+ with col3:
+ st.metric("涉及区域",
+ f"{accident_data['所在街道'].nunique()}个街道")
+
+ # 地点标准化函数（独立版本）
+ def standardize_hotspot_locations(df):
+ """标准化事故地点"""
+ df = df.copy()
+
+ def extract_road_info(location):
+ if pd.isna(location):
+ return "未知路段"
+
+ location = str(location)
+
+ # 常见路段关键词
+ road_keywords = ['路', '道', '街', '巷', '路口', '交叉口', '大道', '公路', '口']
+ area_keywords = ['新城', '临城', '千岛', '翁山', '海天', '海宇', '定沈', '滨海', '港岛', '体育', '长升', '金岛', '桃湾']
+
+ # 提取包含关键词的路段
+ for keyword in road_keywords + area_keywords:
+ if keyword in location:
+ # 简化地点名称
+ words = location.split()
+ for word in words:
+ if keyword in word:
+ return word
+ return location
+
+ # 如果没找到关键词，返回原地点（截断过长的）
+ return location[:20] if len(location) > 20 else location
+
+ df['standardized_location'] = df['事故具体地点'].apply(extract_road_info)
+
+ # 手动标准化映射（根据实际数据调整）
+ location_mapping = {
+ '新城千岛路': '千岛路',
+ '千岛路海天大道': '千岛路海天大道口',
+ '海天大道千岛路': '千岛路海天大道口',
+ '新城翁山路': '翁山路',
+ '翁山路金岛路': '翁山路金岛路口',
+ '海天大道临长路': '海天大道临长路口',
+ '定沈路卫生医院门口': '定沈路医院段',
+ '翁山路海城路西口': '翁山路海城路口',
+ '海宇道路口': '海宇道',
+ '海天大道路口': '海天大道',
+ '定沈路交叉路口': '定沈路',
+ '千岛路路口': '千岛路',
+ '体育路路口': '体育路',
+ '金岛路路口': '金岛路',
+ }
+
+ df['standardized_location'] = df['standardized_location'].replace(location_mapping)
+
+ return df
+
+ # 热点分析函数
+ def analyze_hotspot_frequency(df, time_window='7D'):
+ """分析地点事故频次"""
+ df = standardize_hotspot_locations(df)
+
+ # 计算时间窗口
+ recent_cutoff = df['事故时间'].max() - pd.Timedelta(time_window)
+
+ # 总体统计
+ overall_stats = df.groupby('standardized_location').agg({
+ '事故时间': ['count', 'max'],
+ '事故类型': lambda x: x.mode()[0] if len(x.mode()) > 0 else '财损',
+ '道路类型': lambda x: x.mode()[0] if len(x.mode()) > 0 else '城区道路',
+ '路口路段类型': lambda x: x.mode()[0] if len(x.mode()) > 0 else '普通路段',
+ 'severity': 'sum'
+ })
+
+ # 扁平化列名
+ overall_stats.columns = ['accident_count', 'last_accident', 'main_accident_type',
+ 'main_road_type', 'main_intersection_type', 'total_severity']
+
+ # 近期统计
+ recent_accidents = df[df['事故时间'] >= recent_cutoff]
+ recent_stats = recent_accidents.groupby('standardized_location').agg({
+ '事故时间': 'count',
+ '事故类型': lambda x: x.mode()[0] if len(x.mode()) > 0 else '财损',
+ 'severity': 'sum'
+ }).rename(columns={'事故时间': 'recent_count', '事故类型': 'recent_accident_type', 'severity': 'recent_severity'})
+
+ # 合并数据
+ result = overall_stats.merge(recent_stats, left_index=True, right_index=True, how='left').fillna(0)
+ result['recent_count'] = result['recent_count'].astype(int)
+
+ # 计算趋势指标
+ result['trend_ratio'] = result['recent_count'] / result['accident_count']
+ result['days_since_last'] = (df['事故时间'].max() - result['last_accident']).dt.days
+ result['avg_severity'] = result['total_severity'] / result['accident_count']
+
+ return result.sort_values(['recent_count', 'accident_count'], ascending=False)
+
+ # 风险评分函数
+ def calculate_hotspot_risk_score(hotspot_df):
+ """计算路口风险评分"""
+ df = hotspot_df.copy()
+
+ # 事故频次得分 (0-40分)
+ df['frequency_score'] = (df['accident_count'] / df['accident_count'].max() * 40).clip(0, 40)
+
+ # 近期趋势得分 (0-30分)
+ df['trend_score'] = (df['trend_ratio'] * 30).clip(0, 30)
+
+ # 事故严重度得分 (0-20分)
+ severity_map = {'财损': 5, '伤人': 15, '亡人': 20}
+ df['severity_score'] = df['main_accident_type'].map(severity_map).fillna(5)
+
+ # 时间紧迫度得分 (0-10分)
+ df['urgency_score'] = ((30 - df['days_since_last']) / 30 * 10).clip(0, 10)
+
+ # 总分
+ df['risk_score'] = df['frequency_score'] + df['trend_score'] + df['severity_score'] + df['urgency_score']
+
+ # 风险等级
+ conditions = [
+ df['risk_score'] >= 70,
+ df['risk_score'] >= 50,
+ df['risk_score'] >= 30
+ ]
+ choices = ['高风险', '中风险', '低风险']
+ df['risk_level'] = np.select(conditions, choices, default='一般风险')
+
+ return df.sort_values('risk_score', ascending=False)
+
+ # 策略生成函数
+ def generate_hotspot_strategies(hotspot_df, time_period='本周'):
+ """生成热点针对性策略"""
+ strategies = []
+
+ for location_name, location_data in hotspot_df.iterrows():
+ accident_count = location_data['accident_count']
+ recent_count = location_data['recent_count']
+ accident_type = location_data['main_accident_type']
+ intersection_type = location_data['main_intersection_type']
+ trend_ratio = location_data['trend_ratio']
+ risk_level = location_data['risk_level']
+
+ # 基础信息
+ base_info = f"{time_period}对【{location_name}】"
+ data_support = f"（近期{int(recent_count)}起，累计{int(accident_count)}起，{accident_type}为主）"
+
+ # 智能策略生成
+ strategy_parts = []
+
+ # 基于路口类型和事故类型
+ if '信号灯' in str(intersection_type):
+ if accident_type == '财损':
+ strategy_parts.extend(["加强闯红灯查处", "优化信号配时", "整治不按规定让行"])
+ else:
+ strategy_parts.extend(["完善人行过街设施", "加强非机动车管理", "设置警示标志"])
+ elif '普通路段' in str(intersection_type):
+ strategy_parts.extend(["加强巡逻管控", "整治违法停车", "设置限速标志"])
+ else:
+ strategy_parts.extend(["分析事故成因", "制定综合整治方案"])
+
+ # 基于风险等级
+ if risk_level == '高风险':
+ strategy_parts.append("列为重点整治路段")
+ strategy_parts.append("开展专项整治行动")
+ elif risk_level == '中风险':
+ strategy_parts.append("加强日常监管")
+
+ # 基于趋势
+ if trend_ratio > 0.4:
+ strategy_parts.append("近期重点监控")
+
+ # 组合策略
+ if strategy_parts:
+ strategy = base_info + "，" + "，".join(strategy_parts) + data_support
+ else:
+ strategy = base_info + "加强交通安全管理" + data_support
+
+ strategies.append({
+ 'location': location_name,
+ 'strategy': strategy,
+ 'risk_level': risk_level,
+ 'accident_count': accident_count,
+ 'recent_count': recent_count
+ })
+
+ return strategies
+
+ # 分析参数设置
+ st.subheader("🔧 分析参数设置")
+ col1, col2, col3 = st.columns(3)
+ with col1:
+ time_window = st.selectbox("统计时间窗口", ['7D', '15D', '30D'], index=0, key="hotspot_window")
+ with col2:
+ min_accidents = st.number_input("最小事故数", 1, 50, 3, key="hotspot_min_accidents")
+ with col3:
+ top_n = st.slider("显示热点数量", 3, 20, 8, key="hotspot_top_n")
+
+ if st.button("🚀 开始热点分析", type="primary"):
+ with st.spinner("正在分析事故热点分布..."):
+ # 执行热点分析
+ hotspots = analyze_hotspot_frequency(accident_data, time_window=time_window)
+
+ # 过滤最小事故数
+ hotspots = hotspots[hotspots['accident_count'] >= min_accidents]
+
+ if len(hotspots) > 0:
+ # 计算风险评分
+ hotspots_with_risk = calculate_hotspot_risk_score(hotspots.head(top_n * 3))
+ top_hotspots = hotspots_with_risk.head(top_n)
+
+ # 显示热点排名
+ st.subheader(f"📊 事故多发路口排名（前{top_n}个）")
+
+ display_df = top_hotspots[[
+ 'accident_count', 'recent_count', 'trend_ratio',
+ 'main_accident_type', 'main_intersection_type', 'risk_score', 'risk_level'
+ ]].rename(columns={
+ 'accident_count': '累计事故',
+ 'recent_count': '近期事故',
+ 'trend_ratio': '趋势比例',
+ 'main_accident_type': '主要类型',
+ 'main_intersection_type': '路口类型',
+ 'risk_score': '风险评分',
+ 'risk_level': '风险等级'
+ })
+
+ # 格式化显示
+ styled_df = display_df.style.format({
+ '趋势比例': '{:.2f}',
+ '风险评分': '{:.1f}'
+ }).background_gradient(subset=['风险评分'], cmap='Reds')
+
+ st.dataframe(styled_df, use_container_width=True)
+
+ # 生成策略建议
+ strategies = generate_hotspot_strategies(top_hotspots, time_period='本周')
+
+ st.subheader("🎯 针对性策略建议")
+
+ for i, strategy_info in enumerate(strategies, 1):
+ strategy = strategy_info['strategy']
+ risk_level = strategy_info['risk_level']
+
+ # 根据风险等级显示不同颜色
+ if risk_level == '高风险':
+ st.error(f"🚨 **{i}. {strategy}**")
+ elif risk_level == '中风险':
+ st.warning(f"⚠️ **{i}. {strategy}**")
+ else:
+ st.info(f"✅ **{i}. {strategy}**")
+
+ # 可视化分析
+ st.subheader("📈 数据分析可视化")
+
+ col1, col2 = st.columns(2)
+
+ with col1:
+ # 事故频次分布图
+ fig1 = px.bar(
+ top_hotspots.head(10),
+ x=top_hotspots.head(10).index,
+ y=['accident_count', 'recent_count'],
+ title="事故频次TOP10分布",
+ labels={'value': '事故数量', 'variable': '类型', 'index': '路口名称'},
+ barmode='group'
+ )
+ fig1.update_layout(xaxis_tickangle=-45)
+ st.plotly_chart(fig1, use_container_width=True)
+
+ with col2:
+ # 风险等级分布
+ risk_dist = top_hotspots['risk_level'].value_counts()
+ fig2 = px.pie(
+ values=risk_dist.values,
+ names=risk_dist.index,
+ title="风险等级分布",
+ color_discrete_map={'高风险': 'red', '中风险': 'orange', '低风险': 'green'}
+ )
+ st.plotly_chart(fig2, use_container_width=True)
+
+ # 详细数据下载
+ st.subheader("💾 数据导出")
+
+ col_dl1, col_dl2 = st.columns(2)
+
+ with col_dl1:
+ # 下载热点数据
+ hotspot_csv = top_hotspots.to_csv().encode('utf-8-sig')
+ st.download_button(
+ "📥 下载热点数据CSV",
+ data=hotspot_csv,
+ file_name=f"accident_hotspots_{datetime.now().strftime('%Y%m%d')}.csv",
+ mime="text/csv"
+ )
+
+ with col_dl2:
+ # 下载策略报告
+ report_data = {
+ "analysis_time": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+ "time_window": time_window,
+ "data_source": hotspot_file.name,
+ "total_records": len(accident_data),
+ "analysis_parameters": {
+ "min_accidents": min_accidents,
+ "top_n": top_n
+ },
+ "top_hotspots": top_hotspots.to_dict('records'),
+ "recommended_strategies": strategies,
+ "summary": {
+ "high_risk_count": len(top_hotspots[top_hotspots['risk_level'] == '高风险']),
+ "medium_risk_count": len(top_hotspots[top_hotspots['risk_level'] == '中风险']),
+ "total_analyzed_locations": len(hotspots),
+ "most_dangerous_location": top_hotspots.index[0] if len(top_hotspots) > 0 else "无"
+ }
+ }
+
+ st.download_button(
+ "📄 下载完整分析报告",
+ data=json.dumps(report_data, ensure_ascii=False, indent=2),
+ file_name=f"hotspot_analysis_report_{datetime.now().strftime('%Y%m%d_%H%M')}.json",
+ mime="application/json"
+ )
+
+ else:
+ st.warning("⚠️ 未找到符合条件的事故热点数据，请调整筛选参数")
+
+ # 显示原始数据预览（可选）
+ with st.expander("📋 查看原始数据预览"):
+ st.dataframe(accident_data[['事故时间', '所在街道', '事故类型', '事故具体地点', '道路类型']].head(10),
+ use_container_width=True)
+
+ except Exception as e:
+ st.error(f"❌ 数据加载失败：{str(e)}")
+ st.info("请检查文件格式是否正确，确保包含'事故时间'、'事故类型'、'事故具体地点'等必要字段")
+
+ else:
+ st.info("👆 请上传事故数据Excel文件开始分析")
+ st.markdown("""
+ ### 📝 支持的数据格式要求：
+ - **文件格式**: Excel (.xlsx)
+ - **必要字段**:
+ - `事故时间`: 事故发生时的时间
+ - `事故类型`: 财损/伤人/亡人
+ - `事故具体地点`: 详细的事故发生地点
+ - `所在街道`: 事故发生的街道区域
+ - `道路类型`: 城区道路/其他等
+ - `路口路段类型`: 信号灯路口/普通路段等
+ """)
# --- Tab 1: 总览页
with tab_dash:
fig_line = go.Figure()
diff --git a/overview_series.html b/overview_series.html
index 394e462..1cb6996 100644
--- a/overview_series.html
+++ b/overview_series.html
@@ -2,6 +2,6 @@
+