diff --git a/aiheart/cardio_dashboard.py b/aiheart/cardio_dashboard.py
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+++ b/aiheart/cardio_dashboard.py
@@ -0,0 +1,403 @@
+"""
+心血管疾病数据仪表板
+Streamlit应用程序，用于心血管疾病数据的清洗、特征工程和交互式可视化
+终端启动程序命令
+streamlit run D:\AI_Class\PyCharm\Work_Space\CardAI\module1_dashboard\cardio_dashboard.py
+"""
+
+import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.express as px
+import plotly.graph_objects as go
+from pathlib import Path
+import warnings
+warnings.filterwarnings('ignore')
+
+# 设置页面配置
+st.set_page_config(
+    page_title="心血管疾病数据分析仪表板",
+    page_icon="❤️",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# 常量定义 - 使用绝对路径
+DATA_PATH = Path("D:/AI_Class/PyCharm/Work_Space/CardAI/data/心血管疾病.xlsx")
+# BMI分类（中国标准）
+BMI_CATEGORIES = {
+    "偏瘦": (0, 18.5),
+    "正常": (18.5, 24),
+    "超重": (24, 28),
+    "肥胖": (28, float('inf'))
+}
+CHOLESTEROL_MAP = {1: "正常", 2: "高于正常", 3: "远高于正常"}
+GLUC_MAP = {1: "正常", 2: "高于正常", 3: "远高于正常"}
+GENDER_MAP = {1: "女性", 2: "男性"}
+
+# 数据加载和清洗函数（使用缓存）
+@st.cache_data
+def load_and_clean_data(file_path):
+    """
+    加载并清洗心血管疾病数据
+
+    Args:
+        file_path: Excel文件路径
+
+    Returns:
+        pandas.DataFrame: 清洗后的数据框
+    """
+    try:
+        # 加载数据
+        df = pd.read_excel(file_path)
+
+        # 1. 特征工程
+        # 将年龄从天数转换为年（四舍五入）
+        df['age_years'] = round(df['age'] / 365.25).astype(int)
+
+        # 计算BMI
+        df['BMI'] = df['weight'] / ((df['height'] / 100) ** 2)
+
+        # 2. 异常值处理
+        # 删除舒张压 ≥ 收缩压的记录
+        df = df[df['ap_lo'] < df['ap_hi']]
+
+        # 删除血压极端异常值（保留收缩压∈[90,250]，舒张压∈[60,150]）
+        df = df[(df['ap_hi'] >= 90) & (df['ap_hi'] <= 250)]
+        df = df[(df['ap_lo'] >= 60) & (df['ap_lo'] <= 150)]
+
+        # 3. 类别转换
+        # 胆固醇水平转换
+        df['cholesterol_str'] = df['cholesterol'].map(CHOLESTEROL_MAP)
+
+        # 血糖水平转换
+        df['gluc_str'] = df['gluc'].map(GLUC_MAP)
+
+        # 性别转换
+        df['gender_str'] = df['gender'].map(GENDER_MAP)
+
+        # 心血管疾病状态转换
+        df['cardio_str'] = df['cardio'].map({0: "无心血管疾病", 1: "有心血管疾病"})
+
+        # 4. BMI分类（中国标准）
+        def categorize_bmi(bmi):
+            for category, (low, high) in BMI_CATEGORIES.items():
+                if low <= bmi < high:
+                    return category
+            return "未知"
+
+        df['bmi_category'] = df['BMI'].apply(categorize_bmi)
+
+        # 重置索引
+        df = df.reset_index(drop=True)
+
+        return df
+
+    except FileNotFoundError:
+        st.error(f"数据文件未找到: {file_path}")
+        return pd.DataFrame()
+    except Exception as e:
+        st.error(f"数据加载失败: {str(e)}")
+        return pd.DataFrame()
+
+@st.cache_data
+def filter_data(df, age_range, gender_filter, cardio_filter):
+    """
+    根据筛选条件过滤数据
+
+    Args:
+        df: 原始数据框
+        age_range: 年龄范围 [min, max]
+        gender_filter: 性别筛选列表
+        cardio_filter: 心血管疾病筛选列表
+
+    Returns:
+        pandas.DataFrame: 筛选后的数据框
+    """
+    filtered_df = df.copy()
+
+    # 年龄筛选
+    filtered_df = filtered_df[
+        (filtered_df['age_years'] >= age_range[0]) &
+        (filtered_df['age_years'] <= age_range[1])
+    ]
+
+    # 性别筛选
+    if "全部" not in gender_filter:
+        gender_values = [k for k, v in GENDER_MAP.items() if v in gender_filter]
+        filtered_df = filtered_df[filtered_df['gender'].isin(gender_values)]
+
+    # 心血管疾病筛选
+    if "全部" not in cardio_filter:
+        cardio_values = []
+        if "有心血管疾病" in cardio_filter:
+            cardio_values.append(1)
+        if "无心血管疾病" in cardio_filter:
+            cardio_values.append(0)
+        filtered_df = filtered_df[filtered_df['cardio'].isin(cardio_values)]
+
+    return filtered_df
+
+def create_age_distribution_chart(df):
+    """
+    创建年龄分布直方图
+
+    Args:
+        df: 数据框
+
+    Returns:
+        plotly.graph_objects.Figure: 年龄分布图表
+    """
+    fig = px.histogram(
+        df,
+        x='age_years',
+        color='cardio_str',
+        nbins=30,
+        title='年龄分布（按心血管疾病状态）',
+        labels={'age_years': '年龄（岁）', 'count': '人数', 'cardio_str': '心血管疾病状态'},
+        color_discrete_map={"有心血管疾病": "#EF553B", "无心血管疾病": "#636EFA"},
+        opacity=0.7
+    )
+
+    fig.update_layout(
+        bargap=0.1,
+        xaxis_title="年龄（岁）",
+        yaxis_title="人数",
+        legend_title="心血管疾病状态",
+        hovermode='x unified'
+    )
+
+    return fig
+
+def create_bmi_cardio_chart(df):
+    """
+    创建BMI分类对心血管疾病影响的堆叠柱状图
+
+    Args:
+        df: 数据框
+
+    Returns:
+        plotly.graph_objects.Figure: BMI分类图表
+    """
+    # 计算交叉表
+    cross_tab = pd.crosstab(
+        df['bmi_category'],
+        df['cardio_str'],
+        normalize='index'
+    ).reset_index()
+
+    # 转换为长格式
+    cross_tab_melted = cross_tab.melt(
+        id_vars='bmi_category',
+        var_name='cardio_status',
+        value_name='percentage'
+    )
+
+    # 创建堆叠柱状图
+    fig = px.bar(
+        cross_tab_melted,
+        x='bmi_category',
+        y='percentage',
+        color='cardio_status',
+        title='BMI分类对心血管疾病的影响',
+        labels={
+            'bmi_category': 'BMI分类',
+            'percentage': '比例',
+            'cardio_status': '心血管疾病状态'
+        },
+        color_discrete_map={"有心血管疾病": "#EF553B", "无心血管疾病": "#636EFA"},
+        text_auto='.1%'
+    )
+
+    fig.update_layout(
+        xaxis_title="BMI分类",
+        yaxis_title="比例",
+        legend_title="心血管疾病状态",
+        yaxis_tickformat=',.0%',
+        hovermode='x unified'
+    )
+
+    return fig
+
+def display_summary_stats(df):
+    """
+    显示摘要统计信息
+
+    Args:
+        df: 数据框
+    """
+    total_records = len(df)
+    cardio_cases = df['cardio'].sum()
+    cardio_rate = (cardio_cases / total_records * 100) if total_records > 0 else 0
+
+    col1, col2, col3 = st.columns(3)
+
+    with col1:
+        st.metric(
+            label="总记录数",
+            value=f"{total_records:,}",
+            delta=None
+        )
+
+    with col2:
+        st.metric(
+            label="心血管疾病病例数",
+            value=f"{cardio_cases:,}",
+            delta=None
+        )
+
+    with col3:
+        st.metric(
+            label="心血管疾病风险率",
+            value=f"{cardio_rate:.2f}%",
+            delta=None
+        )
+
+def main():
+    """
+    主函数：Streamlit应用程序入口
+    """
+    # 标题
+    st.title("❤️ 心血管疾病数据分析仪表板")
+    st.markdown("---")
+
+    # 加载数据
+    with st.spinner("正在加载数据..."):
+        df = load_and_clean_data(DATA_PATH)
+
+    if df.empty:
+        st.error("数据加载失败，请检查数据文件路径和格式。")
+        return
+
+    # 侧边栏 - 筛选器
+    st.sidebar.header("🔍 数据筛选")
+
+    # 年龄范围滑块
+    age_min = int(df['age_years'].min())
+    age_max = int(df['age_years'].max())
+    age_range = st.sidebar.slider(
+        "选择年龄范围（岁）",
+        min_value=age_min,
+        max_value=age_max,
+        value=[20, 80],
+        step=1
+    )
+
+    # 性别筛选器
+    gender_options = ["女性", "男性", "全部"]
+    gender_filter = st.sidebar.multiselect(
+        "选择性别",
+        options=gender_options,
+        default=["全部"]
+    )
+
+    # 心血管疾病筛选器
+    cardio_options = ["有心血管疾病", "无心血管疾病", "全部"]
+    cardio_filter = st.sidebar.multiselect(
+        "选择心血管疾病状态",
+        options=cardio_options,
+        default=["全部"]
+    )
+
+    # 应用筛选
+    filtered_df = filter_data(df, age_range, gender_filter, cardio_filter)
+
+    # 显示筛选信息
+    st.sidebar.markdown("---")
+    st.sidebar.info(f"**筛选结果**: {len(filtered_df):,} 条记录")
+
+    # 主页面
+    # 1. 摘要统计
+    st.header("📊 数据摘要")
+    display_summary_stats(filtered_df)
+
+    st.markdown("---")
+
+    # 2. 年龄分布图表
+    st.header("📈 年龄分布分析")
+    col1, col2 = st.columns([3, 1])
+
+    with col1:
+        age_chart = create_age_distribution_chart(filtered_df)
+        st.plotly_chart(age_chart, use_container_width=True)
+
+    with col2:
+        st.markdown("### 年龄统计")
+        st.metric("平均年龄", f"{filtered_df['age_years'].mean():.1f} 岁")
+        st.metric("年龄中位数", f"{filtered_df['age_years'].median():.1f} 岁")
+        st.metric("年龄标准差", f"{filtered_df['age_years'].std():.1f} 岁")
+
+    st.markdown("---")
+
+    # 3. BMI分类分析
+    st.header("⚖️ BMI分类分析")
+    col1, col2 = st.columns([3, 1])
+
+    with col1:
+        bmi_chart = create_bmi_cardio_chart(filtered_df)
+        st.plotly_chart(bmi_chart, use_container_width=True)
+
+    with col2:
+        st.markdown("### BMI统计")
+        st.metric("平均BMI", f"{filtered_df['BMI'].mean():.1f}")
+        st.metric("BMI中位数", f"{filtered_df['BMI'].median():.1f}")
+
+        # BMI分类分布
+        bmi_dist = filtered_df['bmi_category'].value_counts()
+        st.markdown("### BMI分类分布")
+        for category, count in bmi_dist.items():
+            percentage = (count / len(filtered_df)) * 100
+            st.markdown(f"**{category}**: {count:,} ({percentage:.1f}%)")
+
+    st.markdown("---")
+
+    # 4. 数据预览
+    st.header("🔍 数据预览")
+
+    with st.expander("查看筛选后的数据"):
+        st.dataframe(
+            filtered_df[
+                ['id', 'age_years', 'gender_str', 'height', 'weight', 'BMI',
+                 'bmi_category', 'ap_hi', 'ap_lo', 'cholesterol_str',
+                 'gluc_str', 'smoke', 'alco', 'active', 'cardio_str']
+            ].head(100),
+            use_container_width=True
+        )
+
+    # 5. 数据下载
+    st.header("📥 数据导出")
+
+    @st.cache_data
+    def convert_df_to_csv(df):
+        return df.to_csv(index=False).encode('utf-8')
+
+    csv_data = convert_df_to_csv(filtered_df)
+
+    col1, col2 = st.columns(2)
+
+    with col1:
+        st.download_button(
+            label="📥 下载筛选数据 (CSV)",
+            data=csv_data,
+            file_name="filtered_cardio_data.csv",
+            mime="text/csv",
+            help="下载当前筛选条件下的数据"
+        )
+
+    with col2:
+        if st.button("🔄 重置筛选"):
+            st.rerun()
+
+    # 页脚
+    st.markdown("---")
+    st.markdown(
+        """
+        <div style='text-align: center; color: gray;'>
+        <p>心血管疾病数据分析仪表板 | 数据来源: 心血管疾病.xlsx | 总记录数: 70,000</p>
+        </div>
+        """,
+        unsafe_allow_html=True
+    )
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file