diff --git a/pilot/commands/disply_type/show_chart_gen.py b/pilot/commands/disply_type/show_chart_gen.py
index 27f1639d5..6a3ee3e27 100644
--- a/pilot/commands/disply_type/show_chart_gen.py
+++ b/pilot/commands/disply_type/show_chart_gen.py
@@ -4,13 +4,13 @@ from pilot.commands.command_mange import command
 from pilot.configs.config import Config
 import pandas as pd
 import uuid
-import io
 import os
 import matplotlib
 import seaborn as sns
 
 matplotlib.use("Agg")
 import matplotlib.pyplot as plt
+import matplotlib.ticker as mtick
 from matplotlib.font_manager import FontManager
 
 from pilot.configs.model_config import LOGDIR
@@ -21,6 +21,54 @@ logger = build_logger("show_chart_gen", LOGDIR + "show_chart_gen.log")
 static_message_img_path = os.path.join(os.getcwd(), "message/img")
 
 
+def data_pre_classification(df: DataFrame):
+    ## Data pre-classification
+    columns = df.columns.tolist()
+
+    number_columns = []
+    non_numeric_colums = []
+
+    # 收集数据分类小于10个的列
+    non_numeric_colums_value_map = {}
+    numeric_colums_value_map = {}
+    for column_name in columns:
+        if pd.api.types.is_numeric_dtype(df[column_name].dtypes):
+            number_columns.append(column_name)
+            unique_values = df[column_name].unique()
+            numeric_colums_value_map.update({column_name: len(unique_values)})
+        else:
+            non_numeric_colums.append(column_name)
+            unique_values = df[column_name].unique()
+            non_numeric_colums_value_map.update({column_name: len(unique_values)})
+
+    sorted_numeric_colums_value_map = dict(
+        sorted(numeric_colums_value_map.items(), key=lambda x: x[1])
+    )
+    numeric_colums_sort_list = list(sorted_numeric_colums_value_map.keys())
+
+    sorted_colums_value_map = dict(
+        sorted(non_numeric_colums_value_map.items(), key=lambda x: x[1])
+    )
+    non_numeric_colums_sort_list = list(sorted_colums_value_map.keys())
+
+    #  Analyze x-coordinate
+    if len(non_numeric_colums_sort_list) > 0:
+        x_cloumn = non_numeric_colums_sort_list[-1]
+        non_numeric_colums_sort_list.remove(x_cloumn)
+    else:
+        x_cloumn = number_columns[0]
+        numeric_colums_sort_list.remove(x_cloumn)
+
+    #  Analyze y-coordinate
+    if len(numeric_colums_sort_list) > 0:
+        y_column = numeric_colums_sort_list[0]
+        numeric_colums_sort_list.remove(y_column)
+    else:
+        raise ValueError("Not enough numeric columns for chart！")
+
+    return x_cloumn, y_column, non_numeric_colums_sort_list, numeric_colums_sort_list
+
+
 def zh_font_set():
     font_names = [
         "Heiti TC",
@@ -48,9 +96,6 @@ def zh_font_set():
 )
 def response_line_chart(speak: str, df: DataFrame) -> str:
     logger.info(f"response_line_chart:{speak},")
-
-    columns = df.columns.tolist()
-
     if df.size <= 0:
         raise ValueError("No Data！")
 
@@ -85,7 +130,19 @@ def response_line_chart(speak: str, df: DataFrame) -> str:
     sns.set(context="notebook", style="ticks", rc=rc)
 
     fig, ax = plt.subplots(figsize=(8, 5), dpi=100)
-    sns.lineplot(df, x=columns[0], y=columns[1], ax=ax)
+    x, y, non_num_columns, num_colmns = data_pre_classification(df)
+    # ## 复杂折线图实现
+    if len(num_colmns) > 0:
+        num_colmns.append(y)
+        df_melted = pd.melt(
+            df, id_vars=x, value_vars=num_colmns, var_name="line", value_name="Value"
+        )
+        sns.lineplot(data=df_melted, x=x, y="Value", hue="line", ax=ax, palette="Set2")
+    else:
+        sns.lineplot(data=df, x=x, y=y, ax=ax, palette="Set2")
+
+    ax.yaxis.set_major_formatter(mtick.FuncFormatter(lambda y, _: "{:,.0f}".format(y)))
+    ax.yaxis.set_major_formatter(mtick.FuncFormatter(lambda x, _: "{:,.0f}".format(x)))
 
     chart_name = "line_" + str(uuid.uuid1()) + ".png"
     chart_path = static_message_img_path + "/" + chart_name
@@ -102,7 +159,6 @@ def response_line_chart(speak: str, df: DataFrame) -> str:
 )
 def response_bar_chart(speak: str, df: DataFrame) -> str:
     logger.info(f"response_bar_chart:{speak},")
-    columns = df.columns.tolist()
     if df.size <= 0:
         raise ValueError("No Data！")
 
@@ -136,9 +192,44 @@ def response_bar_chart(speak: str, df: DataFrame) -> str:
     sns.set(context="notebook", style="ticks", rc=rc)
 
     fig, ax = plt.subplots(figsize=(8, 5), dpi=100)
-    sns.barplot(df, x=df[columns[0]], y=df[columns[1]], ax=ax)
 
-    chart_name = "pie_" + str(uuid.uuid1()) + ".png"
+    hue = None
+    x, y, non_num_columns, num_colmns = data_pre_classification(df)
+    if len(non_num_columns) >= 1:
+        hue = non_num_columns[0]
+
+    if len(num_colmns) >= 1:
+        if hue:
+            if len(num_colmns) >= 2:
+                can_use_columns = num_colmns[:2]
+            else:
+                can_use_columns = num_colmns
+            sns.barplot(data=df, x=x, y=y, hue=hue, palette="Set2", ax=ax)
+            for sub_y_column in can_use_columns:
+                sns.barplot(
+                    data=df, x=x, y=sub_y_column, hue=hue, palette="Set2", ax=ax
+                )
+        else:
+            if len(num_colmns) >= 3:
+                can_use_columns = num_colmns[:3]
+            else:
+                can_use_columns = num_colmns
+            sns.barplot(
+                data=df, x=x, y=y, hue=can_use_columns[0], palette="Set2", ax=ax
+            )
+
+            for sub_y_column in can_use_columns[1:]:
+                sns.barplot(
+                    data=df, x=x, y=sub_y_column, hue=hue, palette="Set2", ax=ax
+                )
+    else:
+        sns.barplot(data=df, x=x, y=y, hue=hue, palette="Set2", ax=ax)
+
+    # 设置 y 轴刻度格式为普通数字格式
+    ax.yaxis.set_major_formatter(mtick.FuncFormatter(lambda y, _: "{:,.0f}".format(y)))
+    ax.yaxis.set_major_formatter(mtick.FuncFormatter(lambda x, _: "{:,.0f}".format(x)))
+
+    chart_name = "bar_" + str(uuid.uuid1()) + ".png"
     chart_path = static_message_img_path + "/" + chart_name
     plt.savefig(chart_path, bbox_inches="tight", dpi=100)
     html_img = f"""<h5>{speak}</h5><img style='max-width: 100%; max-height: 70%;'  src="/images/{chart_name}" />"""
@@ -188,13 +279,6 @@ def response_pie_chart(speak: str, df: DataFrame) -> str:
         startangle=90,
         autopct="%1.1f%%",
     )
-    # 手动设置 labels 的位置和大小
-    ax.legend(
-        loc="upper right",
-        bbox_to_anchor=(0, 0, 1, 1),
-        labels=df[columns[0]].values,
-        fontsize=10,
-    )
 
     plt.axis("equal")  # 使饼图为正圆形
     # plt.title(columns[0])
diff --git a/pilot/scene/chat_data/chat_excel/excel_learning/bar_1d2c71b4-4637-11ee-86eb-b26789cc3e58.png b/pilot/scene/chat_data/chat_excel/excel_learning/bar_1d2c71b4-4637-11ee-86eb-b26789cc3e58.png
deleted file mode 100644
index e9bd360b4..000000000
Binary files a/pilot/scene/chat_data/chat_excel/excel_learning/bar_1d2c71b4-4637-11ee-86eb-b26789cc3e58.png and /dev/null differ
diff --git a/pilot/scene/chat_data/chat_excel/excel_learning/bar_6077945c-4638-11ee-9fb4-b26789cc3e58.png b/pilot/scene/chat_data/chat_excel/excel_learning/bar_6077945c-4638-11ee-9fb4-b26789cc3e58.png
deleted file mode 100644
index 09ad02b58..000000000
Binary files a/pilot/scene/chat_data/chat_excel/excel_learning/bar_6077945c-4638-11ee-9fb4-b26789cc3e58.png and /dev/null differ
diff --git a/pilot/scene/chat_data/chat_excel/excel_learning/test.py b/pilot/scene/chat_data/chat_excel/excel_learning/test.py
index 2d90f169d..7dc1a2746 100644
--- a/pilot/scene/chat_data/chat_excel/excel_learning/test.py
+++ b/pilot/scene/chat_data/chat_excel/excel_learning/test.py
@@ -1,9 +1,11 @@
 import os
 import duckdb
 import pandas as pd
+import numpy as np
 import matplotlib
 import seaborn as sns
 import uuid
+
 from pandas import DataFrame
 
 import matplotlib.pyplot as plt
@@ -29,8 +31,12 @@ def data_pre_classification(df: DataFrame):
     # 收集数据分类小于10个的列
     non_numeric_colums_value_map = {}
     numeric_colums_value_map = {}
+    df_filtered = df.dropna()
     for column_name in columns:
-        if pd.to_numeric(df[column_name], errors="coerce").notna().all():
+        print(np.issubdtype(df_filtered[column_name].dtype, np.number))
+        # if pd.to_numeric(df[column_name], errors='coerce').notna().all():
+        # if np.issubdtype(df_filtered[column_name].dtype, np.number):
+        if pd.api.types.is_numeric_dtype(df[column_name].dtypes):
             number_columns.append(column_name)
             unique_values = df[column_name].unique()
             numeric_colums_value_map.update({column_name: len(unique_values)})
@@ -39,27 +45,68 @@ def data_pre_classification(df: DataFrame):
             unique_values = df[column_name].unique()
             non_numeric_colums_value_map.update({column_name: len(unique_values)})
 
-    if len(non_numeric_colums) <= 0:
-        sorted_colums_value_map = dict(
-            sorted(numeric_colums_value_map.items(), key=lambda x: x[1])
-        )
-        numeric_colums_sort_list = list(sorted_colums_value_map.keys())
-        x_column = number_columns[0]
-        hue_column = numeric_colums_sort_list[0]
-        y_column = numeric_colums_sort_list[1]
-    elif len(number_columns) <= 0:
-        raise ValueError("Have No numeric Column！")
-    else:
-        # 数字和非数字都存在多列，放弃部分数字列
-        y_column = number_columns[0]
-        x_column = non_numeric_colums[0]
-        # if len(non_numeric_colums) > 1:
-        #
-        # else:
+    sorted_numeric_colums_value_map = dict(
+        sorted(numeric_colums_value_map.items(), key=lambda x: x[1])
+    )
+    numeric_colums_sort_list = list(sorted_numeric_colums_value_map.keys())
 
-        # non_numeric_colums_sort_list.remove(non_numeric_colums[0])
-        # hue_column = non_numeric_colums_sort_list
-    return x_column, y_column, hue_column
+    sorted_colums_value_map = dict(
+        sorted(non_numeric_colums_value_map.items(), key=lambda x: x[1])
+    )
+    non_numeric_colums_sort_list = list(sorted_colums_value_map.keys())
+
+    #  Analyze x-coordinate
+    if len(non_numeric_colums_sort_list) > 0:
+        x_cloumn = non_numeric_colums_sort_list[-1]
+        non_numeric_colums_sort_list.remove(x_cloumn)
+    else:
+        x_cloumn = number_columns[0]
+        numeric_colums_sort_list.remove(x_cloumn)
+
+    #  Analyze y-coordinate
+    if len(numeric_colums_sort_list) > 0:
+        y_column = numeric_colums_sort_list[0]
+        numeric_colums_sort_list.remove(y_column)
+    else:
+        raise ValueError("Not enough numeric columns for chart！")
+
+    return x_cloumn, y_column, non_numeric_colums_sort_list, numeric_colums_sort_list
+
+    #
+    # if len(non_numeric_colums) <=0:
+    #     sorted_colums_value_map = dict(sorted(numeric_colums_value_map.items(), key=lambda x: x[1]))
+    #     numeric_colums_sort_list = list(sorted_colums_value_map.keys())
+    #     x_column = number_columns[0]
+    #     hue_column = numeric_colums_sort_list[0]
+    #     y_column = numeric_colums_sort_list[1]
+    #     cols = numeric_colums_sort_list[2:]
+    # elif len(number_columns) <=0:
+    #     raise ValueError("Have No numeric Column！")
+    # else:
+    #     # 数字和非数字都存在多列，放弃部分数字列
+    #     x_column = non_numeric_colums[0]
+    #     y_column = number_columns[0]
+    #     if len(non_numeric_colums) > 1:
+    #         sorted_colums_value_map = dict(sorted(non_numeric_colums_value_map.items(), key=lambda x: x[1]))
+    #         non_numeric_colums_sort_list = list(sorted_colums_value_map.keys())
+    #         non_numeric_colums_sort_list.remove(non_numeric_colums[0])
+    #         hue_column = non_numeric_colums_sort_list[0]
+    #         if len(number_columns) > 1:
+    #             # try multiple charts
+    #             cols = number_columns.remove( number_columns[0])
+    #
+    #     else:
+    #         sorted_colums_value_map = dict(sorted(numeric_colums_value_map.items(), key=lambda x: x[1]))
+    #         numeric_colums_sort_list = list(sorted_colums_value_map.keys())
+    #         numeric_colums_sort_list.remove(number_columns[0])
+    #         if sorted_colums_value_map[numeric_colums_sort_list[0]].value < 5:
+    #             hue_column = numeric_colums_sort_list[0]
+    #         if len(number_columns) > 2:
+    #             # try multiple charts
+    #             cols = numeric_colums_sort_list.remove(numeric_colums_sort_list[0])
+    #
+    # print(x_column, y_column, hue_column, cols)
+    # return x_column, y_column, hue_column
 
 
 if __name__ == "__main__":
@@ -79,17 +126,47 @@ if __name__ == "__main__":
     # 获取系统中的默认中文字体名称
     # default_font = fm.fontManager.defaultFontProperties.get_family()
 
+    # 创建一个示例 DataFrame
+    df = pd.DataFrame(
+        {
+            "A": [1, 2, 3, None, 5],
+            "B": [10, 20, 30, 40, 50],
+            "C": [1.1, 2.2, None, 4.4, 5.5],
+            "D": ["a", "b", "c", "d", "e"],
+        }
+    )
+
+    # 判断列是否为数字列
+    column_name = "A"  # 要判断的列名
+    is_numeric = pd.to_numeric(df[column_name], errors="coerce").notna().all()
+
+    if is_numeric:
+        print(
+            f"Column '{column_name}' is a numeric column (ignoring null and NaN values in some elements)."
+        )
+    else:
+        print(
+            f"Column '{column_name}' is not a numeric column (ignoring null and NaN values in some elements)."
+        )
+
     #
-    excel_reader = ExcelReader("/Users/tuyang.yhj/Downloads/example.xlsx")
+    # excel_reader = ExcelReader("/Users/tuyang.yhj/Downloads/example.xlsx")
+    excel_reader = ExcelReader("/Users/tuyang.yhj/Downloads/yhj-zx.csv")
     #
     # # colunms, datas = excel_reader.run( "SELECT CONCAT(Year, '-', Quarter) AS QuarterYear, SUM(Sales) AS TotalSales FROM example GROUP BY QuarterYear ORDER BY QuarterYear")
     # # colunms, datas = excel_reader.run( """ SELECT Year, SUM(Sales) AS Total_Sales FROM example GROUP BY Year ORDER BY Year; """)
+    # df = excel_reader.get_df_by_sql_ex(""" SELECT Segment, Country, SUM(Sales) AS Total_Sales, SUM(Profit) AS Total_Profit FROM example GROUP BY Segment, Country """)
     df = excel_reader.get_df_by_sql_ex(
-        """ SELECT Segment, Country, SUM(Sales) AS Total_Sales, SUM(Profit) AS Total_Profit FROM example GROUP BY Segment, Country """
+        """ SELECT `明细`, `费用小计`, `支出小计` FROM yhj-zx limit 10"""
     )
 
-    x, y, hue = data_pre_classification(df)
-    print(x, y, hue)
+    for column_name in df.columns.tolist():
+        print(column_name + ":" + str(df[column_name].dtypes))
+        print(
+            column_name
+            + ":"
+            + str(pd.api.types.is_numeric_dtype(df[column_name].dtypes))
+        )
 
     columns = df.columns.tolist()
     font_names = [
@@ -118,116 +195,66 @@ if __name__ == "__main__":
     sns.color_palette("hls", 10)
     sns.hls_palette(8, l=0.5, s=0.7)
     sns.set(context="notebook", style="ticks", rc=rc)
-    # sns.set_palette("Set3")  # 设置颜色主题
-    # sns.set_style("dark")
-    # sns.color_palette("hls", 10)
-    # sns.hls_palette(8, l=.5, s=.7)
-    # sns.set(context='notebook', style='ticks', rc=rc)
 
     fig, ax = plt.subplots(figsize=(8, 5), dpi=100)
     # plt.ticklabel_format(style='plain')
     # ax = df.plot(kind='bar', ax=ax)
-    # sns.barplot(df, x=x, y=y, hue= "Country", ax=ax)
-    sns.catplot(data=df, x=x, y=y, hue="Country", kind="bar")
+    # sns.barplot(df, x=x, y="Total_Sales", hue='Country', ax=ax)
+    # sns.barplot(df, x=x, y="Total_Profit", hue='Country', ax=ax)
+
+    # sns.catplot(data=df, x=x, y=y, hue='Country',  kind='bar')
+    x, y, non_num_columns, num_colmns = data_pre_classification(df)
+    print(x, y, str(non_num_columns), str(num_colmns))
+    ## 复杂折线图实现
+    if len(num_colmns) > 0:
+        num_colmns.append(y)
+        df_melted = pd.melt(
+            df, id_vars=x, value_vars=num_colmns, var_name="line", value_name="Value"
+        )
+        sns.lineplot(data=df_melted, x=x, y="Value", hue="line", ax=ax, palette="Set2")
+    else:
+        sns.lineplot(data=df, x=x, y=y, ax=ax, palette="Set2")
+
+    # hue = None
+    # ## 复杂柱状图实现
+    # x,y, non_num_columns, num_colmns =data_pre_classification(df)
+    # if len(non_num_columns) >= 1:
+    #     hue = non_num_columns[0]
+
+    # if len(num_colmns)>=1:
+    #     if hue:
+    #         if len(num_colmns) >= 2:
+    #             can_use_columns = num_colmns[:2]
+    #         else:
+    #             can_use_columns = num_colmns
+    #         sns.barplot(data=df, x=x, y=y, hue=hue, palette="Set2", ax=ax)
+    #         for sub_y_column in can_use_columns:
+    #             sns.barplot(data=df, x=x, y=sub_y_column, hue=hue, palette="Set2", ax=ax)
+    #     else:
+    #         if len(num_colmns) >= 3:
+    #             can_use_columns = num_colmns[:3]
+    #         else:
+    #             can_use_columns = num_colmns
+    #         sns.barplot(data=df, x=x, y=y, hue=can_use_columns[0], palette="Set2", ax=ax)
+    #
+    #         for sub_y_column in can_use_columns[1:]:
+    #             sns.barplot(data=df, x=x, y=sub_y_column, hue=hue, palette="Set2", ax=ax)
+    # else:
+    #     sns.barplot(data=df, x=x, y=y, hue=hue, palette="Set2", ax=ax)
+
+    # # 转换 DataFrame 格式
+    # df_melted = pd.melt(df, id_vars=x, value_vars=['Total_Sales', 'Total_Profit'], var_name='line', value_name='y')
+    #
+    # # 绘制多列柱状图
+    #
+    # sns.barplot(data=df, x=x, y="Total_Sales", hue = "Country",  palette="Set2", ax=ax)
+    # sns.barplot(data=df, x=x, y="Total_Profit", hue = "Country",  palette="Set1", ax=ax)
+
     # 设置 y 轴刻度格式为普通数字格式
     ax.yaxis.set_major_formatter(mtick.FuncFormatter(lambda x, _: "{:,.0f}".format(x)))
 
-    # fonts = font_manager.findSystemFonts()
-    # font_path = ""
-    # for font in fonts:
-    #     if "Heiti" in font:
-    #         font_path = font
-    # my_font = font_manager.FontProperties(fname=font_path)
-    # plt.title("测试", fontproperties=my_font)
-    # plt.ylabel(columns[1], fontproperties=my_font)
-    # plt.xlabel(columns[0], fontproperties=my_font)
-
     chart_name = "bar_" + str(uuid.uuid1()) + ".png"
     chart_path = chart_name
     plt.savefig(chart_path, bbox_inches="tight", dpi=100)
 
-    # sns.set(context="notebook", style="ticks", color_codes=True)
-    # sns.set_palette("Set3")  # 设置颜色主题
-    #
-    # # fig, ax = plt.pie(df[columns[1]], labels=df[columns[0]], autopct='%1.1f%%', startangle=90)
-    # fig, ax = plt.subplots(figsize=(8, 5), dpi=100)
-    # plt.subplots_adjust(top=0.9)
-    # ax = df.plot(kind='pie', y=columns[1], ax=ax, labels=df[columns[0]].values, startangle=90, autopct='%1.1f%%')
-    # # 手动设置 labels 的位置和大小
-    # ax.legend(loc='center left', bbox_to_anchor=(-1, 0.5, 0,0), labels=None, fontsize=10)
-    # plt.axis('equal')  # 使饼图为正圆形
-    # plt.show()
-
-    #
-    #
-    # def csv_colunm_foramt(val):
-    #     if str(val).find("$") >= 0:
-    #         return float(val.replace('$', '').replace(',', ''))
-    #     if str(val).find("¥") >= 0:
-    #         return float(val.replace('¥', '').replace(',', ''))
-    #     return val
-    #
-    # # 获取当前时间戳，作为代码开始的时间
-    # start_time = int(time.time() * 1000)
-    #
-    # df = pd.read_excel('/Users/tuyang.yhj/Downloads/example.xlsx')
-    # # 读取 Excel 文件为 Pandas DataFrame
-    # df = pd.read_excel('/Users/tuyang.yhj/Downloads/example.xlsx', converters={i: csv_colunm_foramt for i in range(df.shape[1])})
-    #
-    # # d = df.values
-    # # print(d.shape[0])
-    # # for row in d:
-    # #     print(row[0])
-    # #     print(len(row))
-    # # r = df.iterrows()
-    #
-    # # 获取当前时间戳，作为代码结束的时间
-    # end_time = int(time.time() * 1000)
-    #
-    # print(f"耗时:{(end_time-start_time)/1000}秒")
-    #
-    # # 连接 DuckDB 数据库
-    # con = duckdb.connect(database=':memory:', read_only=False)
-    #
-    # # 将 DataFrame 写入 DuckDB 数据库中的一个表
-    # con.register('example', df)
-    #
-    # # 查询 DuckDB 数据库中的表
-    # conn  = con.cursor()
-    # results = con.execute('SELECT Country, SUM(Profit) AS Total_Profit FROM example GROUP BY Country ORDER BY Total_Profit DESC LIMIT 1;')
-    # colunms = []
-    # for descrip in results.description:
-    #     colunms.append(descrip[0])
-    # print(colunms)
-    # for row in results.fetchall():
-    #     print(row)
-    #
-    #
-    # # 连接 DuckDB 数据库
-    # # con = duckdb.connect(':memory:')
-    #
-    # # # 加载 spatial 扩展
-    # # con.execute('install spatial;')
-    # # con.execute('load spatial;')
-    # #
-    # # # 查询 duckdb_internal 系统表，获取扩展列表
-    # # result = con.execute("SELECT * FROM duckdb_internal.functions WHERE schema='list_extensions';")
-    # #
-    # # # 遍历查询结果，输出扩展名称和版本号
-    # # for row in result:
-    # #     print(row['name'], row['return_type'])
-    # # duckdb.read_csv('/Users/tuyang.yhj/Downloads/example_csc.csv')
-    # # result = duckdb.sql('SELECT * FROM "/Users/tuyang.yhj/Downloads/yhj-zx.csv" ')
-    # # result = duckdb.sql('SELECT * FROM "/Users/tuyang.yhj/Downloads/example_csc.csv" limit 20')
-    # # for row in result.fetchall():
-    # #     print(row)
-    #
-    #
-    # # result = con.execute("SELECT * FROM st_read('/Users/tuyang.yhj/Downloads/example.xlsx', layer='Sheet1')")
-    # # # 遍历查询结果
-    # # for row in result.fetchall():
-    # #     print(row)
-    # print("xx")
-    #
-    #
     #
diff --git a/pilot/scene/chat_data/chat_excel/excel_reader.py b/pilot/scene/chat_data/chat_excel/excel_reader.py
index ff4759fa2..7ac8b0d4e 100644
--- a/pilot/scene/chat_data/chat_excel/excel_reader.py
+++ b/pilot/scene/chat_data/chat_excel/excel_reader.py
@@ -71,7 +71,8 @@ class ExcelReader:
         for column_name in df_tmp.columns:
             self.columns_map.update({column_name: excel_colunm_format(column_name)})
             try:
-                self.df[column_name] = self.df[column_name].astype(float)
+                self.df[column_name] = pd.to_numeric(self.df[column_name])
+                self.df[column_name] = self.df[column_name].fillna(0)
             except Exception as e:
                 print("transfor column error！" + column_name)