diff --git a/aisenzhecode/沥青/定性模型数据项12-11 - 2025年1月16日备份.xls b/aisenzhecode/沥青/定性模型数据项12-11 - 2025年1月16日备份.xls
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diff --git a/aisenzhecode/沥青/定性模型数据项12-11.xls b/aisenzhecode/沥青/定性模型数据项12-11.xls
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diff --git a/aisenzhecode/沥青/定性模型数据项12-11.xlsx b/aisenzhecode/沥青/定性模型数据项12-11.xlsx
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diff --git a/aisenzhecode/沥青/沥青定性模型每日推送-ytj.ipynb b/aisenzhecode/沥青/沥青定性模型每日推送-ytj.ipynb
index bdf0855..5dec613 100644
--- a/aisenzhecode/沥青/沥青定性模型每日推送-ytj.ipynb
+++ b/aisenzhecode/沥青/沥青定性模型每日推送-ytj.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -34,7 +34,7 @@
     "}\n",
     "\n",
     "\n",
-    "read_file_path_name = \"定性模型数据项12-11.xls\"\n",
+    "read_file_path_name = \"定性模型数据项12-11.xlsx\"\n",
     "one_cols = []\n",
     "two_cols = []\n",
     "\n",
@@ -51,32 +51,34 @@
     "    :param threshold: 变化阈值\n",
     "    \"\"\"\n",
     "    # 读取Excel文件\n",
-    "    try:\n",
-    "        df = pd.read_excel(file_path, engine='openpyxl')\n",
-    "    except:\n",
-    "        df = pd.read_excel(file_path, engine='xlrd')\n",
+    "    # try:\n",
+    "    #     df = pd.read_excel(file_path, engine='openpyxl')\n",
+    "    # except:\n",
+    "    #     df = pd.read_excel(file_path, engine='xlrd')\n",
+    "        \n",
+    "    df = pd.read_excel(file_path)\n",
     "    # 所有列列统一数据格式为float\n",
     "    df = df.applymap(lambda x: float(x) if isinstance(x, (int, float)) else x)\n",
     "    \n",
-    "    print(df.tail())\n",
+    "    # print(df.tail())\n",
     "    # 填充缺失值\n",
     "    df = df.fillna(method='ffill')\n",
     "\n",
     "    # 获取最后两行数据\n",
     "    df1 = df.tail(2)\n",
-    "    print(df1)\n",
+    "    # print(df1)\n",
     "    # 获取前一天的指定列值\n",
     "    previous_value = df1.iloc[0, column_index]\n",
-    "    print(previous_value,type(previous_value))\n",
+    "    print('前一天的',previous_value,type(previous_value))\n",
     "    # 获取当前的指定列值\n",
     "    current_value = df1.iloc[1, column_index]\n",
-    "    print(current_value,type(current_value))\n",
+    "    print('现在的',current_value,type(current_value))\n",
     "    # 判断指定列值的变化是否大于阈值\n",
     "    if abs(current_value - previous_value) > threshold:\n",
     "        # 如果变化大于阈值，将当前的指定列值改为前一天的值\n",
     "        df.iloc[-1, column_index] = previous_value\n",
     "        print('修改了')\n",
-    "    print(df.tail())\n",
+    "    # print(df.tail())\n",
     "    # 将修改后的数据写回Excel文件\n",
     "    df.to_excel(file_path, index=False,engine='openpyxl')\n",
     "\n",
@@ -90,7 +92,7 @@
     "        token =  None\n",
     "    return token\n",
     "\n",
-    "def updateExcelData(date='',token=None):\n",
+    "def updateExcelDatabak(date='',token=None):\n",
     "    workbook = xlrd.open_workbook(read_file_path_name)\n",
     "\n",
     "    # 选择第一个表格\n",
@@ -134,7 +136,7 @@
     "        else:\n",
     "            append_rows.append(\"\")\n",
     "\n",
-    "    workbook = xlrd.open_workbook('定性模型数据项12-11.xls')\n",
+    "    workbook = xlrd.open_workbook('定性模型数据项12-11.xlsx')\n",
     "\n",
     "    # 获取所有sheet的个数\n",
     "    sheet_count = len(workbook.sheet_names())\n",
@@ -173,10 +175,125 @@
     "                new_sheet.write(row_count, col, append_rows[col])\n",
     "\n",
     "    # 保存新的xls文件\n",
-    "    new_workbook.save(\"定性模型数据项12-11.xls\")\n",
+    "    new_workbook.save(\"定性模型数据项12-11.xlsx\")\n",
+    "\n",
+    "def updateYesterdayExcelData(date='', token=None):\n",
+    "    # 使用pandas读取Excel文件\n",
+    "    df = pd.read_excel(read_file_path_name, engine='openpyxl')\n",
+    "\n",
+    "    # 获取第二行的数据作为列名\n",
+    "    one_cols = df.iloc[0,:].tolist()\n",
+    "\n",
+    "    # 获取当前日期的前一天\n",
+    "    if date == '':\n",
+    "        previous_date = (datetime.now() - timedelta(days=1)).strftime('%Y-%m-%d')\n",
+    "    else:\n",
+    "        # 字符串转日期\n",
+    "        previous_date = (datetime.strptime(date, \"%Y-%m-%d\")-timedelta(days=1)).strftime('%Y-%m-%d')\n",
+    "    \n",
+    "\n",
+    "    cur_time, cur_time2 = getNow(previous_date)\n",
+    "    search_data = {\n",
+    "        \"data\": {\n",
+    "            \"date\": cur_time,\n",
+    "            \"dataItemNoList\": one_cols[1:]\n",
+    "        },\n",
+    "        \"funcModule\": \"数据项\",\n",
+    "        \"funcOperation\": \"查询\"\n",
+    "    }\n",
+    "    headers = {\"Authorization\": token}\n",
+    "    search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))\n",
+    "    search_value = json.loads(search_res.text)[\"data\"]\n",
+    "    if search_value:\n",
+    "        datas = search_value\n",
+    "    else:\n",
+    "        datas = None\n",
+    "\n",
+    "    append_rows = [cur_time2]\n",
+    "    dataItemNo_dataValue = {}\n",
+    "    for data_value in datas:\n",
+    "        if \"dataValue\" not in data_value:\n",
+    "            print(data_value)\n",
+    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n",
+    "        else:\n",
+    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
+    "    for value in one_cols[1:]:\n",
+    "        if value in dataItemNo_dataValue:\n",
+    "            append_rows.append(dataItemNo_dataValue[value])\n",
+    "        else:\n",
+    "            append_rows.append(\"\")\n",
+    "\n",
+    "    print('更新数据前')\n",
+    "    print(df.tail(1))\n",
+    "    # 检查日期是否已存在于数据中\n",
+    "    if previous_date not in df['日期'].values:\n",
+    "        # 将新的数据添加到DataFrame中\n",
+    "        new_row = pd.DataFrame([append_rows], columns=df.columns.tolist())\n",
+    "        df = pd.concat([df, new_row], ignore_index=True)\n",
+    "    else:\n",
+    "        # 更新现有数据\n",
+    "        print('日期存在，即将更新')\n",
+    "        print('新数据',append_rows[1:])\n",
+    "        df.loc[df['日期'] == previous_date, df.columns.tolist()[1:]] = append_rows[1:]\n",
+    "\n",
+    "    print('更新数据后')\n",
+    "    print(df.tail(1))\n",
+    "    # 使用pandas保存Excel文件\n",
+    "    df.to_excel(\"定性模型数据项12-11.xlsx\", index=False, engine='openpyxl')\n",
+    "\n",
+    "\n",
+    "def updateExcelData(date='', token=None):\n",
+    "    # 使用pandas读取Excel文件\n",
+    "    df = pd.read_excel(read_file_path_name, engine='openpyxl')\n",
+    "\n",
+    "    # 获取第一行的数据作为列名\n",
+    "    # one_cols = df.columns.tolist()\n",
+    "    \n",
+    "    # 获取第二行的数据作为列名\n",
+    "    one_cols = df.iloc[0,:].tolist()\n",
+    "\n",
+    "    cur_time, cur_time2 = getNow(date)\n",
+    "    search_data = {\n",
+    "        \"data\": {\n",
+    "            \"date\": cur_time,\n",
+    "            \"dataItemNoList\": one_cols[1:]\n",
+    "        },\n",
+    "        \"funcModule\": \"数据项\",\n",
+    "        \"funcOperation\": \"查询\"\n",
+    "    }\n",
+    "    headers = {\"Authorization\": token}\n",
+    "    search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))\n",
+    "    search_value = json.loads(search_res.text)[\"data\"]\n",
+    "    if search_value:\n",
+    "        datas = search_value\n",
+    "    else:\n",
+    "        datas = None\n",
+    "\n",
+    "    append_rows = [cur_time2]\n",
+    "    dataItemNo_dataValue = {}\n",
+    "    for data_value in datas:\n",
+    "        if \"dataValue\" not in data_value:\n",
+    "            print(data_value)\n",
+    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n",
+    "        else:\n",
+    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
+    "    for value in one_cols[1:]:\n",
+    "        if value in dataItemNo_dataValue:\n",
+    "            append_rows.append(dataItemNo_dataValue[value])\n",
+    "        else:\n",
+    "            append_rows.append(\"\")\n",
+    "\n",
+    "    # 将新的数据添加到DataFrame中\n",
+    "    new_row = pd.DataFrame([append_rows], columns=df.columns.tolist())\n",
+    "    df = pd.concat([df, new_row], ignore_index=True)\n",
+    "    # df = df.append(pd.Series(append_rows), ignore_index=True)\n",
+    "\n",
+    "    # 使用pandas保存Excel文件\n",
+    "    df.to_excel(\"定性模型数据项12-11.xlsx\", index=False, engine='openpyxl')\n",
+    "\n",
     "\n",
     "def qualitativeModel():\n",
-    "    df = pd.read_excel('定性模型数据项12-11.xls')\n",
+    "    df = pd.read_excel('定性模型数据项12-11.xlsx')\n",
     "\n",
     "    df=df.fillna(df.ffill())\n",
     "    df1 = df[-2:].reset_index()\n",
@@ -185,21 +302,31 @@
     "    2025年1月8日 修改：\n",
     "        复盘分析后发现2024-7月开始，开工率数据从0.28 变为了28 ，改为下面的判断规则\n",
     "    '''\n",
-    "    if df1.loc[1,'70号沥青开工率'] > 30:\n",
-    "        a = (df1.loc[1,'70号沥青开工率']-0.2)*5/0.1\n",
+    "    if df1.loc[1,'70号沥青开工率'] / 100  > 0.3:\n",
+    "        a = (df1.loc[1,'70号沥青开工率'] / 100 -0.2)*5/0.1\n",
     "    else :\n",
     "        a = 0\n",
     "    b = df1.loc[1,'资金因素']\n",
+    "    \n",
+    "    print('昨日计划提货偏差改之前',df1.loc[1,'昨日计划提货偏差'])\n",
+    "    # 昨日计划提货偏差 = 京博产量 - 计划产量\n",
+    "    df1.loc[1,'昨日计划提货偏差'] = df1.loc[1,'京博产量'] - df1.loc[1,'计划产量']\n",
+    "    \n",
+    "    print('昨日计划提货偏差改之后',df1.loc[1,'昨日计划提货偏差'])\n",
     "    if df1.loc[1,'昨日计划提货偏差']>0:\n",
     "        c = df1.loc[1,'昨日计划提货偏差']*10/2000\n",
     "    else :\n",
     "        c = df1.loc[1,'昨日计划提货偏差']*10/3000\n",
-    "    d = df1.loc[1,'生产情况']\n",
+    "        \n",
+    "    # 生产情况 = (京博产量 - 计划产量)/500*5\n",
+    "    d = (df1.loc[1,'京博产量'] - df1.loc[1,'计划产量']) / 500 * 5\n",
+    "    \n",
     "    if df1.loc[1,'基质沥青库存']/265007 >0.8:\n",
     "        e = (df1.loc[1,'基质沥青库存'] - df1.loc[0,'基质沥青库存'])*10/-5000\n",
     "    else : \n",
     "        e = 0\n",
-    "    f = df1.loc[1,'下游客户价格预期']\n",
+    "#     f = df1.loc[1,'下游客户价格预期']\n",
+    "    f = 1 # 2025年1月23日修改：价格预期都按1计算\n",
     "    if abs(df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])>=100:\n",
     "        g = (df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])*50/100\n",
     "    else :\n",
@@ -213,7 +340,10 @@
     "    if date == '':\n",
     "        now = datetime.now() - timedelta(days=offset)\n",
     "    else:\n",
-    "        date = datetime.strptime(date, \"%Y-%m-%d\")\n",
+    "        try:\n",
+    "            date = datetime.strptime(date, \"%Y-%m-%d\")\n",
+    "        except:\n",
+    "            date = datetime.strptime(date, \"%Y%m%d\")\n",
     "        now = date\n",
     "\n",
     "    year = now.year\n",
@@ -293,7 +423,7 @@
     "        else:\n",
     "            append_rows.append(\"\")\n",
     "\n",
-    "    workbook = xlrd.open_workbook('定性模型数据项12-11.xls')\n",
+    "    workbook = xlrd.open_workbook('定性模型数据项12-11.xlsx')\n",
     "\n",
     "    # 获取所有sheet的个数\n",
     "    sheet_count = len(workbook.sheet_names())\n",
@@ -332,11 +462,11 @@
     "                new_sheet.write(row_count, col, append_rows[col])\n",
     "\n",
     "    # 保存新的xls文件\n",
-    "    new_workbook.save(\"定性模型数据项12-11.xls\")\n",
+    "    new_workbook.save(\"定性模型数据项12-11.xlsx\")\n",
     "\n",
-    "    update_e_value('定性模型数据项12-11.xls', 8, 1000)\n",
+    "    update_e_value('定性模型数据项12-11.xlsx', 8, 1000)\n",
     "\n",
-    "    df = pd.read_excel('定性模型数据项12-11.xls')\n",
+    "    df = pd.read_excel('定性模型数据项12-11.xlsx')\n",
     "\n",
     "    df=df.fillna(df.ffill())\n",
     "    df1 = df[-2:].reset_index()\n",
@@ -359,7 +489,8 @@
     "        e = (df1.loc[1,'基质沥青库存'] - df1.loc[0,'基质沥青库存'])*10/-5000\n",
     "    else : \n",
     "        e = 0\n",
-    "    f = df1.loc[1,'下游客户价格预期']\n",
+    "#     f = df1.loc[1,'下游客户价格预期']\n",
+    "    f = 1 # 2025年1月23日修改：价格预期都按1计算\n",
     "    if abs(df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])>=100:\n",
     "        g = (df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])*50/100\n",
     "    else :\n",
@@ -459,7 +590,7 @@
     "        else:\n",
     "            append_rows.append(\"\")\n",
     "\n",
-    "    workbook = xlrd.open_workbook('定性模型数据项12-11.xls')\n",
+    "    workbook = xlrd.open_workbook('定性模型数据项12-11.xlsx')\n",
     "\n",
     "    # 获取所有sheet的个数\n",
     "    sheet_count = len(workbook.sheet_names())\n",
@@ -497,11 +628,11 @@
     "                new_sheet.write(row_count, col, append_rows[col])\n",
     "\n",
     "    # 保存新的xls文件\n",
-    "    new_workbook.save(\"定性模型数据项12-11.xls\")\n",
+    "    new_workbook.save(\"定性模型数据项12-11.xlsx\")\n",
     "    \n",
-    "    update_e_value('定性模型数据项12-11.xls', 8, 1000)\n",
+    "    update_e_value('定性模型数据项12-11.xlsx', 8, 1000)\n",
     "\n",
-    "    df = pd.read_excel('定性模型数据项12-11.xls')\n",
+    "    df = pd.read_excel('定性模型数据项12-11.xlsx')\n",
     "    df=df.fillna(df.ffill())\n",
     "    df1 = df[-2:].reset_index()\n",
     "    # if df1.loc[1,'70号沥青开工率'] > 0.3:   -- 2025年1月9日 发版更改\n",
@@ -519,38 +650,204 @@
     "        e = (df1.loc[1,'基质沥青库存'] - df1.loc[0,'基质沥青库存'])*10/-5000\n",
     "    else : \n",
     "        e = 0\n",
-    "    f = df1.loc[1,'下游客户价格预期']\n",
+    "#     f = df1.loc[1,'下游客户价格预期']\n",
+    "    f = 1 # 2025年1月23日修改：价格预期都按1计算\n",
     "    if abs(df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])>=100:\n",
     "        g = (df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])*50/100\n",
     "    else :\n",
     "        g = 0\n",
     "    h = df1.loc[1,'订单结构']\n",
     "    x = round(0.08*a+0*b+0.15*c+0.08*d +0.03*e +0.08*f +0.4*g+0.18*h+df1.loc[0,'京博指导价'],2)\n",
+    "    \n",
+    "\n",
+    "    # login_res1 = requests.post(url=login_url, json=login_data, timeout=(3, 30))\n",
+    "    # text1 = json.loads(login_res1.text)\n",
+    "    # token_push = text1[\"data\"][\"accessToken\"]\n",
     "\n",
     "\n",
-    "    login_res1 = requests.post(url=login_url, json=login_data, timeout=(3, 30))\n",
-    "    text1 = json.loads(login_res1.text)\n",
-    "    token_push = text1[\"data\"][\"accessToken\"]\n",
+    "    # data1 = {\n",
+    "    #     \"funcModule\": \"数据表信息列表\",\n",
+    "    #     \"funcOperation\": \"新增\",\n",
+    "    #     \"data\": [\n",
+    "    #         {\"dataItemNo\": \"C01100036|Forecast_Price|DX|ACN\",\n",
+    "    #          \"dataDate\": cur_time,\n",
+    "    #          \"dataStatus\": \"add\",\n",
+    "    #          \"dataValue\": x\n",
+    "    #          }\n",
+    "\n",
+    "    #     ]\n",
+    "    # }\n",
+    "    # headers1 = {\"Authorization\": token_push}\n",
+    "    # res = requests.post(url=upload_url, headers=headers1, json=data1, timeout=(3, 5))\n",
+    "    \n",
+    "    \n",
     "\n",
     "\n",
-    "    data1 = {\n",
-    "        \"funcModule\": \"数据表信息列表\",\n",
-    "        \"funcOperation\": \"新增\",\n",
-    "        \"data\": [\n",
-    "            {\"dataItemNo\": \"C01100036|Forecast_Price|DX|ACN\",\n",
-    "             \"dataDate\": cur_time,\n",
-    "             \"dataStatus\": \"add\",\n",
-    "             \"dataValue\": x\n",
-    "             }\n",
+    "def start_test():\n",
+    "    workbook = xlrd.open_workbook(read_file_path_name)\n",
     "\n",
-    "        ]\n",
+    "\n",
+    "\n",
+    "    # 选择第一个表格\n",
+    "    sheet = workbook.sheet_by_index(0)\n",
+    "\n",
+    "    # 获取行数和列数\n",
+    "    num_rows = sheet.nrows\n",
+    "\n",
+    "\n",
+    "\n",
+    "    row_data = sheet.row_values(1)\n",
+    "    one_cols = row_data\n",
+    "\n",
+    "\n",
+    "    login_res = requests.post(url=login_url, json=login_data, timeout=(3, 5))\n",
+    "    text = json.loads(login_res.text)\n",
+    "    if text[\"status\"]:\n",
+    "        token = text[\"data\"][\"accessToken\"]\n",
+    "    else:\n",
+    "        print(\"获取认证失败\")\n",
+    "        token =  None\n",
+    "\n",
+    "\n",
+    "    now = datetime.now()\n",
+    "    year = now.year\n",
+    "    month = now.month\n",
+    "    day = now.day\n",
+    "\n",
+    "    if month < 10:\n",
+    "        month = \"0\" + str(month)\n",
+    "    if day < 10:\n",
+    "        day = \"0\" + str(day)\n",
+    "    cur_time = str(year) + str(month) + str(day)\n",
+    "    cur_time2 = str(year) + \"-\" + str(month) + \"-\" + str(day)\n",
+    "    search_data = {\n",
+    "        \"data\": {\n",
+    "            \"date\": cur_time,\n",
+    "            \"dataItemNoList\": one_cols[1:]\n",
+    "        },\n",
+    "        \"funcModule\": \"数据项\",\n",
+    "        \"funcOperation\": \"查询\"\n",
     "    }\n",
-    "    headers1 = {\"Authorization\": token_push}\n",
-    "    res = requests.post(url=upload_url, headers=headers1, json=data1, timeout=(3, 5))\n",
+    "    headers = {\"Authorization\": token}\n",
+    "    search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))\n",
+    "    search_value = json.loads(search_res.text)[\"data\"]\n",
+    "#     datas = search_value\n",
+    "    if search_value:\n",
+    "        datas = search_value\n",
+    "    else :\n",
+    "        datas = None\n",
+    "   \n",
+    "\n",
+    "    append_rows = [cur_time2]\n",
+    "    dataItemNo_dataValue = {}\n",
+    "#     for data_value in datas:\n",
+    "#         dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
+    "    for data_value in datas:\n",
+    "        if \"dataValue\" not in data_value:\n",
+    "            print(data_value)\n",
+    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n",
+    "        else:\n",
+    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
+    "    for value in one_cols[1:]:\n",
+    "        if value in dataItemNo_dataValue:\n",
+    "            append_rows.append(dataItemNo_dataValue[value])\n",
+    "        else:\n",
+    "            append_rows.append(\"\")\n",
+    "\n",
+    "    workbook = xlrd.open_workbook('定性模型数据项12-11.xlsx')\n",
+    "\n",
+    "    # 获取所有sheet的个数\n",
+    "    sheet_count = len(workbook.sheet_names())\n",
+    "\n",
+    "    # 获取所有sheet的名称\n",
+    "    sheet_names = workbook.sheet_names()\n",
+    "\n",
+    "    new_workbook = xlwt.Workbook()\n",
+    "    for i in range(sheet_count):\n",
+    "        # 获取当前sheet\n",
+    "        sheet = workbook.sheet_by_index(i)\n",
+    "\n",
+    "        # 获取sheet的行数和列数\n",
+    "        row_count = sheet.nrows\n",
+    "        col_count = sheet.ncols\n",
+    "        # 获取原有数据\n",
+    "        data = []\n",
+    "        for row in range(row_count):\n",
+    "            row_data = []\n",
+    "            for col in range(col_count):\n",
+    "                row_data.append(sheet.cell_value(row, col))\n",
+    "            data.append(row_data)\n",
+    "        # 创建xlwt的Workbook对象\n",
+    "        # 创建sheet\n",
+    "        new_sheet = new_workbook.add_sheet(sheet_names[i])\n",
+    "\n",
+    "        # 将原有的数据写入新的sheet\n",
+    "        for row in range(row_count):\n",
+    "            for col in range(col_count):\n",
+    "                new_sheet.write(row, col, data[row][col])\n",
+    "\n",
+    "        if i == 0:\n",
+    "            # 在新的sheet中添加数据\n",
+    "            for col in range(col_count):\n",
+    "                new_sheet.write(row_count, col, append_rows[col])\n",
+    "\n",
+    "    # 保存新的xls文件\n",
+    "    new_workbook.save(\"定性模型数据项12-11.xlsx\")\n",
+    "    \n",
+    "    update_e_value('定性模型数据项12-11.xlsx', 8, 1000)\n",
+    "\n",
+    "    df = pd.read_excel('定性模型数据项12-11.xlsx')\n",
+    "    df=df.fillna(df.ffill())\n",
+    "    df1 = df[-2:].reset_index()\n",
+    "    # if df1.loc[1,'70号沥青开工率'] > 0.3:   -- 2025年1月9日 发版更改\n",
+    "    if df1.loc[1,'70号沥青开工率'] / 100  > 0.3:\n",
+    "        a = (df1.loc[1,'70号沥青开工率'] / 100 -0.2)*5/0.1\n",
+    "    else :\n",
+    "        a = 0\n",
+    "    b = df1.loc[1,'资金因素']\n",
+    "    if df1.loc[1,'昨日计划提货偏差']>0:\n",
+    "        c = df1.loc[1,'昨日计划提货偏差']*10/2000\n",
+    "    else :\n",
+    "        c = df1.loc[1,'昨日计划提货偏差']*10/3000\n",
+    "    d = df1.loc[1,'生产情况']\n",
+    "    if df1.loc[1,'基质沥青库存']/265007 >0.8:\n",
+    "        e = (df1.loc[1,'基质沥青库存'] - df1.loc[0,'基质沥青库存'])*10/-5000\n",
+    "    else : \n",
+    "        e = 0\n",
+    "#     f = df1.loc[1,'下游客户价格预期']\n",
+    "    f = 1 # 2025年1月23日修改：价格预期都按1计算\n",
+    "    if abs(df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])>=100:\n",
+    "        g = (df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])*50/100\n",
+    "    else :\n",
+    "        g = 0\n",
+    "    h = df1.loc[1,'订单结构']\n",
+    "    x = round(0.08*a+0*b+0.15*c+0.08*d +0.03*e +0.08*f +0.4*g+0.18*h+df1.loc[0,'京博指导价'],2)\n",
+    "    \n",
+    "\n",
+    "    # login_res1 = requests.post(url=login_url, json=login_data, timeout=(3, 30))\n",
+    "    # text1 = json.loads(login_res1.text)\n",
+    "    # token_push = text1[\"data\"][\"accessToken\"]\n",
+    "\n",
+    "\n",
+    "    # data1 = {\n",
+    "    #     \"funcModule\": \"数据表信息列表\",\n",
+    "    #     \"funcOperation\": \"新增\",\n",
+    "    #     \"data\": [\n",
+    "    #         {\"dataItemNo\": \"C01100036|Forecast_Price|DX|ACN\",\n",
+    "    #          \"dataDate\": cur_time,\n",
+    "    #          \"dataStatus\": \"add\",\n",
+    "    #          \"dataValue\": x\n",
+    "    #          }\n",
+    "\n",
+    "    #     ]\n",
+    "    # }\n",
+    "    # headers1 = {\"Authorization\": token_push}\n",
+    "    # res = requests.post(url=upload_url, headers=headers1, json=data1, timeout=(3, 5))\n",
     "    \n",
     "    \n",
     "\n",
     "\n",
+    "\n",
     "def start_1():\n",
     "    workbook = xlrd.open_workbook(read_file_path_name)\n",
     "\n",
@@ -623,7 +920,7 @@
     "        else:\n",
     "            append_rows.append(\"\")\n",
     "\n",
-    "    workbook = xlrd.open_workbook('定性模型数据项12-11.xls')\n",
+    "    workbook = xlrd.open_workbook('定性模型数据项12-11.xlsx')\n",
     "\n",
     "    # 获取所有sheet的个数\n",
     "    sheet_count = len(workbook.sheet_names())\n",
@@ -661,7 +958,7 @@
     "                new_sheet.write(row_count, col, append_rows[col])\n",
     "\n",
     "    # 保存新的xls文件\n",
-    "    new_workbook.save(\"定性模型数据项12-11.xls\")\n",
+    "    new_workbook.save(\"定性模型数据项12-11.xlsx\")\n",
     "\n",
     "\n",
     "\n",
@@ -670,10 +967,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
+    "### 原始代码备份\n",
+    "\n",
     "# if __name__ == \"__main__\":\n",
     "\n",
     "    # 需要单独运行放开\n",
@@ -701,32 +1000,142 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# 重新上传定性数据\n",
+    "# if __name__ == \"__main__\":\n",
+    "#     def main():\n",
+    "#         # 获取当前日期\n",
+    "#         date = datetime.now().date()\n",
+    "#         date = date.strftime('%Y%m%d')\n",
+    "#         # 获取登录token\n",
+    "#         token = getLogToken()\n",
+    "#         updateExcelData(date,token)\n",
+    "#         update_e_value('定性模型数据项12-11.xlsx', 8, 1000)\n",
+    "#         x = qualitativeModel()\n",
+    "#         print('预测结果：',x)\n",
+    "#         cur_time,cur_time2 = getNow(date)\n",
+    "#         pushData(cur_time,x,token)\n",
     "\n",
-    "from datetime import datetime, timedelta\n",
+    "#     print(\"运行中...\")\n",
+    "#     # 每天定时12点运行\n",
+    "#     while True:\n",
+    "#         # 获取当前时间\n",
+    "#         current_time = time.strftime(\"%H:%M:%S\", time.localtime())\n",
+    "#         try:\n",
+    "#             # 判断当前时间是否为执行任务的时间点\n",
+    "#             if current_time == \"12:00:00\":\n",
+    "#                 try:\n",
+    "#                     main()\n",
+    "#                 except Exception as e:\n",
+    "#                     print(f\"12点执行失败: {e}\")\n",
+    "#                     # 等待到12点30分再次执行\n",
+    "#                     while current_time != \"12:30:00\":\n",
+    "#                         current_time = time.strftime(\"%H:%M:%S\", time.localtime())\n",
+    "#                         time.sleep(1)\n",
+    "#                     try:\n",
+    "#                         main()\n",
+    "#                     except Exception as e:\n",
+    "#                         print(f\"12点30分也执行失败: {e}\")\n",
+    "#             elif current_time == \"20:00:00\":\n",
+    "#                 print(\"更新前一天数据\")\n",
+    "#                 token = getLogToken()\n",
+    "#                 updateYesterdayExcelData(token=token)\n",
+    "                \n",
+    "#             time.sleep(1)\n",
+    "#         except Exception as e:\n",
+    "#             print(f\"执行失败: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # 重新上传定性数据\n",
     "\n",
-    "def main(date='',token=None):\n",
-    "    updateExcelData(date,token)\n",
-    "    update_e_value('定性模型数据项12-11.xls', 8, 1000)\n",
-    "    x = qualitativeModel()\n",
-    "    cur_time,cur_time2 = getNow(date)\n",
-    "    pushData(cur_time,x,token)\n",
+    "# def main(date='',token=None):\n",
+    "#     updateExcelData(date,token)\n",
+    "#     update_e_value('定性模型数据项12-11.xlsx', 8, 1000)\n",
+    "#     x = qualitativeModel()\n",
+    "#     print('**************************************************预测结果：',x)\n",
+    "#     cur_time,cur_time2 = getNow(date)\n",
+    "#     pushData(cur_time,x,token)\n",
     "\n",
     "\n",
-    "start_date = datetime(2024, 7, 1)\n",
-    "end_date = datetime(2025, 1, 1)\n",
-    "token = getLogToken()\n",
-    "while start_date < end_date:\n",
-    "    print(start_date.strftime('%Y%m%d'))\n",
-    "    main(start_date.strftime('%Y%m%d'),token)\n",
-    "    start_date += timedelta(days=1)\n",
-    "    time.sleep(1)\n",
+    "# start_date = datetime(2025, 1, 15)\n",
+    "# end_date = datetime(2025, 1, 24)\n",
+    "# token = getLogToken()\n",
+    "# while start_date < end_date:\n",
+    "#     print(start_date.strftime('%Y%m%d'))\n",
+    "#     main(start_date.strftime('%Y%m%d'),token)\n",
+    "#     start_date += timedelta(days=1)\n",
+    "#     time.sleep(5)\n",
     "    \n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "更新数据前\n",
+      "              日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期       即期成本  \\\n",
+      "1326  2025-01-23  3650        6    6      NaN  NaN    NaN     3650  3846.3643   \n",
+      "\n",
+      "     订单结构      计划产量 京博产量  \n",
+      "1326    1  4505.365  NaN  \n",
+      "日期存在，即将更新\n",
+      "新数据 [3650.0, '', '', '', '', '', 3650.0, 3846.3643, 1.0, 4505.365, '']\n",
+      "更新数据后\n",
+      "              日期   京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期  \\\n",
+      "1326  2025-01-23  3650.0                                      3650.0   \n",
+      "\n",
+      "           即期成本 订单结构      计划产量 京博产量  \n",
+      "1326  3846.3643  1.0  4505.365       \n",
+      "更新完了\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 调试更新数据\n",
+    "date = '2025-01-24'\n",
+    "token = getLogToken()\n",
+    "updateYesterdayExcelData(date=date,token=token)\n",
+    "print('更新完了')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # 快速调试线上\n",
+    "# def main():\n",
+    "#     # 获取当前日期\n",
+    "#     date = datetime.now().date()\n",
+    "#     date = date.strftime('%Y%m%d')\n",
+    "#     # 获取登录token\n",
+    "#     token = getLogToken()\n",
+    "#     updateExcelData(date,token)\n",
+    "#     update_e_value('定性模型数据项12-11.xlsx', 8, 1000)\n",
+    "#     x = qualitativeModel()\n",
+    "#     print('预测结果：',x)\n",
+    "#     cur_time,cur_time2 = getNow(date)\n",
+    "#     pushData(cur_time,x,token)\n",
+    "\n",
+    "# current_time = time.strftime(\"%H:%M:%S\", time.localtime())\n",
+    "# main()\n",
+    "# token = getLogToken()\n",
+    "# updateYesterdayExcelData(token=token)"
+   ]
   }
  ],
  "metadata": {
diff --git a/aisenzhecode/液化石油气/化工品价格预测准确率.xlsx b/aisenzhecode/液化石油气/化工品价格预测准确率.xlsx
new file mode 100644
index 0000000..31df769
Binary files /dev/null and b/aisenzhecode/液化石油气/化工品价格预测准确率.xlsx differ
diff --git a/aisenzhecode/液化石油气/日度价格预测_液化气最佳模型.pkl b/aisenzhecode/液化石油气/日度价格预测_液化气最佳模型.pkl
index f2c685e..27f0f01 100644
Binary files a/aisenzhecode/液化石油气/日度价格预测_液化气最佳模型.pkl and b/aisenzhecode/液化石油气/日度价格预测_液化气最佳模型.pkl differ
diff --git a/aisenzhecode/液化石油气/液化气价格预测ytj.ipynb b/aisenzhecode/液化石油气/液化气价格预测ytj.ipynb
index 68e16bb..aa07da2 100644
--- a/aisenzhecode/液化石油气/液化气价格预测ytj.ipynb
+++ b/aisenzhecode/液化石油气/液化气价格预测ytj.ipynb
@@ -2,20 +2,30 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\statsmodels\\compat\\pandas.py:49: FutureWarning:\n",
+      "\n",
+      "The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n",
+      "\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
        "        <script type=\"text/javascript\">\n",
        "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
-       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
+       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
        "        if (typeof require !== 'undefined') {\n",
        "        require.undef(\"plotly\");\n",
        "        requirejs.config({\n",
        "            paths: {\n",
-       "                'plotly': ['https://cdn.plot.ly/plotly-2.12.1.min']\n",
+       "                'plotly': ['https://cdn.plot.ly/plotly-2.2.0.min']\n",
        "            }\n",
        "        });\n",
        "        require(['plotly'], function(Plotly) {\n",
@@ -35,7 +45,7 @@
     "import json\n",
     "import xlrd\n",
     "import xlwt\n",
-    "from datetime import datetime\n",
+    "from datetime import datetime,timedelta\n",
     "import time\n",
     "# 变量定义\n",
     "login_url = \"http://10.200.32.39/jingbo-api/api/server/login\"\n",
@@ -95,7 +105,6 @@
     "from sklearn.linear_model import Lasso\n",
     "from xgboost import XGBRegressor\n",
     "\n",
-    "import datetime\n",
     "import statsmodels.api as sm\n",
     "try:\n",
     "    from keras.preprocessing.sequence import TimeseriesGenerator\n",
@@ -150,7 +159,7 @@
     "def get_data_value(token, dataItemNoList,date):\n",
     "    search_data = {\n",
     "        \"data\": {\n",
-    "            \"date\": get_cur_time(date)[0],\n",
+    "            \"date\": date,\n",
     "            \"dataItemNoList\": dataItemNoList\n",
     "        },\n",
     "        \"funcModule\": \"数据项\",\n",
@@ -158,8 +167,11 @@
     "    }\n",
     "    headers = {\"Authorization\": token}\n",
     "    search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))\n",
+    "    print(search_data)\n",
+    "    \n",
     "    try:\n",
     "        search_value = json.loads(search_res.text)[\"data\"]\n",
+    "        print(search_value)\n",
     "    except json.JSONDecodeError as e:\n",
     "        print(f\"Error decoding JSON: {e}\")\n",
     "        print(\"Response content:\", search_res.text)\n",
@@ -196,8 +208,7 @@
     "\n",
     "def get_cur_time(date = ''):\n",
     "    if date == '':\n",
-    "        import datetime\n",
-    "        now = datetime.datetime.now()\n",
+    "        now = datetime.now()\n",
     "    else:\n",
     "        now = date\n",
     "    year = now.year\n",
@@ -234,7 +245,7 @@
     "        \"funcOperation\": \"新增\",\n",
     "        \"data\": [\n",
     "            {\"dataItemNo\": \"250855713|Forecast_Price|ACN\",\n",
-    "             \"dataDate\": get_cur_time(date)[0],\n",
+    "             \"dataDate\": date,\n",
     "             \"dataStatus\": \"add\",\n",
     "             \"dataValue\": forecast_price()\n",
     "             }\n",
@@ -243,7 +254,7 @@
     "    }\n",
     "    headers = {\"Authorization\": token_push}\n",
     "    res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5))\n",
-    "    print(res.text)\n",
+    "    # print(res.text)\n",
     "\n",
     "    \n",
     "# def upload_data_to_system(token):\n",
@@ -263,7 +274,7 @@
     "#     res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5))\n",
     "#     print(res.text)\n",
     "\n",
-    "    \n",
+    "price_list = []\n",
     "    \n",
     "def forecast_price():\n",
     "    # df_test = pd.read_csv('定价模型数据收集0212.csv')\n",
@@ -307,6 +318,7 @@
     "    a = a[0]\n",
     "    a = float(a)\n",
     "    a = round(a,2)\n",
+    "    price_list.append(a)\n",
     "    return a\n",
     "def optimize_Model():\n",
     "    from sklearn.model_selection import train_test_split\n",
@@ -348,7 +360,6 @@
     "    from sklearn.linear_model import Lasso\n",
     "    from xgboost import XGBRegressor\n",
     "\n",
-    "    from datetime import datetime\n",
     "    import statsmodels.api as sm\n",
     "    try:\n",
     "        from keras.preprocessing.sequence import TimeseriesGenerator\n",
@@ -516,9 +527,7 @@
     "\n",
     "\n",
     "\n",
-    "\n",
-    "\n",
-    "def start(date):\n",
+    "def start(date=''):\n",
     "    read_xls_data()\n",
     "\n",
     "    token = get_head_auth()\n",
@@ -527,13 +536,13 @@
     "    token_push = get_head_push_auth()\n",
     "    if not token_push:\n",
     "        return\n",
-    "\n",
-    "    datas = get_data_value(token, one_cols[1:],date)\n",
+    "    cur_time,cur_time2 = getNow(date)\n",
+    "    datas = get_data_value(token, one_cols[1:],cur_time)\n",
     "#     if not datas:\n",
     "#         return\n",
     "\n",
     "    # data_list = [two_cols, one_cols]\n",
-    "    append_rows = [get_cur_time(date)[1]]\n",
+    "    append_rows = [cur_time2]\n",
     "    dataItemNo_dataValue = {}\n",
     "    for data_value in datas:\n",
     "        if \"dataValue\" not in data_value:\n",
@@ -549,28 +558,50 @@
     "            append_rows.append(\"\")\n",
     "    save_xls(append_rows)\n",
     "    optimize_Model()\n",
-    "    upload_data_to_system(token_push,date)\n",
+    "    upload_data_to_system(token_push,cur_time)\n",
     "    \n",
     "    \n",
     "    \n",
     "    # data_list.append(three_cols)\n",
     "    # write_xls(data_list)\n",
     "\n",
+    "def getNow(date='',offset=0):\n",
+    "    if date == '':\n",
+    "        from datetime import datetime\n",
+    "        now = datetime.now() - timedelta(days=offset)\n",
+    "    else:\n",
+    "        try:\n",
+    "            date = datetime.strptime(date, \"%Y-%m-%d\")\n",
+    "        except:\n",
+    "            date = datetime.strptime(date, \"%Y%m%d\")\n",
+    "        now = date\n",
     "\n",
-    "def start_1():\n",
+    "    year = now.year\n",
+    "    month = now.month\n",
+    "    day = now.day\n",
+    "\n",
+    "    if month < 10:\n",
+    "        month = \"0\" + str(month)\n",
+    "    if day < 10:\n",
+    "        day = \"0\" + str(day)\n",
+    "    cur_time = str(year) + str(month) + str(day)\n",
+    "    cur_time2 = str(year) + \"-\" + str(month) + \"-\" + str(day)\n",
+    "    return cur_time,cur_time2\n",
+    "    \n",
+    "def start_1(date=''):\n",
     "    read_xls_data()\n",
     "\n",
     "    token = get_head_auth()\n",
     "    if not token:\n",
     "        return\n",
     "    \n",
-    "\n",
-    "    datas = get_data_value(token, one_cols[1:])\n",
+    "    cur_time,cur_time2 = getNow(date)\n",
+    "    datas = get_data_value(token, one_cols[1:],date=cur_time)\n",
     "#     if not datas:\n",
     "#         return\n",
     "\n",
     "    # data_list = [two_cols, one_cols]\n",
-    "    append_rows = [get_cur_time()[1]]\n",
+    "    append_rows = [cur_time2]\n",
     "    dataItemNo_dataValue = {}\n",
     "    for data_value in datas:\n",
     "        if \"dataValue\" not in data_value:\n",
@@ -688,71 +719,89 @@
     "\n",
     "    # 保存新的xls文件\n",
     "    new_workbook.save(\"液化气数据.xls\")\n",
-    "\n",
-    "\n",
-    "if __name__ == \"__main__\":\n",
-    "    pass\n",
-    "    # 需要单独运行放开\n",
-    "#      start()\n",
-    "\n",
-    "    # 每天定时12点运行\n",
-    "    # while True:\n",
-    "    #     # 获取当前时间\n",
-    "    #     current_time = time.strftime(\"%H:%M:%S\", time.localtime())\n",
-    "    #     current_time_1 = time.strftime(\"%H:%M:%S\", time.localtime())\n",
-    "\n",
-    "    #     # 判断当前时间是否为执行任务的时间点\n",
-    "    #     if current_time == \"09:15:00\":\n",
-    "    #         print(\"执行定时任务\")\n",
-    "    #         start()\n",
-    "\n",
-    "    #     # 休眠1秒钟，避免过多占用CPU资源\n",
-    "    #         time.sleep(1)\n",
-    "        \n",
-    "    #     elif current_time_1 == \"20:00:00\":\n",
-    "    #         print(\"更新数据\")\n",
-    "    #         start_1()\n",
-    "    #     time.sleep(1)\n",
-    "\n",
-    "\n",
-    "#     # 检测数据准确性, 需要检测放开\n",
-    "#     # check_data(\"100028098|LISTING_PRICE\")\n",
-    "#     # check_data(\"9137070016544622XB|DAY_Yield\")\n"
+    "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "20241213\n",
-      "{'dataDate': '20241213', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_20960\\2946673419.py:291: UserWarning:\n",
-      "\n",
-      "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
-      "\n",
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_20960\\2946673419.py:293: UserWarning:\n",
-      "\n",
-      "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using matplotlib backend: <object object at 0x0000016FC45DB050>\n",
-      "%pylab is deprecated, use %matplotlib inline and import the required libraries.\n",
+      "运行中\n",
+      "13:38:57\n",
+      "13:38:58\n",
+      "13:38:59\n",
+      "13:39:00\n",
+      "13:39:01\n",
+      "13:39:02\n",
+      "13:39:03\n",
+      "13:39:04\n",
+      "13:39:05\n",
+      "13:39:06\n",
+      "13:39:07\n",
+      "13:39:08\n",
+      "13:39:09\n",
+      "13:39:10\n",
+      "13:39:11\n",
+      "13:39:12\n",
+      "13:39:13\n",
+      "13:39:14\n",
+      "13:39:15\n",
+      "13:39:16\n",
+      "13:39:17\n",
+      "13:39:18\n",
+      "13:39:19\n",
+      "13:39:20\n",
+      "13:39:21\n",
+      "13:39:22\n",
+      "13:39:23\n",
+      "13:39:24\n",
+      "13:39:25\n",
+      "13:39:26\n",
+      "13:39:27\n",
+      "13:39:28\n",
+      "13:39:29\n",
+      "13:39:30\n",
+      "13:39:31\n",
+      "13:39:32\n",
+      "13:39:33\n",
+      "13:39:34\n",
+      "13:39:35\n",
+      "13:39:36\n",
+      "13:39:37\n",
+      "13:39:38\n",
+      "13:39:39\n",
+      "13:39:40\n",
+      "13:39:41\n",
+      "13:39:42\n",
+      "13:39:43\n",
+      "13:39:44\n",
+      "13:39:45\n",
+      "13:39:46\n",
+      "13:39:47\n",
+      "13:39:48\n",
+      "13:39:49\n",
+      "13:39:50\n",
+      "13:39:51\n",
+      "13:39:52\n",
+      "13:39:53\n",
+      "13:39:54\n",
+      "13:39:55\n",
+      "13:39:56\n",
+      "13:39:57\n",
+      "13:39:58\n",
+      "13:39:59\n",
+      "13:40:00\n",
+      "执行定时任务\n",
+      "{'data': {'date': '20250123', 'dataItemNoList': ['251926669|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|YEDAY_AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE\\n', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
+      "[{'dataDate': '20250123', 'dataItemNo': '251926669|STRIKE_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250123', 'dataItemNo': '91370305773165341A|C01100008|STRIKE_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250123', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 5430.0}, {'dataDate': '20250123', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 5535.0}]\n",
+      "Using matplotlib backend: Qt5Agg\n",
       "Populating the interactive namespace from numpy and matplotlib\n",
       "Fitting 3 folds for each of 180 candidates, totalling 540 fits\n"
      ]
@@ -761,18 +810,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\IPython\\core\\magics\\pylab.py:162: UserWarning:\n",
+      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\IPython\\core\\magics\\pylab.py:160: UserWarning:\n",
       "\n",
-      "pylab import has clobbered these variables: ['plot', 'datetime', 'random', '__version__']\n",
+      "pylab import has clobbered these variables: ['datetime', 'random', 'plot', '__version__']\n",
       "`%matplotlib` prevents importing * from pylab and numpy\n",
-      "\n",
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_20960\\2946673419.py:240: UserWarning:\n",
-      "\n",
-      "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
-      "\n",
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_20960\\2946673419.py:242: UserWarning:\n",
-      "\n",
-      "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
       "\n"
      ]
     },
@@ -781,232 +822,344 @@
      "output_type": "stream",
      "text": [
       "Date\n",
-      "2024-12-13    5284.853516\n",
-      "Name: 日度预测价格, dtype: float32\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_20960\\2946673419.py:274: FutureWarning:\n",
-      "\n",
-      "Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\"confirmFlg\":false,\"status\":true}\n",
-      "20241214\n",
-      "{'dataDate': '20241214', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_20960\\2946673419.py:291: UserWarning:\n",
-      "\n",
-      "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
-      "\n",
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_20960\\2946673419.py:293: UserWarning:\n",
-      "\n",
-      "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using matplotlib backend: QtAgg\n",
-      "%pylab is deprecated, use %matplotlib inline and import the required libraries.\n",
-      "Populating the interactive namespace from numpy and matplotlib\n",
-      "Fitting 3 folds for each of 180 candidates, totalling 540 fits\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_20960\\2946673419.py:240: UserWarning:\n",
-      "\n",
-      "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
-      "\n",
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_20960\\2946673419.py:242: UserWarning:\n",
-      "\n",
-      "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date\n",
-      "2024-12-14    5230.033691\n",
-      "Name: 日度预测价格, dtype: float32\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_20960\\2946673419.py:274: FutureWarning:\n",
-      "\n",
-      "Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\"confirmFlg\":false,\"status\":true}\n",
-      "20241215\n",
-      "{'dataDate': '20241215', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_20960\\2946673419.py:291: UserWarning:\n",
-      "\n",
-      "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
-      "\n",
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_20960\\2946673419.py:293: UserWarning:\n",
-      "\n",
-      "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using matplotlib backend: QtAgg\n",
-      "%pylab is deprecated, use %matplotlib inline and import the required libraries.\n",
-      "Populating the interactive namespace from numpy and matplotlib\n",
-      "Fitting 3 folds for each of 180 candidates, totalling 540 fits\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_20960\\2946673419.py:240: UserWarning:\n",
-      "\n",
-      "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
-      "\n",
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_20960\\2946673419.py:242: UserWarning:\n",
-      "\n",
-      "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date\n",
-      "2024-12-15    5229.949707\n",
-      "Name: 日度预测价格, dtype: float32\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_20960\\2946673419.py:274: FutureWarning:\n",
-      "\n",
-      "Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\"confirmFlg\":false,\"status\":true}\n",
-      "20241216\n",
-      "{'dataDate': '20241216', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_20960\\2946673419.py:291: UserWarning:\n",
-      "\n",
-      "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
-      "\n",
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_20960\\2946673419.py:293: UserWarning:\n",
-      "\n",
-      "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using matplotlib backend: QtAgg\n",
-      "%pylab is deprecated, use %matplotlib inline and import the required libraries.\n",
-      "Populating the interactive namespace from numpy and matplotlib\n",
-      "Fitting 3 folds for each of 180 candidates, totalling 540 fits\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_20960\\2946673419.py:240: UserWarning:\n",
-      "\n",
-      "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
-      "\n",
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_20960\\2946673419.py:242: UserWarning:\n",
-      "\n",
-      "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
-      "\n",
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_20960\\2946673419.py:274: FutureWarning:\n",
-      "\n",
-      "Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date\n",
-      "2024-12-16    5240.49707\n",
+      "2025-01-23    5399.734863\n",
       "Name: 日度预测价格, dtype: float32\n",
-      "{\"confirmFlg\":false,\"status\":true}\n"
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+      "13:44:47\n",
+      "13:44:48\n",
+      "13:44:49\n",
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+      "13:44:51\n",
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+      "13:44:57\n",
+      "13:44:58\n",
+      "13:44:59\n",
+      "13:45:00\n",
+      "更新数据\n",
+      "{'data': {'date': '20250123', 'dataItemNoList': ['251926669|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|YEDAY_AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE\\n', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
+      "[{'dataDate': '20250123', 'dataItemNo': '251926669|STRIKE_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250123', 'dataItemNo': '91370305773165341A|C01100008|STRIKE_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250123', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 5430.0}, {'dataDate': '20250123', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 5535.0}]\n",
+      "13:45:04\n",
+      "13:45:05\n",
+      "13:45:06\n",
+      "13:45:07\n",
+      "13:45:08\n",
+      "13:45:09\n",
+      "13:45:10\n",
+      "13:45:11\n",
+      "13:45:12\n",
+      "13:45:13\n",
+      "13:45:14\n"
      ]
     }
    ],
    "source": [
-    "from datetime import datetime, timedelta\n",
     "\n",
-    "start_date = datetime(2024, 12, 13)\n",
-    "end_date = datetime(2024, 12, 17)\n",
+    "if __name__ == \"__main__\":\n",
+    "    print('运行中')\n",
+    "    # 需要单独运行放开\n",
+    "#     start()\n",
+    "#     start_1(date='2025-01-22')\n",
+    "#     start_1()\n",
     "\n",
-    "while start_date < end_date:\n",
-    "    print(start_date.strftime('%Y%m%d'))\n",
-    "    start(start_date)\n",
-    "    # time.sleep(1)\n",
-    "    # start_2(start_date)\n",
-    "    start_date += timedelta(days=1)"
+    "    # 每天定时12点运行\n",
+    "    while True:\n",
+    "        try:\n",
+    "            # 获取当前时间\n",
+    "            current_time = time.strftime(\"%H:%M:%S\", time.localtime())\n",
+    "            current_time_1 = time.strftime(\"%H:%M:%S\", time.localtime())\n",
+    "#             print(current_time_1)\n",
+    "\n",
+    "            # 判断当前时间是否为执行任务的时间点\n",
+    "            if current_time == \"09:15:00\":\n",
+    "                print(\"执行定时任务\")\n",
+    "                start()\n",
+    "\n",
+    "                # 休眠1秒钟，避免过多占用CPU资源\n",
+    "                time.sleep(1)\n",
+    "\n",
+    "            elif current_time_1 == \"20:00:00\":\n",
+    "                print(\"更新数据\")\n",
+    "                start_1()\n",
+    "                time.sleep(1)\n",
+    "        except:\n",
+    "            print('执行错误')\n",
+    "        time.sleep(1)\n",
+    "\n",
+    "\n",
+    "    # 检测数据准确性, 需要检测放开\n",
+    "    # check_data(\"100028098|LISTING_PRICE\")\n",
+    "    # check_data(\"9137070016544622XB|DAY_Yield\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from datetime import datetime, timedelta\n",
+    "\n",
+    "# start_date = datetime(2025, 1, 21)\n",
+    "# end_date = datetime(2025, 1, 23)\n",
+    "\n",
+    "# while start_date < end_date:\n",
+    "#     print(start_date.strftime('%Y%m%d'))\n",
+    "#     start(start_date)\n",
+    "#     # time.sleep(1)\n",
+    "#     # start_2(start_date)\n",
+    "#     start_date += timedelta(days=1)\n",
+    "#     time.sleep(5)\n",
+    "\n",
+    "# print(price_list)"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "base",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -1020,7 +1173,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.7.0"
   }
  },
  "nbformat": 4,
diff --git a/aisenzhecode/液化石油气/液化气数据.xls b/aisenzhecode/液化石油气/液化气数据.xls
index 11e4832..23df85d 100644
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diff --git a/aisenzhecode/液化石油气/液化气数据2024年11月25日备份.xls b/aisenzhecode/液化石油气/液化气数据2024年11月25日备份.xls
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diff --git a/aisenzhecode/液化石油气/液化气数据手动添加数据.xlsx b/aisenzhecode/液化石油气/液化气数据手动添加数据.xlsx
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diff --git a/aisenzhecode/液化石油气/液化石油气报表2025年1月16日.xlsx b/aisenzhecode/液化石油气/液化石油气报表2025年1月16日.xlsx
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diff --git a/原油预测定时任务，请勿关闭.py b/原油预测定时任务，请勿关闭.py
index 880e5d3..41341c2 100644
--- a/原油预测定时任务，请勿关闭.py
+++ b/原油预测定时任务，请勿关闭.py
@@ -1,15 +1,22 @@
 # 定时执行cmd命令
-import os
-import time
-from main import predict_main
 
+import time
+import os
 while True:
     try:
         print(time.strftime('%H:%M'))
         # 判断是不是工作日且 是17：00 7：00  才执行
-        if time.strftime('%A') not in ['Saturday', 'Sunday'] and time.strftime('%H:%M') in [ '18:00']:
-            predict_main()
+        if time.strftime('%A') not in ['Saturday', 'Sunday'] and time.strftime('%H:%M') in ['07:50']:
+            # 要执行的命令
+            command = r"d:\yuanyouyuce\envs\predict\python main_yuanyou.py"  
+
+            # 调用系统命令
+            os.system(command)
         time.sleep(60)
     except:
         time.sleep(3600)
-        predict_main()
\ No newline at end of file
+        # 要执行的命令
+        command = r"d:\yuanyouyuce\envs\predict\python main_yuanyou.py"  
+
+        # 调用系统命令
+        os.system(command)
\ No newline at end of file