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 6794dda..6dae873 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 620599c..ecf3db8 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
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -10,12 +10,12 @@
       "text/html": [
        "        <script type=\"text/javascript\">\n",
        "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
-       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
+       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.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-2.2.0.min']\n",
+       "                'plotly': ['https://cdn.plot.ly/plotly-2.12.1.min']\n",
        "            }\n",
        "        });\n",
        "        require(['plotly'], function(Plotly) {\n",
@@ -196,36 +196,19 @@
     "        \"funcOperation\": \"新增\",\n",
     "        \"data\": [\n",
     "            {\"dataItemNo\": \"250855713|Forecast_Price|ACN\",\n",
-    "             \"dataDate\": date,\n",
+    "             \"dataDate\": getNow(date=date)[0],\n",
     "             \"dataStatus\": \"add\",\n",
     "             \"dataValue\": forecast_price()\n",
     "             }\n",
     "\n",
     "        ]\n",
     "    }\n",
-    "    # headers = {\"Authorization\": token_push}\n",
-    "    # res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5))\n",
-    "    # print(res.text)\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('预测值：',data['data'][0]['dataValue'])\n",
     "\n",
     "    \n",
-    "# def upload_data_to_system(token):\n",
-    "#     data = {\n",
-    "#         \"funcModule\": \"数据表信息列表\",\n",
-    "#         \"funcOperation\": \"新增\",\n",
-    "#         \"data\": [\n",
-    "#             {\"dataItemNo\": \"C01100036|Forecast_ Price|ACN\",\n",
-    "#              \"dataDate\": '20230706',\n",
-    "#              \"dataStatus\": \"add\",\n",
-    "#              \"dataValue\": 3780.0\n",
-    "#              }\n",
-    "\n",
-    "#         ]\n",
-    "#     }\n",
-    "#     headers = {\"Authorization\": token}\n",
-    "#     res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5))\n",
-    "#     print(res.text)\n",
-    "\n",
     "price_list = []\n",
     "    \n",
     "def forecast_price():\n",
@@ -698,7 +681,7 @@
     "    # 更新当日数据\n",
     "    start(date)\n",
     "    # 训练模型\n",
-    "    optimize_Model()\n",
+    "    # optimize_Model()\n",
     "    # 预测&上传预测结果\n",
     "    upload_data_to_system(token_push,start_date)\n",
     "\n"
@@ -717,15 +700,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "运行中ing...\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "\n",
     "if __name__ == \"__main__\":\n",
@@ -734,16 +709,17 @@
     "    while True:\n",
     "        # 获取当前时间\n",
     "        current_time = time.strftime(\"%H:%M:%S\", time.localtime())\n",
-    "        try:\n",
+    "        # try:\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",
-    "                main()\n",
-    "            elif current_time == \"20:00:00\":\n",
-    "                start_1()\n",
-    "            time.sleep(1)\n",
-    "        except:\n",
-    "            print(f\"{current_time}执行失败\")\n",
+    "        if current_time == \"09:15:00\":\n",
+    "        # if current_time == \"17:12:00\":\n",
+    "            print(\"执行定时任务\")\n",
+    "            main()\n",
+    "        elif current_time == \"20:00:00\":\n",
+    "            start_1()\n",
+    "        time.sleep(1)\n",
+    "        # except:\n",
+    "        #     print(f\"{current_time}执行失败\")\n",
     "\n",
     "    # 检测数据准确性, 需要检测放开\n",
     "    # check_data(\"100028098|LISTING_PRICE\")\n",
@@ -754,10 +730,93 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0NjY0NTcwMCwianRpIjoiODRhODFmYmZjYTUxNGM4ZGIzOGFiZTVlM2U0ZWFhYmUifQ.Vu5gbh0s1TMd3NE6T89l0h67TQaf3r_TDHvO_lZsIcs\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_2516\\268695125.py:602: DeprecationWarning:\n",
+      "\n",
+      "The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "当月数据更新完成\n",
+      "20250507\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_2516\\268695125.py:602: DeprecationWarning:\n",
+      "\n",
+      "The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "当月数据更新完成\n",
+      "获取到的数据项ID['YHQMXBB|C01100008|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|AMOUNT', 'C01100008|YEDAY_AMOUNT', '100028046|LISTING_PRICE', 'ICE_CL0_LAST_YEDAY_PRICE', '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']\n",
+      "获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0NjY0NTcwOCwianRpIjoiNDFjODI0ZjQ0MDIzNDZlZmI5NjY5NTcwZGE3MmZiYmYifQ.OUIHg0JlPstctzFKJ__8Qqo7oZuxedssnRJlUYXujtc\n",
+      "获取20250507数据\n",
+      "数据项查询参数search_data：\n",
+      "{'data': {'date': '20250507', 'dataItemNoList': ['YHQMXBB|C01100008|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|AMOUNT', 'C01100008|YEDAY_AMOUNT', '100028046|LISTING_PRICE', 'ICE_CL0_LAST_YEDAY_PRICE', '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",
+      "数据项查询结果search_res：\n",
+      "{\"confirmFlg\":false,\"data\":[{\"dataDate\":\"20250507\",\"dataItemNo\":\"91110000710926094P|C01100008|SUPPLY_MERE\",\"dataValue\":1300.00000000},{\"dataDate\":\"20250507\",\"dataItemNo\":\"91110000710932515R|C01100008|SUPPLY_MERE\"},{\"dataDate\":\"20250507\",\"dataItemNo\":\"91370321164425136B|C01100008|STRIKE_PRICE\",\"dataValue\":4750.00000000},{\"dataDate\":\"20250507\",\"dataItemNo\":\"91370321164425136B|C01100008|SUPPLY_MERE\",\"dataValue\":300.00000000},{\"dataDate\":\"20250507\",\"dataItemNo\":\"91370500674526498A|C01100008|SUPPLY_MERE\",\"dataValue\":175.00000000},{\"dataDate\":\"20250507\",\"dataItemNo\":\"91370521164880008P|C01100008|STRIKE_PRICE\",\"dataValue\":4805.00000000},{\"dataDate\":\"20250507\",\"dataItemNo\":\"C01100008|AUCTION_MAX_PRICE\",\"dataValue\":4770.00000000},{\"dataDate\":\"20250507\",\"dataItemNo\":\"C01100008|CORTED_VALUE\",\"dataValue\":4770.00000000},{\"dataDate\":\"20250507\",\"dataItemNo\":\"C01100008|OTHER|SUPPLY_MERE\",\"dataValue\":5000.00000000},{\"dataDate\":\"20250507\",\"dataItemNo\":\"C01100008|SUY_DED_DAP\",\"dataValue\":-50.00000000},{\"dataDate\":\"20250507\",\"dataItemNo\":\"ICE_CL0_LAST_YEDAY_PRICE\",\"dataValue\":62.04000000},{\"dataDate\":\"20250507\",\"dataItemNo\":\"SD|WJH|DEMANDS\",\"dataValue\":8500.00000000},{\"dataDate\":\"20250507\",\"dataItemNo\":\"SD|WJH|SALES_PRICE\",\"dataValue\":7830.00000000},{\"dataDate\":\"20250507\",\"dataItemNo\":\"YHQMXBB|C01100008|STRIKE_PRICE\",\"dataValue\":4770.00000000}],\"status\":true}\n",
+      "数据项查询结果: [{'dataDate': '20250507', 'dataItemNo': '91110000710926094P|C01100008|SUPPLY_MERE', 'dataValue': 1300.0}, {'dataDate': '20250507', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}, {'dataDate': '20250507', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 4750.0}, {'dataDate': '20250507', 'dataItemNo': '91370321164425136B|C01100008|SUPPLY_MERE', 'dataValue': 300.0}, {'dataDate': '20250507', 'dataItemNo': '91370500674526498A|C01100008|SUPPLY_MERE', 'dataValue': 175.0}, {'dataDate': '20250507', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 4805.0}, {'dataDate': '20250507', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE', 'dataValue': 4770.0}, {'dataDate': '20250507', 'dataItemNo': 'C01100008|CORTED_VALUE', 'dataValue': 4770.0}, {'dataDate': '20250507', 'dataItemNo': 'C01100008|OTHER|SUPPLY_MERE', 'dataValue': 5000.0}, {'dataDate': '20250507', 'dataItemNo': 'C01100008|SUY_DED_DAP', 'dataValue': -50.0}, {'dataDate': '20250507', 'dataItemNo': 'ICE_CL0_LAST_YEDAY_PRICE', 'dataValue': 62.04}, {'dataDate': '20250507', 'dataItemNo': 'SD|WJH|DEMANDS', 'dataValue': 8500.0}, {'dataDate': '20250507', 'dataItemNo': 'SD|WJH|SALES_PRICE', 'dataValue': 7830.0}, {'dataDate': '20250507', 'dataItemNo': 'YHQMXBB|C01100008|STRIKE_PRICE', 'dataValue': 4770.0}]\n",
+      "{'dataDate': '20250507', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
+      "添加的行： ['2025-05-07', 4770.0, 4770.0, 4770.0, '', '', '', 62.04, '', '', '', '', 4805.0, 4750.0, '', '', '', 1300.0, '', 175.0, 300.0, 5000.0, 8500.0, -50.0, '', '', '', '', '', 7830.0]\n",
+      "更新 2025-05-07 数据\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_2516\\268695125.py:186: 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_2516\\268695125.py:188: 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_2516\\268695125.py:220: 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",
+      "2025-05-07    4769.817871\n",
+      "Name: 日度预测价格, dtype: float32\n",
+      "{\"confirmFlg\":false,\"status\":true}\n",
+      "预测值： 4769.82\n"
+     ]
+    }
+   ],
    "source": [
-    "# start_date = datetime(2025, 4, 1)\n",
-    "# end_date = datetime(2025, 5, 7)\n",
+    "# start_date = datetime(2025, 5, 7)\n",
+    "# end_date = datetime(2025, 5, 8)\n",
     "# token = get_head_auth()\n",
     "\n",
     "# while start_date < end_date:\n",
@@ -783,7 +842,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -797,7 +856,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,
diff --git a/aisenzhecode/液化石油气/液化气数据.xlsx b/aisenzhecode/液化石油气/液化气数据.xlsx
index 16eae9e..5fa9051 100644
Binary files a/aisenzhecode/液化石油气/液化气数据.xlsx and b/aisenzhecode/液化石油气/液化气数据.xlsx differ
diff --git a/aisenzhecode/石油苯/日度价格预测_最佳模型.pkl b/aisenzhecode/石油苯/日度价格预测_最佳模型.pkl
index a97204f..85cb275 100644
Binary files a/aisenzhecode/石油苯/日度价格预测_最佳模型.pkl and b/aisenzhecode/石油苯/日度价格预测_最佳模型.pkl differ
diff --git a/aisenzhecode/石油苯/纯苯价格预测-自定义日期ytj.ipynb b/aisenzhecode/石油苯/纯苯价格预测-自定义日期ytj.ipynb
index 842a333..d893f36 100644
--- a/aisenzhecode/石油苯/纯苯价格预测-自定义日期ytj.ipynb
+++ b/aisenzhecode/石油苯/纯苯价格预测-自定义日期ytj.ipynb
@@ -5,27 +5,17 @@
    "execution_count": null,
    "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) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
+       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.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-2.2.0.min']\n",
+       "                'plotly': ['https://cdn.plot.ly/plotly-2.12.1.min']\n",
        "            }\n",
        "        });\n",
        "        require(['plotly'], function(Plotly) {\n",
@@ -43,7 +33,106 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "运行中ing\n"
+      "运行中ing\n",
+      "执行定时任务\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_22664\\2912906777.py:771: DeprecationWarning:\n",
+      "\n",
+      "The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "获取到的数据项ID['数据项编码', 'C01100047|STRIKE_PRICE', 'Brentspj', '913716251671540959|EXCHANGE_RATE', 'C01100010|LIST_PRICE01', '250326561|STRIKE_PRICE', 'C01100047|LIST_PRICE', 'C01100047|LIST_PRICE-1', 'C01100047|LIST_PRICE-01', 'OIL_CHEM|guonei|6097|PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370783724809024G|BEN|PRICE', '91370500737223620X|BEN|PRICE', '91370503706169019D|BEN|PRICE', '91370503164840647R|BEN|PRICE', 'C01100047|TURNOVER', '913705221649223519|C01100047|EXW', 'C01100047|CAPACITY']\n",
+      "获取到的数据项ID['C01100047|STRIKE_PRICE', 'Brentspj', '913716251671540959|EXCHANGE_RATE', 'C01100010|LIST_PRICE01', '250326561|STRIKE_PRICE', 'C01100047|LIST_PRICE', 'C01100047|LIST_PRICE-1', 'C01100047|LIST_PRICE-01', 'OIL_CHEM|guonei|6097|PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370783724809024G|BEN|PRICE', '91370500737223620X|BEN|PRICE', '91370503706169019D|BEN|PRICE', '91370503164840647R|BEN|PRICE', 'C01100047|TURNOVER', '913705221649223519|C01100047|EXW', 'C01100047|CAPACITY']\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_22664\\2912906777.py:771: DeprecationWarning:\n",
+      "\n",
+      "The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
+      "\n",
+      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_22664\\2912906777.py:320: 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",
+      "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:631: ConvergenceWarning:\n",
+      "\n",
+      "Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.681e+05, tolerance: 4.960e+04\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": [
+      "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\IPython\\core\\magics\\pylab.py:162: UserWarning:\n",
+      "\n",
+      "pylab import has clobbered these variables: ['__version__', 'datetime', 'plot', 'random']\n",
+      "`%matplotlib` prevents importing * from pylab and numpy\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Date\n",
+      "2025-05-07    5427.172363\n",
+      "Name: 日度预测价格, dtype: float32\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_22664\\2912906777.py:273: 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_22664\\2912906777.py:303: 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": [
+      "{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20250507', 'dataStatus': 'add', 'dataValue': 5427.17}]}\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[3], line 834\u001b[0m\n\u001b[0;32m    832\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m更新数据\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    833\u001b[0m     start_1()\n\u001b[1;32m--> 834\u001b[0m time\u001b[38;5;241m.\u001b[39msleep(\u001b[38;5;241m1\u001b[39m)\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
    ],
@@ -246,6 +335,9 @@
     "def get_cur_time(date=''):\n",
     "    if date == '':\n",
     "        now = datetime.now()\n",
+    "    # 如果是字符串，尝试解析日期\n",
+    "    elif isinstance(date, str):\n",
+    "        now = datetime.strptime(date, '%Y-%m-%d')\n",
     "    else:\n",
     "        now = date\n",
     "    year = now.year\n",
@@ -280,7 +372,7 @@
     "        \"funcOperation\": \"新增\",\n",
     "        \"data\": [\n",
     "            {\"dataItemNo\": \"C01100047|FORECAST_PRICE\",\n",
-    "             \"dataDate\": get_cur_time(date)[0],\n",
+    "             \"dataDate\": date,\n",
     "             \"dataStatus\": \"add\",\n",
     "             \"dataValue\": datavalue\n",
     "             }\n",
@@ -292,25 +384,7 @@
     "    res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5))\n",
     "    print(res.text)\n",
     "\n",
-    "    \n",
-    "# def upload_data_to_system(token):\n",
-    "#     data = {\n",
-    "#         \"funcModule\": \"数据表信息列表\",\n",
-    "#         \"funcOperation\": \"新增\",\n",
-    "#         \"data\": [\n",
-    "#             {\"dataItemNo\": \"C01100036|Forecast_ Price|ACN\",\n",
-    "#              \"dataDate\": '20230706',\n",
-    "#              \"dataStatus\": \"add\",\n",
-    "#              \"dataValue\": 3780.0\n",
-    "#              }\n",
-    "\n",
-    "#         ]\n",
-    "#     }\n",
-    "#     headers = {\"Authorization\": token}\n",
-    "#     res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5))\n",
-    "#     print(res.text)\n",
-    "\n",
-    "    \n",
+    "   \n",
     "    \n",
     "def forecast_price():\n",
     "    # df_test = pd.read_csv('定价模型数据收集0212.csv')\n",
@@ -916,7 +990,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -930,7 +1004,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,
diff --git a/aisenzhecode/石油苯/纯苯数据项.xls b/aisenzhecode/石油苯/纯苯数据项.xls
index 0902450..bd53b41 100644
Binary files a/aisenzhecode/石油苯/纯苯数据项.xls and b/aisenzhecode/石油苯/纯苯数据项.xls differ
diff --git a/models/xgboostmodels.py b/models/xgboostmodels.py
new file mode 100644
index 0000000..f90079a
--- /dev/null
+++ b/models/xgboostmodels.py
@@ -0,0 +1,236 @@
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn.feature_selection import SelectFromModel
+from sklearn.preprocessing import OrdinalEncoder
+from sklearn.impute import SimpleImputer
+from sklearn.model_selection import train_test_split
+import pandas as pd
+from sklearn.linear_model import Lasso
+from xgboost import XGBRegressor
+import statsmodels.api as sm
+import plotly.express as px
+import plotly.graph_objects as go
+import xgboost as xgb
+from xgboost import plot_importance, plot_tree
+from sklearn.metrics import mean_absolute_error
+from statsmodels.tools.eval_measures import mse, rmse
+from sklearn.model_selection import GridSearchCV
+import warnings
+import pickle
+from sklearn.metrics import mean_squared_error
+from sklearn.metrics import r2_score
+import numpy as np
+from matplotlib import pyplot as plt
+import os
+from matplotlib import rcParams
+rcParams['font.sans-serif'] = ['SimHei']  # Windows系统自带的中文字体
+rcParams['axes.unicode_minus'] = False  # 解决负号显示问题
+
+pd.set_option('display.max_rows', 40)
+pd.set_option('display.max_columns', 40)
+
+
+class xgboostmodels():
+    def __init__(self, df, steps=3):
+        self.df = df
+        self.steps = steps
+        self.features = []
+
+    def optimize_Model(self, df):
+        # 生成滞后特征（示例使用3期滞后）
+        steps = self.steps
+        for i in range(1, steps+1):
+            df[f'lag_{i}'] = df['y'].shift(i)
+
+        # 生成多步预测目标（示例预测未来3步）
+        forecast_horizon = steps
+        for i in range(forecast_horizon):
+            df[f'target_{i+1}'] = df['y'].shift(-(i+1))
+
+        df = df.dropna()  # 删除包含NaN的行
+
+        # 更新特征和目标变量
+        features = [col for col in df.columns if col.startswith('lag')]
+        targets = [col for col in df.columns if col.startswith('target')]
+
+        X = df[features]
+        y = df[targets]
+
+        # 按时间顺序分割数据集（时间序列不能随机分割）
+        split = int(0.8 * len(df))
+        X_train, x_test = X.iloc[:split], X.iloc[split:]
+        y_train, y_test = y.iloc[:split], y.iloc[split:]
+
+        # 修改模型为多输出回归
+        from sklearn.multioutput import MultiOutputRegressor
+        base_model = XGBRegressor(random_state=0)
+        estimator = MultiOutputRegressor(base_model)  # 保持多输出包装器
+
+        # 模型参数网格需要调整为适用于多输出回归器
+        parameters = {
+            'estimator__max_depth': range(2, 11, 2),     # 注意参数前缀
+            'estimator__n_estimators': range(50, 101, 10),
+            'estimator__learning_rate': [0.01, 0.03, 0.1, 0.3, 0.5, 1]
+        }
+
+        grid_search_XGB = GridSearchCV(
+            estimator=estimator,
+            param_grid=parameters,
+            cv=3,
+            verbose=True,
+            scoring='neg_mean_squared_error'  # 使用更适合时序的评估指标
+        )
+
+        grid_search_XGB.fit(X_train, y_train)
+
+        # 优化后的结果展示
+        print("最佳参数组合:")
+        best_params = {}
+        for param, value in grid_search_XGB.best_params_.items():
+            best_params[param.split('__')[1]] = value
+            print(f"{param}: {value}")
+
+        best_params_df = pd.DataFrame(
+            [best_params], index=["最佳参数组合"])
+        y_pred = grid_search_XGB.predict(x_test)
+
+        # 多步预测评估指标
+        evaluation_results = []
+        for step in range(forecast_horizon):
+            mse = mean_squared_error(y_test.iloc[:, step], y_pred[:, step])
+            rmse = np.sqrt(mse)
+            r2 = r2_score(y_test.iloc[:, step], y_pred[:, step])
+
+            evaluation_results.append({
+                '预测步长': step+1,
+                '均方根误差(RMSE)': rmse,
+                'R²分数': r2
+            })
+
+        # 生成多步预测结果对比表
+        results_df = pd.DataFrame(evaluation_results).set_index('预测步长')
+        print("\n多步预测性能评估:")
+        print(results_df)
+
+        # 保存完整模型（包含多输出包装器）
+        Pkl_Filename = "report\日度价格预测_最佳模型.pkl"
+        with open(Pkl_Filename, 'wb') as file:
+            pickle.dump(grid_search_XGB.best_estimator_, file)
+
+        return results_df, best_params_df
+
+    def forecast_price(self, df):
+        steps = self.steps
+        import joblib
+        Best_model_DalyLGPrice = joblib.load(
+            "report\日度价格预测_最佳模型.pkl")
+        df = df.copy()
+        df.set_index('ds', inplace=True)
+
+        # 生成滞后特征（与训练时保持一致）
+        for i in range(1, steps+1):
+            df[f'lag_{i}'] = df['y'].shift(i)
+
+        # 获取特征列（保持与训练时相同的特征顺序）
+        features = [f'lag_{i}' for i in range(1, steps+1)]
+
+        # 初始化预测序列（保留最后3个已知值作为初始输入）
+        current_values = df[features][-1:].values  # 初始形状 (1,3)
+
+        predictions = Best_model_DalyLGPrice.predict(current_values)[0]
+
+        # 生成带日期的预测结果
+        last_date = df.index[-1]
+        date_range = pd.date_range(
+            start=last_date + pd.Timedelta(days=1),
+            periods=steps
+        )
+
+        # 保存预测结果到DataFrame
+        forecast_series = pd.Series(
+            predictions, index=date_range.strftime('%Y-%m-%d'), name='预测值')
+
+        # 拼接原始数据和预测结果
+        df = pd.concat([df[['y']], forecast_series], axis=0)
+        # 绘制预测结果
+        plt.figure(figsize=(12, 6))
+        n_points = min(len(df), 30)  # 自动适配数据量
+        plt.plot(df.index[-n_points:], df['y'].iloc[-n_points:],
+                 label=f'原始数据(近{n_points}天)', marker='o', markersize=4)
+
+        plt.plot(forecast_series.index, forecast_series.values,
+                 label=f'预测值 (未来{steps}天)', linestyle='--', marker='x')
+
+        plt.axvline(x=last_date, color='red', linestyle=':',
+                    label='当前日期分界线', alpha=0.7)
+        plt.title('价格预测趋势')
+        plt.xlabel('日期')
+        plt.ylabel('价格')
+        plt.legend()
+        plt.grid(True)
+
+        # 自动旋转日期标签
+        plt.gcf().autofmt_xdate()
+
+        # 确保保存目录存在
+        os.makedirs("report", exist_ok=True)
+        plt.savefig("report/价格预测.png", dpi=300, bbox_inches='tight')
+        plt.close()
+
+        return forecast_series
+
+    def generate_xgboost_report(self, model_results, best_params_df, forecast_series,
+                                save_path="report/xgboost_report.md"):
+        """
+        生成XGBoost模型报告
+        参数:
+            model_results: 模型对比结果DataFrame
+            evaluation_df: 多步评估结果DataFrame  
+            forecast_series: 预测结果Series
+            save_path: 报告保存路径
+        """
+        # 构建Markdown内容
+        md_content = f"""
+# XGBoost价格预测分析报告  <span style="color:gray; font-size:0.8em">{pd.Timestamp.now().strftime('%Y-%m-%d')}</span>
+
+## 一、模型实现
+
+### 1. 特征工程
+- 使用{len(model_results)}期历史滞后特征
+- 预测未来{len(model_results)}个时间步长
+- 数据集分割比例：80% 训练集 / 20% 测试集
+
+### 2. 模型配置
+
+{best_params_df.to_markdown()}
+
+## 二、性能评估
+### 多步预测误差分析
+{model_results.to_markdown()}
+
+## 三、预测结果
+### 未来{len(forecast_series)}日价格预测
+![预测结果](价格预测.png)
+{forecast_series.to_markdown()}
+
+        """
+
+        # 保存报告
+        os.makedirs(os.path.dirname(save_path), exist_ok=True)
+        with open(save_path, 'w', encoding='utf-8') as f:
+            f.write(md_content)
+
+        print(f"报告已生成至：{os.path.abspath(save_path)}")
+
+
+if __name__ == '__main__':
+    df = pd.read_csv(
+        "D:\code\PriceForecast-svn\yuanyoudataset\指标数据.csv")
+    df['ds'] = pd.to_datetime(df['ds'])
+    xg = xgboostmodels(df=df, steps=5)
+    model_results, best_params_df = xg.optimize_Model(df=df)
+    forecast_series = xg.forecast_price(df)
+    xg.generate_xgboost_report(
+        model_results=model_results,
+        best_params_df=best_params_df,
+        forecast_series=forecast_series
+    )
diff --git a/report/xgboost_report.md b/report/xgboost_report.md
new file mode 100644
index 0000000..b49ee8c
--- /dev/null
+++ b/report/xgboost_report.md
@@ -0,0 +1,40 @@
+# XGBoost 价格预测分析报告 <span style="color:gray; font-size:0.8em">2025-05-08</span>
+
+## 一、模型实现
+
+### 1. 特征工程
+
+- 使用 5 期历史滞后特征
+- 预测未来 10 个时间步长
+- 数据集分割比例：80% 训练集 / 20% 测试集
+
+### 2. 模型配置
+
+|              | learning_rate | max_depth | n_estimators |
+| :----------- | ------------: | --------: | -----------: |
+| 最佳参数组合 |             1 |         4 |          100 |
+
+## 二、性能评估
+
+### 多步预测误差分析
+
+| 预测步长 | 均方根误差(RMSE) |  R² 分数 |
+| -------: | ---------------: | -------: |
+|        1 |          2.30044 | 0.828193 |
+|        2 |          2.74815 | 0.756595 |
+|        3 |          3.16346 | 0.679378 |
+|        4 |          3.62202 |  0.58126 |
+|        5 |          3.77657 | 0.546517 |
+
+## 三、预测结果
+
+### 未来 5 日价格预测
+
+![预测结果](价格预测.png)
+| | 预测值 |
+|:--------------------|---------:|
+| 2025-03-14 00:00:00 | 68.8125 |
+| 2025-03-15 00:00:00 | 68.6643 |
+| 2025-03-16 00:00:00 | 69.283 |
+| 2025-03-17 00:00:00 | 71.7288 |
+| 2025-03-18 00:00:00 | 68.8356 |
diff --git a/report/日度价格预测_最佳模型.pkl b/report/日度价格预测_最佳模型.pkl
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index 0000000..8102373
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