From 98e0178da03642b9f3a0c4e1a177d1c1b63e0716 Mon Sep 17 00:00:00 2001
From: workpc <liurui_test@163.com>
Date: Mon, 30 Dec 2024 14:00:16 +0800
Subject: [PATCH] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E9=A2=84=E6=B5=8B=E7=BB=93?=
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MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
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---
 .../日度价格预测_液化气最佳模型.pkl           |  Bin 261197 -> 297700 bytes
 .../液化石油气/液化气价格预测ytj.ipynb        | 1455 ++---------------
 aisenzhecode/液化石油气/液化气数据.xls        |  Bin 162304 -> 166400 bytes
 config_jingbo.py                              |  232 ++-
 lib/dataread.py                               |    4 +-
 main_yongan.py                                |  288 ++++
 main_yuanyou.py                               |  392 ++---
 models/nerulforcastmodels.py                  |   25 +-
 8 files changed, 706 insertions(+), 1690 deletions(-)
 create mode 100644 main_yongan.py

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 509bab49e3845e0d9d087b21eda936798abd5874..f2c685e2e9ab67dd84a196dc112be86391e256a8 100644
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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 4f475c8..68e16bb 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": 12,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -722,34 +722,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "20241101\n",
-      "{'dataDate': '20241101', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
-      "{'dataDate': '20241101', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE'}\n"
+      "20241213\n",
+      "{'dataDate': '20241213', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\2946673419.py:291: UserWarning:\n",
+      "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_18460\\2946673419.py:293: UserWarning:\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",
+      "\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",
+      "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__', 'plot', 'datetime', 'random']\n",
+      "pylab import has clobbered these variables: ['plot', 'datetime', 'random', '__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"
      ]
     },
@@ -757,25 +780,16 @@
      "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"
+      "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_18460\\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_18460\\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_18460\\2946673419.py:274: FutureWarning:\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"
@@ -785,24 +799,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Date\n",
-      "2024-11-01    4799.513672\n",
-      "Name: 日度预测价格, dtype: float32\n",
       "{\"confirmFlg\":false,\"status\":true}\n",
-      "20241102\n",
-      "{'dataDate': '20241102', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
-      "{'dataDate': '20241102', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE'}\n"
+      "20241214\n",
+      "{'dataDate': '20241214', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\2946673419.py:291: UserWarning:\n",
+      "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_18460\\2946673419.py:293: UserWarning:\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"
@@ -822,15 +832,30 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\2946673419.py:240: UserWarning:\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_18460\\2946673419.py:242: UserWarning:\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_18460\\2946673419.py:274: FutureWarning:\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"
@@ -840,24 +865,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Date\n",
-      "2024-11-02    4803.988281\n",
-      "Name: 日度预测价格, dtype: float32\n",
       "{\"confirmFlg\":false,\"status\":true}\n",
-      "20241103\n",
-      "{'dataDate': '20241103', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
-      "{'dataDate': '20241103', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE'}\n"
+      "20241215\n",
+      "{'dataDate': '20241215', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\2946673419.py:291: UserWarning:\n",
+      "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_18460\\2946673419.py:293: UserWarning:\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"
@@ -877,15 +898,30 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\2946673419.py:240: UserWarning:\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_18460\\2946673419.py:242: UserWarning:\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_18460\\2946673419.py:274: FutureWarning:\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"
@@ -895,24 +931,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Date\n",
-      "2024-11-03    4752.453125\n",
-      "Name: 日度预测价格, dtype: float32\n",
       "{\"confirmFlg\":false,\"status\":true}\n",
-      "20241104\n",
-      "{'dataDate': '20241104', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
-      "{'dataDate': '20241104', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE'}\n"
+      "20241216\n",
+      "{'dataDate': '20241216', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\2946673419.py:291: UserWarning:\n",
+      "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_18460\\2946673419.py:293: UserWarning:\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"
@@ -932,15 +964,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\2946673419.py:240: UserWarning:\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_18460\\2946673419.py:242: UserWarning:\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_18460\\2946673419.py:274: FutureWarning:\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"
@@ -951,1310 +983,7 @@
      "output_type": "stream",
      "text": [
       "Date\n",
-      "2024-11-04    4749.770508\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "{\"confirmFlg\":false,\"status\":true}\n",
-      "20241105\n",
-      "{'dataDate': '20241105', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
-      "{'dataDate': '20241105', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\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_18460\\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_18460\\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_18460\\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_18460\\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",
-      "2024-11-05    4729.610352\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "{\"confirmFlg\":false,\"status\":true}\n",
-      "20241106\n",
-      "{'dataDate': '20241106', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
-      "{'dataDate': '20241106', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\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_18460\\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_18460\\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_18460\\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_18460\\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",
-      "2024-11-06    4755.190918\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "{\"confirmFlg\":false,\"status\":true}\n",
-      "20241107\n",
-      "{'dataDate': '20241107', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
-      "{'dataDate': '20241107', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\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_18460\\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_18460\\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_18460\\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_18460\\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",
-      "2024-11-07    4751.669434\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "{\"confirmFlg\":false,\"status\":true}\n",
-      "20241108\n",
-      "{'dataDate': '20241108', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
-      "{'dataDate': '20241108', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\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_18460\\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_18460\\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_18460\\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_18460\\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",
-      "2024-11-08    4779.73584\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "{\"confirmFlg\":false,\"status\":true}\n",
-      "20241109\n",
-      "{'dataDate': '20241109', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
-      "{'dataDate': '20241109', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\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_18460\\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_18460\\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_18460\\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_18460\\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",
-      "2024-11-09    4770.091797\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "{\"confirmFlg\":false,\"status\":true}\n",
-      "20241110\n",
-      "{'dataDate': '20241110', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
-      "{'dataDate': '20241110', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\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_18460\\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_18460\\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_18460\\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_18460\\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",
-      "2024-11-10    4770.462402\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "{\"confirmFlg\":false,\"status\":true}\n",
-      "20241111\n",
-      "{'dataDate': '20241111', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
-      "{'dataDate': '20241111', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\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_18460\\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_18460\\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_18460\\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_18460\\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",
-      "2024-11-11    4787.013672\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "{\"confirmFlg\":false,\"status\":true}\n",
-      "20241112\n",
-      "{'dataDate': '20241112', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
-      "{'dataDate': '20241112', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\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_18460\\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_18460\\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_18460\\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_18460\\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",
-      "2024-11-12    4829.234863\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "{\"confirmFlg\":false,\"status\":true}\n",
-      "20241113\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\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_18460\\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_18460\\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_18460\\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_18460\\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",
-      "2024-11-13    4825.259766\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "{\"confirmFlg\":false,\"status\":true}\n",
-      "20241114\n",
-      "{'dataDate': '20241114', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\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_18460\\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_18460\\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_18460\\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_18460\\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",
-      "2024-11-14    4886.885742\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "{\"confirmFlg\":false,\"status\":true}\n",
-      "20241115\n",
-      "{'dataDate': '20241115', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\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_18460\\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_18460\\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_18460\\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_18460\\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",
-      "2024-11-15    4999.224609\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "{\"confirmFlg\":false,\"status\":true}\n",
-      "20241116\n",
-      "{'dataDate': '20241116', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\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_18460\\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_18460\\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_18460\\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_18460\\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",
-      "2024-11-16    4999.256348\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "{\"confirmFlg\":false,\"status\":true}\n",
-      "20241117\n",
-      "{'dataDate': '20241117', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\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_18460\\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_18460\\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_18460\\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_18460\\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",
-      "2024-11-17    4906.539551\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "{\"confirmFlg\":false,\"status\":true}\n",
-      "20241118\n",
-      "{'dataDate': '20241118', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\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_18460\\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_18460\\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_18460\\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_18460\\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",
-      "2024-11-18    4864.897949\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "{\"confirmFlg\":false,\"status\":true}\n",
-      "20241119\n",
-      "{'dataDate': '20241119', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\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_18460\\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_18460\\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_18460\\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_18460\\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",
-      "2024-11-19    4848.118164\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "{\"confirmFlg\":false,\"status\":true}\n",
-      "20241120\n",
-      "{'dataDate': '20241120', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\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_18460\\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_18460\\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_18460\\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_18460\\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",
-      "2024-11-20    4886.458496\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "{\"confirmFlg\":false,\"status\":true}\n",
-      "20241121\n",
-      "{'dataDate': '20241121', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\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_18460\\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_18460\\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_18460\\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_18460\\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",
-      "2024-11-21    4939.601562\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "{\"confirmFlg\":false,\"status\":true}\n",
-      "20241122\n",
-      "{'dataDate': '20241122', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\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_18460\\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_18460\\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_18460\\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_18460\\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",
-      "2024-11-22    4911.041992\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "{\"confirmFlg\":false,\"status\":true}\n",
-      "20241123\n",
-      "{'dataDate': '20241123', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\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_18460\\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_18460\\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_18460\\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_18460\\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",
-      "2024-11-23    4948.775391\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "{\"confirmFlg\":false,\"status\":true}\n",
-      "20241124\n",
-      "{'dataDate': '20241124', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\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_18460\\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_18460\\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_18460\\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_18460\\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",
-      "2024-11-24    4933.570312\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "{\"confirmFlg\":false,\"status\":true}\n",
-      "20241125\n",
-      "{'dataDate': '20241125', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\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_18460\\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_18460\\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_18460\\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_18460\\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",
-      "2024-11-25    4892.017578\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "{\"confirmFlg\":false,\"status\":true}\n",
-      "20241126\n",
-      "{'dataDate': '20241126', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\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_18460\\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_18460\\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_18460\\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_18460\\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",
-      "2024-11-26    4840.280273\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "{\"confirmFlg\":false,\"status\":true}\n",
-      "20241127\n",
-      "{'dataDate': '20241127', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\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_18460\\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_18460\\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_18460\\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_18460\\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",
-      "2024-11-27    4840.000488\n",
-      "Name: 日度预测价格, dtype: float32\n",
-      "{\"confirmFlg\":false,\"status\":true}\n",
-      "20241128\n",
-      "{'dataDate': '20241128', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_18460\\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_18460\\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_18460\\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_18460\\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_18460\\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",
-      "2024-11-28    4848.003906\n",
+      "2024-12-16    5240.49707\n",
       "Name: 日度预测价格, dtype: float32\n",
       "{\"confirmFlg\":false,\"status\":true}\n"
      ]
@@ -2263,8 +992,8 @@
    "source": [
     "from datetime import datetime, timedelta\n",
     "\n",
-    "start_date = datetime(2024, 11, 1)\n",
-    "end_date = datetime(2024, 11, 29)\n",
+    "start_date = datetime(2024, 12, 13)\n",
+    "end_date = datetime(2024, 12, 17)\n",
     "\n",
     "while start_date < end_date:\n",
     "    print(start_date.strftime('%Y%m%d'))\n",
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
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zb-SsKLS2n5Ju{c14s{gjaa%4Av5F^hIVxj23Uv+2t_a>g5W-EUV2;|A8QW3mjoQ3X
zct3o<wyN;Km*9;~;ysU#DC<t=vIcLl2Jbf0tHE2-IMDG;QJ3ZbY5;z9fM=X|9^xfV
z05u8J0KB-Fk2(%!4$j9>viL1w6%~a#)2B(FMhn@fGpVI5v_w1vYEX9lTi#K-N5rxQ
z<$OH&i5*R3uuu;5q?Ni*>QHVsmAYN2JCr(<W6xO1YPR@7sY|8qsuX+AHUv%P4y#M0
zK0&EN*^_myPahX`N{3QB<cfPIs#T`a5K05eCrqVbQ#Kn8r2*yUXK$o5Ol1=>rl1^#
za(Hv<<2q_o7!dTG{o8-3NMxTeML`ULxiJWKn1UDty#w`QbGu>=K+FXYlgktX;4H%M
zBJ!*f=QRjMu|o?$&HemVSQ|-9E>mp6GR1%_E#9_%PpS_o7js2g+eut3Qyj>hCNd7>
za6M8k?oh^ElyOs8O5#xV!Y7^&_TjvjB|P>IHp2N0B<?7racPa)3QHWytJlBX|65c%
zB*a~n*7lJQ$^?|VOl88ZOgNMY7iB^!6E{^RGL#92G9i@-n=%3AP-N$OJ*)e%mWe4U
zlR}w<vcps+?aHJ>nRHPmWyB_-6#GNLgotfG;S>>D+vbeer1T{-BX&g+%FFeim7c2+
z-zJl;N)fS@>@S|8sZUyI(di>CbTsc&C%u$avW)$UWjQ;_*-^oc73{c^9hK}@dHTXW
XdW&apKmCVx)6^HObX{uI({$v2jR>&5

delta 155
zcmZpe!qxDFbAkb*^+ZEy9t%bW21_8e0^*H{^BE@=aBgCn$SlI4$^Zmx48a+xsU?Q3
z4Dt*U;wLX<Rb-84Vql2he415Ofk}#S^D9+Z#>pSm6B$*TgEiWNH5j)CYcTOzvDq;R
zF>GU+o>0ytvfZJK=>YfkhH|D2EZdrx?yzkC)5gRCv`ArE2h&fc0;cJO<xC#aojaNK
F0ssd2E9w9M

diff --git a/config_jingbo.py b/config_jingbo.py
index b18daba..577b21b 100644
--- a/config_jingbo.py
+++ b/config_jingbo.py
@@ -197,52 +197,124 @@ ClassifyId = 1214
 
 
 ################################################################################################################  变量定义--测试环境
-# login_pushreport_url = "http://192.168.100.53:8080/jingbo-dev/api/server/login"
-# upload_url = "http://192.168.100.53:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
+login_pushreport_url = "http://192.168.100.53:8080/jingbo-dev/api/server/login"
+upload_url = "http://192.168.100.53:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
+# upload_url = "http://192.168.100.109:8080/jingbo/api/analysis/reportInfo/researchUploadReportSave" # zhaoqiwei
+upload_warning_url = "http://192.168.100.53:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
+query_data_list_item_nos_url = "http://192.168.100.53:8080/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
+
+login_data = {
+    "data": {
+        "account": "api_test",
+        # "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
+        "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
+        "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
+        "terminal": "API"
+    },
+    "funcModule": "API",
+    "funcOperation": "获取token"
+}
+
+upload_data = {
+    "funcModule":'研究报告信息',
+    "funcOperation":'上传原油价格预测报告',
+    "data":{
+        "ownerAccount":'arui', #报告所属用户账号
+        "reportType":'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
+        "fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', #文件名称 
+        "fileBase64": '' ,#文件内容base64
+        "categoryNo":'yyjgycbg', # 研究报告分类编码
+        "smartBusinessClassCode":'YCJGYCBG', #分析报告分类编码
+        "reportEmployeeCode":"E40116", # 报告人
+        "reportDeptCode" :"D0044" ,# 报告部门
+        "productGroupCode":"RAW_MATERIAL"  # 商品分类
+  }
+}
+
+
+warning_data = {
+    "funcModule":'原油特征停更预警',
+    "funcOperation":'原油特征停更预警',
+    "data":{
+    'WARNING_TYPE_NAME':'特征数据停更预警',
+    'WARNING_CONTENT':'',
+    'WARNING_DATE':''
+  }
+}
+
+query_data_list_item_nos_data = {
+   "funcModule": "数据项",
+   "funcOperation": "查询",
+    "data": {
+        "dateStart":"20200101",
+        "dateEnd":"20241231",
+        "dataItemNoList":["Brentzdj","Brentzgj"] # 数据项编码,代表 brent最低价和最高价
+    }
+}
+
+
+# 北京环境数据库
+host = '192.168.101.27'
+port = 3306
+dbusername ='root'  
+password = '123456'
+dbname = 'jingbo_test'
+table_name = 'v_tbl_crude_oil_warning'
+
+
+### 开关
+is_train = False # 是否训练
+is_debug = False # 是否调试
+is_eta = True # 是否使用eta接口
+is_market = True # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
+is_timefurture = True # 是否使用时间特征
+is_fivemodels = False # 是否使用之前保存的最佳的5个模型
+is_edbcode = False # 特征使用edbcoding列表中的
+is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
+is_update_eta  = False  # 预测结果上传到eta
+is_update_report = True # 是否上传报告
+is_update_warning_data =  True # 是否上传预警数据
+is_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征
+is_del_tow_month = True # 是否删除两个月不更新的特征
+
+
+################################################################################################################  变量定义--雍安测试环境
+# login_pushreport_url = "http://192.168.100.115:9090/dom-api/api/server/login"
+# upload_url = "http://192.168.100.115:9090/dom-api/api/analysis/reportInfo/researchUploadReportSave"
 # # upload_url = "http://192.168.100.109:8080/jingbo/api/analysis/reportInfo/researchUploadReportSave" # zhaoqiwei
-# upload_warning_url = "http://192.168.100.53:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
-# query_data_list_item_nos_url = "http://192.168.100.53:8080/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
+# upload_warning_url = "http://192.168.100.115:9090/dom-api/api/basicBuiness/crudeOilWarning/save"
+# query_data_list_item_nos_url = "http://192.168.100.115:9090/dom-api/api/warehouse/dwDataItem/queryDataListItemNos"
+
+
+
+
+
 
 # login_data = {
 #     "data": {
-#         "account": "api_test",
-#         # "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
-#         "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
-#         "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
+#         "account": "api-dev",
+#         "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", 
+#         "tenantHashCode": "1eb24ab5a6af12e30daf78af276664f1",
 #         "terminal": "API"
 #     },
 #     "funcModule": "API",
 #     "funcOperation": "获取token"
 # }
 
-# # upload_data = {
-# #     "funcModule":'研究报告信息',
-# #     "funcOperation":'上传原油价格预测报告',
-# #     "data":{
-# #         "ownerAccount":'arui', #报告所属用户账号
-# #         "reportType":'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
-# #         "fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', #文件名称 
-# #         "fileBase64": '' ,#文件内容base64
-# #         "categoryNo":'yyjgycbg', # 研究报告分类编码
-# #         "smartBusinessClassCode":'YCJGYCBG', #分析报告分类编码
-# #         "reportEmployeeCode":"E40116", # 报告人
-# #         "reportDeptCode" :"D0044" ,# 报告部门
-# #         "productGroupCode":"RAW_MATERIAL"  # 商品分类
-# #   }
-# # }
 
 # upload_data = {
 #     "funcModule":'研究报告信息',
 #     "funcOperation":'上传原油价格预测报告',
 #     "data":{
-#         "ownerAccount":'arui', #报告所属用户账号
+#         "ownerAccount":'rui.liu', #报告所属用户账号
 #         "reportType":'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
 #         "fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', #文件名称 
 #         "fileBase64": '' ,#文件内容base64
 #         "categoryNo":'yyjgycbg', # 研究报告分类编码
 #         "smartBusinessClassCode":'1', #分析报告分类编码
-#         "reportEmployeeCode":"E40116", # 报告人
-#         "reportDeptCode" :"D0044" ,# 报告部门
+#         "reportEmployeeCode":"U270018", # 报告人
+#         "reportDeptCode" :"D270001" ,# 报告部门
+#         # "reportDeptCode" :"000001" ,# 报告部门
 #         "productGroupCode":"RAW_MATERIAL"  # 商品分类
 #   }
 # }
@@ -294,108 +366,6 @@ ClassifyId = 1214
 # is_del_tow_month = True # 是否删除两个月不更新的特征
 
 
-################################################################################################################  变量定义--雍安测试环境
-login_pushreport_url = "http://192.168.100.115:9090/dom-api/api/server/login"
-upload_url = "http://192.168.100.115:9090/dom-api/api/analysis/reportInfo/researchUploadReportSave"
-# upload_url = "http://192.168.100.109:8080/jingbo/api/analysis/reportInfo/researchUploadReportSave" # zhaoqiwei
-upload_warning_url = "http://192.168.100.115:9090/dom-api/api/basicBuiness/crudeOilWarning/save"
-query_data_list_item_nos_url = "http://192.168.100.115:9090/dom-api/api/warehouse/dwDataItem/queryDataListItemNos"
-
-
-
-
-
-
-login_data = {
-    "data": {
-        "account": "api-dev",
-        "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", 
-        "tenantHashCode": "1eb24ab5a6af12e30daf78af276664f1",
-        "terminal": "API"
-    },
-    "funcModule": "API",
-    "funcOperation": "获取token"
-}
-
-# upload_data = {
-#     "funcModule":'研究报告信息',
-#     "funcOperation":'上传原油价格预测报告',
-#     "data":{
-#         "ownerAccount":'arui', #报告所属用户账号
-#         "reportType":'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
-#         "fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', #文件名称 
-#         "fileBase64": '' ,#文件内容base64
-#         "categoryNo":'yyjgycbg', # 研究报告分类编码
-#         "smartBusinessClassCode":'YCJGYCBG', #分析报告分类编码
-#         "reportEmployeeCode":"E40116", # 报告人
-#         "reportDeptCode" :"D0044" ,# 报告部门
-#         "productGroupCode":"RAW_MATERIAL"  # 商品分类
-#   }
-# }
-
-upload_data = {
-    "funcModule":'研究报告信息',
-    "funcOperation":'上传原油价格预测报告',
-    "data":{
-        "ownerAccount":'arui', #报告所属用户账号
-        "reportType":'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
-        "fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', #文件名称 
-        "fileBase64": '' ,#文件内容base64
-        "categoryNo":'yyjgycbg', # 研究报告分类编码
-        "smartBusinessClassCode":'1', #分析报告分类编码
-        "reportEmployeeCode":"E40116", # 报告人
-        "reportDeptCode" :"D0044" ,# 报告部门
-        "productGroupCode":"RAW_MATERIAL"  # 商品分类
-  }
-}
-
-
-warning_data = {
-    "funcModule":'原油特征停更预警',
-    "funcOperation":'原油特征停更预警',
-    "data":{
-    'WARNING_TYPE_NAME':'特征数据停更预警',
-    'WARNING_CONTENT':'',
-    'WARNING_DATE':''
-  }
-}
-
-query_data_list_item_nos_data = {
-   "funcModule": "数据项",
-   "funcOperation": "查询",
-    "data": {
-        "dateStart":"20200101",
-        "dateEnd":"20241231",
-        "dataItemNoList":["Brentzdj","Brentzgj"] # 数据项编码,代表 brent最低价和最高价
-    }
-}
-
-
-# 北京环境数据库
-host = '192.168.101.27'
-port = 3306
-dbusername ='root'  
-password = '123456'
-dbname = 'jingbo_test'
-table_name = 'v_tbl_crude_oil_warning'
-
-
-### 开关
-is_train = False # 是否训练
-is_debug = False # 是否调试
-is_eta = False # 是否使用eta接口
-is_market = True # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
-is_timefurture = True # 是否使用时间特征
-is_fivemodels = False # 是否使用之前保存的最佳的5个模型
-is_edbcode = False # 特征使用edbcoding列表中的
-is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
-is_update_eta  = False  # 预测结果上传到eta
-is_update_report = True # 是否上传报告
-is_update_warning_data =  False # 是否上传预警数据
-is_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征
-is_del_tow_month = True # 是否删除两个月不更新的特征
-
-
 
 # 连接到数据库
 db_mysql = MySQLDB(host=host, user=dbusername, password=password, database=dbname)
@@ -448,12 +418,14 @@ settings = f'{input_size}-{horizon}-{train_steps}--{k}-{data_set}-{y}'
 now = datetime.datetime.now().strftime('%Y-%m-%d') # 获取当前日期时间
 reportname = f'Brent原油大模型预测--{now}.pdf' # 报告文件名
 reportname = reportname.replace(':', '-') # 替换冒号
-
+if end_time == '':
+    end_time = now
 ### 邮件配置
 username='1321340118@qq.com'
 passwd='wgczgyhtyyyyjghi'
 # recv=['liurui_test@163.com','52585119@qq.com']
-recv=['liurui_test@163.com']
+recv=['liurui_test@163.com','jin.wang@chambroad.com']
+# recv=['liurui_test@163.com']
 title='reportname'
 content='brent价格预测报告请看附件'
 file=os.path.join(dataset,'reportname')
diff --git a/lib/dataread.py b/lib/dataread.py
index 4b5dbce..1cb5a62 100644
--- a/lib/dataread.py
+++ b/lib/dataread.py
@@ -140,12 +140,13 @@ def get_head_auth_report():
         str: 如果登录成功，返回认证令牌；否则返回 None。
     """
     logger.info("获取token中...")
+    logger.info(f'url:{login_pushreport_url},login_data:{login_data}')
     # 发送 POST 请求到登录 URL，携带登录数据
     login_res = requests.post(url=login_pushreport_url, json=login_data, timeout=(3, 30))
     
     # 将响应内容转换为 JSON 格式
     text = json.loads(login_res.text)
-    
+    logger.info(f'token接口响应：{text}')
     # 如果响应状态为成功
     if text["status"]:
         # 从响应数据中获取认证令牌
@@ -1662,6 +1663,7 @@ def get_market_data(end_time,df):
     logger.info('获取数据中...')
     items_res = requests.post(url=query_data_list_item_nos_url, headers=headers, json=query_data_list_item_nos_data, timeout=(3, 35))
     json_data = json.loads(items_res.text)
+    logger.info(f"获取到的数据:{json_data}")
     df3 = pd.DataFrame(json_data['data'])
     # 按照dataItemNo 分组 得到多个dataframe ，最后根据dataDate merge 成一个dataframe
     df2 = pd.DataFrame()
diff --git a/main_yongan.py b/main_yongan.py
new file mode 100644
index 0000000..e1be026
--- /dev/null
+++ b/main_yongan.py
@@ -0,0 +1,288 @@
+# 读取配置
+from lib.dataread import *
+from lib.tools import SendMail,exception_logger
+from models.nerulforcastmodels import ex_Model,model_losss,model_losss_juxiting,brent_export_pdf,tansuanli_export_pdf,pp_export_pdf,model_losss_juxiting
+
+import glob
+import torch
+torch.set_float32_matmul_precision("high")
+
+
+
+def predict_main():
+    """
+    主预测函数，用于从 ETA 获取数据、处理数据、训练模型并进行预测。
+
+    参数:
+        signature (BinanceAPI): Binance API 实例。
+        etadata (EtaReader): ETA 数据读取器实例。
+        is_eta (bool): 是否从 ETA 获取数据。
+        data_set (str): 数据集名称。
+        dataset (str): 数据集路径。
+        add_kdj (bool): 是否添加 KDJ 指标。
+        is_timefurture (bool): 是否添加时间衍生特征。
+        end_time (str): 结束时间。
+        is_edbnamelist (bool): 是否使用 EDB 名称列表。
+        edbnamelist (list): EDB 名称列表。
+        y (str): 预测目标列名。
+        sqlitedb (SQLiteDB): SQLite 数据库实例。
+        is_corr (bool): 是否进行相关性分析。
+        horizon (int): 预测时域。
+        input_size (int): 输入数据大小。
+        train_steps (int): 训练步数。
+        val_check_steps (int): 验证检查步数。
+        early_stop_patience_steps (int): 早停耐心步数。
+        is_debug (bool): 是否调试模式。
+        dataset (str): 数据集名称。
+        is_train (bool): 是否训练模型。
+        is_fivemodels (bool): 是否使用五个模型。
+        val_size (float): 验证集大小。
+        test_size (float): 测试集大小。
+        settings (dict): 模型设置。
+        now (str): 当前时间。
+        etadata (EtaReader): ETA 数据读取器实例。
+        modelsindex (list): 模型索引列表。
+        data (str): 数据类型。
+        is_eta (bool): 是否从 ETA 获取数据。
+
+    返回:
+        None
+    """
+    # global end_time
+    # signature = BinanceAPI(APPID, SECRET)
+    # etadata = EtaReader(signature=signature,
+    #                     classifylisturl=classifylisturl,
+    #                     classifyidlisturl=classifyidlisturl,
+    #                     edbcodedataurl=edbcodedataurl,
+    #                     edbcodelist=edbcodelist,
+    #                     edbdatapushurl=edbdatapushurl,
+    #                     edbdeleteurl=edbdeleteurl,
+    #                     edbbusinessurl=edbbusinessurl
+    #                     )
+    # # 获取数据
+    # if is_eta:
+    #     logger.info('从eta获取数据...')
+    #     signature = BinanceAPI(APPID, SECRET)
+    #     etadata = EtaReader(signature=signature,
+    #                         classifylisturl=classifylisturl,
+    #                         classifyidlisturl=classifyidlisturl,
+    #                         edbcodedataurl=edbcodedataurl,
+    #                         edbcodelist=edbcodelist,
+    #                         edbdatapushurl=edbdatapushurl,
+    #                         edbdeleteurl=edbdeleteurl,
+    #                         edbbusinessurl=edbbusinessurl,
+    #                         )
+    #     df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(data_set=data_set, dataset=dataset)  # 原始数据，未处理
+
+    #     if is_market:
+    #         logger.info('从市场信息平台获取数据...')
+    #         try:
+    #             df_zhibiaoshuju = get_market_data(end_time,df_zhibiaoshuju)
+    #         except :
+    #             logger.info('从市场信息平台获取数据失败')
+        
+    #     # 保存到xlsx文件的sheet表
+    #     with pd.ExcelWriter(os.path.join(dataset,data_set)) as file:
+    #         df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False)
+    #         df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
+        
+        
+    #     # 数据处理
+    #     df = datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=y, dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
+    #                     end_time=end_time)
+
+    # else:
+    #     # 读取数据
+    #     logger.info('读取本地数据：' + os.path.join(dataset, data_set))
+    #     df,df_zhibiaoliebiao = getdata(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
+    #                  is_timefurture=is_timefurture, end_time=end_time)  # 原始数据，未处理
+
+    # # 更改预测列名称
+    # df.rename(columns={y: 'y'}, inplace=True)
+
+    # if is_edbnamelist:
+    #     df = df[edbnamelist]
+    # df.to_csv(os.path.join(dataset, '指标数据.csv'), index=False)
+    # # 保存最新日期的y值到数据库
+    # # 取第一行数据存储到数据库中
+    # first_row = df[['ds', 'y']].tail(1)
+    # print(first_row['ds'].values[0])
+    # print(first_row['y'].values[0])
+    # # 判断y的类型是否为float
+    # if not isinstance(first_row['y'].values[0], float):
+    #     logger.info(f'{end_time}预测目标数据为空，跳过')
+    #     return None
+
+    # # 将最新真实值保存到数据库
+    # if not sqlitedb.check_table_exists('trueandpredict'):
+    #     first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
+    # else:
+    #     for row in first_row.itertuples(index=False):
+    #         row_dict = row._asdict()
+    #         row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
+    #         check_query = sqlitedb.select_data('trueandpredict', where_condition=f"ds = '{row.ds}'")
+    #         if len(check_query) > 0:
+    #             set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
+    #             sqlitedb.update_data('trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
+    #             continue
+    #         sqlitedb.insert_data('trueandpredict', tuple(row_dict.values()), columns=row_dict.keys())
+
+    # # 更新accuracy表的y值
+    # if not sqlitedb.check_table_exists('accuracy'):
+    #     pass
+    # else:
+    #     update_y = sqlitedb.select_data('accuracy',where_condition="y is null")
+    #     if len(update_y) > 0:
+    #         logger.info('更新accuracy表的y值')
+    #         # 找到update_y 中ds且df中的y的行
+    #         update_y = update_y[update_y['ds']<=end_time]
+    #         logger.info(f'要更新y的信息：{update_y}')
+    #         try:
+    #             for row in update_y.itertuples(index=False):
+    #                 row_dict = row._asdict()  	
+    #                 yy = df[df['ds']==row_dict['ds']]['y'].values[0]
+    #                 LOW = df[df['ds']==row_dict['ds']]['Brentzdj'].values[0]
+    #                 HIGH = df[df['ds']==row_dict['ds']]['Brentzgj'].values[0]
+    #                 sqlitedb.update_data('accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
+    #         except Exception as e:
+    #             logger.info(f'更新accuracy表的y值失败：{e}')
+
+    # import datetime
+    # # 判断当前日期是不是周一
+    # is_weekday = datetime.datetime.now().weekday() == 0
+    # if is_weekday:
+    #     logger.info('今天是周一，更新预测模型')
+    #     # 计算最近60天预测残差最低的模型名称
+    #     model_results = sqlitedb.select_data('trueandpredict', order_by="ds DESC", limit="60")
+    #     # 删除空值率为40%以上的列
+    #     if len(model_results) > 10:
+    #         model_results = model_results.dropna(thresh=len(model_results)*0.6,axis=1)
+    #     # 删除空行
+    #     model_results = model_results.dropna()
+    #     modelnames = model_results.columns.to_list()[2:]
+    #     for col in model_results[modelnames].select_dtypes(include=['object']).columns:
+    #         model_results[col] = model_results[col].astype(np.float32)
+    #     # 计算每个预测值与真实值之间的偏差率
+    #     for model in modelnames:
+    #         model_results[f'{model}_abs_error_rate'] = abs(model_results['y'] - model_results[model]) / model_results['y']
+    #     # 获取每行对应的最小偏差率值
+    #     min_abs_error_rate_values = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
+    #     # 获取每行对应的最小偏差率值对应的列名
+    #     min_abs_error_rate_column_name = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
+    #     # 将列名索引转换为列名
+    #     min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(lambda x: x.split('_')[0])
+    #     # 取出现次数最多的模型名称
+    #     most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
+    #     logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
+    #     # 保存结果到数据库
+    #     if not sqlitedb.check_table_exists('most_model'):
+    #         sqlitedb.create_table('most_model', columns="ds datetime, most_common_model TEXT")
+    #     sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
+
+    # try:
+    #     if is_weekday:
+    #     # if True:
+    #         logger.info('今天是周一，发送特征预警')
+    #         # 上传预警信息到数据库
+    #         warning_data_df = df_zhibiaoliebiao.copy()
+    #         warning_data_df = warning_data_df[warning_data_df['停更周期']> 3 ][['指标名称', '指标id', '频度','更新周期','指标来源','最后更新时间','停更周期']]
+    #         # 重命名列名
+    #         warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY', '更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'UPDATE_SUSPENSION_CYCLE'})
+    #         from sqlalchemy import create_engine
+    #         import urllib
+    #         global password
+    #         if '@' in password:
+    #             password = urllib.parse.quote_plus(password)
+
+    #         engine = create_engine(f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
+    #         warning_data_df['WARNING_DATE'] =  datetime.date.today().strftime("%Y-%m-%d %H:%M:%S")
+    #         warning_data_df['TENANT_CODE'] =  'T0004'
+    #         # 插入数据之前查询表数据然后新增id列
+    #         existing_data = pd.read_sql(f"SELECT * FROM {table_name}", engine)
+    #         if not existing_data.empty:
+    #             max_id = existing_data['ID'].astype(int).max()
+    #             warning_data_df['ID'] = range(max_id + 1, max_id + 1 + len(warning_data_df))
+    #         else:
+    #             warning_data_df['ID'] = range(1, 1 + len(warning_data_df))
+    #         warning_data_df.to_sql(table_name,  con=engine, if_exists='append', index=False)
+    #         if is_update_warning_data:
+    #             upload_warning_info(len(warning_data_df))
+    # except:
+    #     logger.info('上传预警信息到数据库失败')
+
+    # if is_corr:
+    #     df = corr_feature(df=df)
+
+    # df1 = df.copy()  # 备份一下，后面特征筛选完之后加入ds y 列用
+    # logger.info(f"开始训练模型...")
+    # row, col = df.shape
+
+    # now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
+    # ex_Model(df,
+    #          horizon=horizon,
+    #          input_size=input_size,
+    #          train_steps=train_steps,
+    #          val_check_steps=val_check_steps,
+    #          early_stop_patience_steps=early_stop_patience_steps,
+    #          is_debug=is_debug,
+    #          dataset=dataset,
+    #          is_train=is_train,
+    #          is_fivemodels=is_fivemodels,
+    #          val_size=val_size,
+    #          test_size=test_size,
+    #          settings=settings,
+    #          now=now,
+    #          etadata=etadata,
+    #          modelsindex=modelsindex,
+    #          data=data,
+    #          is_eta=is_eta,
+    #          end_time=end_time,
+    #          )
+
+
+    # logger.info('模型训练完成')
+ 
+    # logger.info('训练数据绘图ing')
+    # model_results3 = model_losss(sqlitedb,end_time=end_time)
+    # logger.info('训练数据绘图end')
+    
+    # 模型报告
+    logger.info('制作报告ing')
+    title = f'{settings}--{end_time}-预测报告' # 报告标题
+    reportname = f'Brent原油大模型预测--{end_time}.pdf' # 报告文件名
+    reportname = reportname.replace(':', '-') # 替换冒号
+    brent_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time,
+                reportname=reportname,sqlitedb=sqlitedb),
+
+    logger.info('制作报告end')
+    logger.info('模型训练完成')
+
+    # # LSTM 单变量模型
+    # ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
+    
+    # # lstm 多变量模型
+    # ex_Lstm_M(df,n_days=input_size,out_days=horizon,is_debug=is_debug,datasetpath=dataset)
+    
+    # # GRU 模型
+    # # ex_GRU(df)
+
+    # 发送邮件
+    # m = SendMail(
+    #     username=username,
+    #     passwd=passwd,
+    #     recv=recv,
+    #     title=title,
+    #     content=content,
+    #     file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime),
+    #     ssl=ssl,
+    # )
+    # m.send_mail()   
+
+
+if __name__ == '__main__':
+    global end_time
+    is_on = True
+    # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
+    for i_time in pd.date_range('2024-12-27', '2024-12-28', freq='B'):
+        end_time = i_time.strftime('%Y-%m-%d')
+        predict_main()
diff --git a/main_yuanyou.py b/main_yuanyou.py
index e1be026..dc5825f 100644
--- a/main_yuanyou.py
+++ b/main_yuanyou.py
@@ -48,203 +48,203 @@ def predict_main():
     返回:
         None
     """
-    # global end_time
-    # signature = BinanceAPI(APPID, SECRET)
-    # etadata = EtaReader(signature=signature,
-    #                     classifylisturl=classifylisturl,
-    #                     classifyidlisturl=classifyidlisturl,
-    #                     edbcodedataurl=edbcodedataurl,
-    #                     edbcodelist=edbcodelist,
-    #                     edbdatapushurl=edbdatapushurl,
-    #                     edbdeleteurl=edbdeleteurl,
-    #                     edbbusinessurl=edbbusinessurl
-    #                     )
-    # # 获取数据
-    # if is_eta:
-    #     logger.info('从eta获取数据...')
-    #     signature = BinanceAPI(APPID, SECRET)
-    #     etadata = EtaReader(signature=signature,
-    #                         classifylisturl=classifylisturl,
-    #                         classifyidlisturl=classifyidlisturl,
-    #                         edbcodedataurl=edbcodedataurl,
-    #                         edbcodelist=edbcodelist,
-    #                         edbdatapushurl=edbdatapushurl,
-    #                         edbdeleteurl=edbdeleteurl,
-    #                         edbbusinessurl=edbbusinessurl,
-    #                         )
-    #     df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(data_set=data_set, dataset=dataset)  # 原始数据，未处理
+    global end_time
+    signature = BinanceAPI(APPID, SECRET)
+    etadata = EtaReader(signature=signature,
+                        classifylisturl=classifylisturl,
+                        classifyidlisturl=classifyidlisturl,
+                        edbcodedataurl=edbcodedataurl,
+                        edbcodelist=edbcodelist,
+                        edbdatapushurl=edbdatapushurl,
+                        edbdeleteurl=edbdeleteurl,
+                        edbbusinessurl=edbbusinessurl
+                        )
+    # 获取数据
+    if is_eta:
+        logger.info('从eta获取数据...')
+        signature = BinanceAPI(APPID, SECRET)
+        etadata = EtaReader(signature=signature,
+                            classifylisturl=classifylisturl,
+                            classifyidlisturl=classifyidlisturl,
+                            edbcodedataurl=edbcodedataurl,
+                            edbcodelist=edbcodelist,
+                            edbdatapushurl=edbdatapushurl,
+                            edbdeleteurl=edbdeleteurl,
+                            edbbusinessurl=edbbusinessurl,
+                            )
+        df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(data_set=data_set, dataset=dataset)  # 原始数据，未处理
 
-    #     if is_market:
-    #         logger.info('从市场信息平台获取数据...')
-    #         try:
-    #             df_zhibiaoshuju = get_market_data(end_time,df_zhibiaoshuju)
-    #         except :
-    #             logger.info('从市场信息平台获取数据失败')
+        if is_market:
+            logger.info('从市场信息平台获取数据...')
+            try:
+                df_zhibiaoshuju = get_market_data(end_time,df_zhibiaoshuju)
+            except :
+                logger.info('从市场信息平台获取数据失败')
         
-    #     # 保存到xlsx文件的sheet表
-    #     with pd.ExcelWriter(os.path.join(dataset,data_set)) as file:
-    #         df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False)
-    #         df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
+        # 保存到xlsx文件的sheet表
+        with pd.ExcelWriter(os.path.join(dataset,data_set)) as file:
+            df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False)
+            df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
         
         
-    #     # 数据处理
-    #     df = datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=y, dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
-    #                     end_time=end_time)
+        # 数据处理
+        df = datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=y, dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
+                        end_time=end_time)
 
-    # else:
-    #     # 读取数据
-    #     logger.info('读取本地数据：' + os.path.join(dataset, data_set))
-    #     df,df_zhibiaoliebiao = getdata(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
-    #                  is_timefurture=is_timefurture, end_time=end_time)  # 原始数据，未处理
+    else:
+        # 读取数据
+        logger.info('读取本地数据：' + os.path.join(dataset, data_set))
+        df,df_zhibiaoliebiao = getdata(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
+                     is_timefurture=is_timefurture, end_time=end_time)  # 原始数据，未处理
 
-    # # 更改预测列名称
-    # df.rename(columns={y: 'y'}, inplace=True)
+    # 更改预测列名称
+    df.rename(columns={y: 'y'}, inplace=True)
 
-    # if is_edbnamelist:
-    #     df = df[edbnamelist]
-    # df.to_csv(os.path.join(dataset, '指标数据.csv'), index=False)
-    # # 保存最新日期的y值到数据库
-    # # 取第一行数据存储到数据库中
-    # first_row = df[['ds', 'y']].tail(1)
-    # print(first_row['ds'].values[0])
-    # print(first_row['y'].values[0])
-    # # 判断y的类型是否为float
-    # if not isinstance(first_row['y'].values[0], float):
-    #     logger.info(f'{end_time}预测目标数据为空，跳过')
-    #     return None
+    if is_edbnamelist:
+        df = df[edbnamelist]
+    df.to_csv(os.path.join(dataset, '指标数据.csv'), index=False)
+    # 保存最新日期的y值到数据库
+    # 取第一行数据存储到数据库中
+    first_row = df[['ds', 'y']].tail(1)
+    print(first_row['ds'].values[0])
+    print(first_row['y'].values[0])
+    # 判断y的类型是否为float
+    if not isinstance(first_row['y'].values[0], float):
+        logger.info(f'{end_time}预测目标数据为空，跳过')
+        return None
 
-    # # 将最新真实值保存到数据库
-    # if not sqlitedb.check_table_exists('trueandpredict'):
-    #     first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
-    # else:
-    #     for row in first_row.itertuples(index=False):
-    #         row_dict = row._asdict()
-    #         row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
-    #         check_query = sqlitedb.select_data('trueandpredict', where_condition=f"ds = '{row.ds}'")
-    #         if len(check_query) > 0:
-    #             set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
-    #             sqlitedb.update_data('trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
-    #             continue
-    #         sqlitedb.insert_data('trueandpredict', tuple(row_dict.values()), columns=row_dict.keys())
+    # 将最新真实值保存到数据库
+    if not sqlitedb.check_table_exists('trueandpredict'):
+        first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
+    else:
+        for row in first_row.itertuples(index=False):
+            row_dict = row._asdict()
+            row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
+            check_query = sqlitedb.select_data('trueandpredict', where_condition=f"ds = '{row.ds}'")
+            if len(check_query) > 0:
+                set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
+                sqlitedb.update_data('trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
+                continue
+            sqlitedb.insert_data('trueandpredict', tuple(row_dict.values()), columns=row_dict.keys())
 
-    # # 更新accuracy表的y值
-    # if not sqlitedb.check_table_exists('accuracy'):
-    #     pass
-    # else:
-    #     update_y = sqlitedb.select_data('accuracy',where_condition="y is null")
-    #     if len(update_y) > 0:
-    #         logger.info('更新accuracy表的y值')
-    #         # 找到update_y 中ds且df中的y的行
-    #         update_y = update_y[update_y['ds']<=end_time]
-    #         logger.info(f'要更新y的信息：{update_y}')
-    #         try:
-    #             for row in update_y.itertuples(index=False):
-    #                 row_dict = row._asdict()  	
-    #                 yy = df[df['ds']==row_dict['ds']]['y'].values[0]
-    #                 LOW = df[df['ds']==row_dict['ds']]['Brentzdj'].values[0]
-    #                 HIGH = df[df['ds']==row_dict['ds']]['Brentzgj'].values[0]
-    #                 sqlitedb.update_data('accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
-    #         except Exception as e:
-    #             logger.info(f'更新accuracy表的y值失败：{e}')
+    # 更新accuracy表的y值
+    if not sqlitedb.check_table_exists('accuracy'):
+        pass
+    else:
+        update_y = sqlitedb.select_data('accuracy',where_condition="y is null")
+        if len(update_y) > 0:
+            logger.info('更新accuracy表的y值')
+            # 找到update_y 中ds且df中的y的行
+            update_y = update_y[update_y['ds']<=end_time]
+            logger.info(f'要更新y的信息：{update_y}')
+            try:
+                for row in update_y.itertuples(index=False):
+                    row_dict = row._asdict()  	
+                    yy = df[df['ds']==row_dict['ds']]['y'].values[0]
+                    LOW = df[df['ds']==row_dict['ds']]['Brentzdj'].values[0]
+                    HIGH = df[df['ds']==row_dict['ds']]['Brentzgj'].values[0]
+                    sqlitedb.update_data('accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
+            except Exception as e:
+                logger.info(f'更新accuracy表的y值失败：{e}')
 
-    # import datetime
-    # # 判断当前日期是不是周一
-    # is_weekday = datetime.datetime.now().weekday() == 0
-    # if is_weekday:
-    #     logger.info('今天是周一，更新预测模型')
-    #     # 计算最近60天预测残差最低的模型名称
-    #     model_results = sqlitedb.select_data('trueandpredict', order_by="ds DESC", limit="60")
-    #     # 删除空值率为40%以上的列
-    #     if len(model_results) > 10:
-    #         model_results = model_results.dropna(thresh=len(model_results)*0.6,axis=1)
-    #     # 删除空行
-    #     model_results = model_results.dropna()
-    #     modelnames = model_results.columns.to_list()[2:]
-    #     for col in model_results[modelnames].select_dtypes(include=['object']).columns:
-    #         model_results[col] = model_results[col].astype(np.float32)
-    #     # 计算每个预测值与真实值之间的偏差率
-    #     for model in modelnames:
-    #         model_results[f'{model}_abs_error_rate'] = abs(model_results['y'] - model_results[model]) / model_results['y']
-    #     # 获取每行对应的最小偏差率值
-    #     min_abs_error_rate_values = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
-    #     # 获取每行对应的最小偏差率值对应的列名
-    #     min_abs_error_rate_column_name = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
-    #     # 将列名索引转换为列名
-    #     min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(lambda x: x.split('_')[0])
-    #     # 取出现次数最多的模型名称
-    #     most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
-    #     logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
-    #     # 保存结果到数据库
-    #     if not sqlitedb.check_table_exists('most_model'):
-    #         sqlitedb.create_table('most_model', columns="ds datetime, most_common_model TEXT")
-    #     sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
+    import datetime
+    # 判断当前日期是不是周一
+    is_weekday = datetime.datetime.now().weekday() == 0
+    if is_weekday:
+        logger.info('今天是周一，更新预测模型')
+        # 计算最近60天预测残差最低的模型名称
+        model_results = sqlitedb.select_data('trueandpredict', order_by="ds DESC", limit="60")
+        # 删除空值率为90%以上的列
+        if len(model_results) > 10:
+            model_results = model_results.dropna(thresh=len(model_results)*0.1,axis=1)
+        # 删除空行
+        model_results = model_results.dropna()
+        modelnames = model_results.columns.to_list()[2:-1]
+        for col in model_results[modelnames].select_dtypes(include=['object']).columns:
+            model_results[col] = model_results[col].astype(np.float32)
+        # 计算每个预测值与真实值之间的偏差率
+        for model in modelnames:
+            model_results[f'{model}_abs_error_rate'] = abs(model_results['y'] - model_results[model]) / model_results['y']
+        # 获取每行对应的最小偏差率值
+        min_abs_error_rate_values = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
+        # 获取每行对应的最小偏差率值对应的列名
+        min_abs_error_rate_column_name = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
+        # 将列名索引转换为列名
+        min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(lambda x: x.split('_')[0])
+        # 取出现次数最多的模型名称
+        most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
+        logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
+        # 保存结果到数据库
+        if not sqlitedb.check_table_exists('most_model'):
+            sqlitedb.create_table('most_model', columns="ds datetime, most_common_model TEXT")
+        sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
 
-    # try:
-    #     if is_weekday:
-    #     # if True:
-    #         logger.info('今天是周一，发送特征预警')
-    #         # 上传预警信息到数据库
-    #         warning_data_df = df_zhibiaoliebiao.copy()
-    #         warning_data_df = warning_data_df[warning_data_df['停更周期']> 3 ][['指标名称', '指标id', '频度','更新周期','指标来源','最后更新时间','停更周期']]
-    #         # 重命名列名
-    #         warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY', '更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'UPDATE_SUSPENSION_CYCLE'})
-    #         from sqlalchemy import create_engine
-    #         import urllib
-    #         global password
-    #         if '@' in password:
-    #             password = urllib.parse.quote_plus(password)
+    try:
+        if is_weekday:
+        # if True:
+            logger.info('今天是周一，发送特征预警')
+            # 上传预警信息到数据库
+            warning_data_df = df_zhibiaoliebiao.copy()
+            warning_data_df = warning_data_df[warning_data_df['停更周期']> 3 ][['指标名称', '指标id', '频度','更新周期','指标来源','最后更新时间','停更周期']]
+            # 重命名列名
+            warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY', '更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'UPDATE_SUSPENSION_CYCLE'})
+            from sqlalchemy import create_engine
+            import urllib
+            global password
+            if '@' in password:
+                password = urllib.parse.quote_plus(password)
 
-    #         engine = create_engine(f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
-    #         warning_data_df['WARNING_DATE'] =  datetime.date.today().strftime("%Y-%m-%d %H:%M:%S")
-    #         warning_data_df['TENANT_CODE'] =  'T0004'
-    #         # 插入数据之前查询表数据然后新增id列
-    #         existing_data = pd.read_sql(f"SELECT * FROM {table_name}", engine)
-    #         if not existing_data.empty:
-    #             max_id = existing_data['ID'].astype(int).max()
-    #             warning_data_df['ID'] = range(max_id + 1, max_id + 1 + len(warning_data_df))
-    #         else:
-    #             warning_data_df['ID'] = range(1, 1 + len(warning_data_df))
-    #         warning_data_df.to_sql(table_name,  con=engine, if_exists='append', index=False)
-    #         if is_update_warning_data:
-    #             upload_warning_info(len(warning_data_df))
-    # except:
-    #     logger.info('上传预警信息到数据库失败')
+            engine = create_engine(f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
+            warning_data_df['WARNING_DATE'] =  datetime.date.today().strftime("%Y-%m-%d %H:%M:%S")
+            warning_data_df['TENANT_CODE'] =  'T0004'
+            # 插入数据之前查询表数据然后新增id列
+            existing_data = pd.read_sql(f"SELECT * FROM {table_name}", engine)
+            if not existing_data.empty:
+                max_id = existing_data['ID'].astype(int).max()
+                warning_data_df['ID'] = range(max_id + 1, max_id + 1 + len(warning_data_df))
+            else:
+                warning_data_df['ID'] = range(1, 1 + len(warning_data_df))
+            warning_data_df.to_sql(table_name,  con=engine, if_exists='append', index=False)
+            if is_update_warning_data:
+                upload_warning_info(len(warning_data_df))
+    except:
+        logger.info('上传预警信息到数据库失败')
 
-    # if is_corr:
-    #     df = corr_feature(df=df)
+    if is_corr:
+        df = corr_feature(df=df)
 
-    # df1 = df.copy()  # 备份一下，后面特征筛选完之后加入ds y 列用
-    # logger.info(f"开始训练模型...")
-    # row, col = df.shape
+    df1 = df.copy()  # 备份一下，后面特征筛选完之后加入ds y 列用
+    logger.info(f"开始训练模型...")
+    row, col = df.shape
 
-    # now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
-    # ex_Model(df,
-    #          horizon=horizon,
-    #          input_size=input_size,
-    #          train_steps=train_steps,
-    #          val_check_steps=val_check_steps,
-    #          early_stop_patience_steps=early_stop_patience_steps,
-    #          is_debug=is_debug,
-    #          dataset=dataset,
-    #          is_train=is_train,
-    #          is_fivemodels=is_fivemodels,
-    #          val_size=val_size,
-    #          test_size=test_size,
-    #          settings=settings,
-    #          now=now,
-    #          etadata=etadata,
-    #          modelsindex=modelsindex,
-    #          data=data,
-    #          is_eta=is_eta,
-    #          end_time=end_time,
-    #          )
+    now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
+    ex_Model(df,
+             horizon=horizon,
+             input_size=input_size,
+             train_steps=train_steps,
+             val_check_steps=val_check_steps,
+             early_stop_patience_steps=early_stop_patience_steps,
+             is_debug=is_debug,
+             dataset=dataset,
+             is_train=is_train,
+             is_fivemodels=is_fivemodels,
+             val_size=val_size,
+             test_size=test_size,
+             settings=settings,
+             now=now,
+             etadata=etadata,
+             modelsindex=modelsindex,
+             data=data,
+             is_eta=is_eta,
+             end_time=end_time,
+             )
 
 
-    # logger.info('模型训练完成')
+    logger.info('模型训练完成')
  
-    # logger.info('训练数据绘图ing')
-    # model_results3 = model_losss(sqlitedb,end_time=end_time)
-    # logger.info('训练数据绘图end')
+    logger.info('训练数据绘图ing')
+    model_results3 = model_losss(sqlitedb,end_time=end_time)
+    logger.info('训练数据绘图end')
     
     # 模型报告
     logger.info('制作报告ing')
@@ -267,22 +267,24 @@ def predict_main():
     # # ex_GRU(df)
 
     # 发送邮件
-    # m = SendMail(
-    #     username=username,
-    #     passwd=passwd,
-    #     recv=recv,
-    #     title=title,
-    #     content=content,
-    #     file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime),
-    #     ssl=ssl,
-    # )
-    # m.send_mail()   
+    m = SendMail(
+        username=username,
+        passwd=passwd,
+        recv=recv,
+        title=title,
+        content=content,
+        file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime),
+        ssl=ssl,
+    )
+    m.send_mail()   
 
 
 if __name__ == '__main__':
-    global end_time
-    is_on = True
-    # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
-    for i_time in pd.date_range('2024-12-27', '2024-12-28', freq='B'):
-        end_time = i_time.strftime('%Y-%m-%d')
-        predict_main()
+    # global end_time
+    # is_on = True
+    # # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
+    # for i_time in pd.date_range('2024-12-27', '2024-12-28', freq='B'):
+    #     end_time = i_time.strftime('%Y-%m-%d')
+    #     predict_main()
+
+    predict_main()
\ No newline at end of file
diff --git a/models/nerulforcastmodels.py b/models/nerulforcastmodels.py
index 0181609..affe008 100644
--- a/models/nerulforcastmodels.py
+++ b/models/nerulforcastmodels.py
@@ -317,6 +317,28 @@ def model_losss(sqlitedb,end_time):
     except ValueError :
         df_predict['ds'] = pd.to_datetime(df_predict['ds'],format=r'%Y/%m/%d')
     
+    def first_row_to_database(df):
+        # # 取第一行数据存储到数据库中
+        first_row = df.head(1)
+        first_row['ds'] = first_row['ds'].dt.strftime('%Y-%m-%d 00:00:00')
+        # 将预测结果保存到数据库
+        if not sqlitedb.check_table_exists('trueandpredict'):
+            first_row.to_sql('trueandpredict',sqlitedb.connection,index=False)
+        else:
+            for col in first_row.columns:
+                    sqlitedb.add_column_if_not_exists('trueandpredict',col,'TEXT')
+            for row in first_row.itertuples(index=False):
+                row_dict = row._asdict()
+                columns=row_dict.keys()
+                check_query = sqlitedb.select_data('trueandpredict',where_condition = f"ds = '{row.ds}'")
+                if len(check_query) > 0:
+                    set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
+                    sqlitedb.update_data('trueandpredict',set_clause,where_condition = f"ds = '{row.ds}'")
+                    continue
+                sqlitedb.insert_data('trueandpredict',tuple(row_dict.values()),columns=columns)
+
+    first_row_to_database(df_predict)
+    
     df_combined3 = pd.concat([df_combined3, df_predict]).reset_index(drop=True)
 
     # 计算每个模型与最佳模型的绝对误差比例，根据设置的阈值rote筛选预测值显示最大最小值
@@ -1018,6 +1040,7 @@ def brent_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inpu
     df4 = sqlitedb.select_data('accuracy_rote',order_by='结束日期 desc',limit=1)
     df4 = df4.T
     df4 = df4.reset_index()
+    df4 = df4.T
     data = df4.values.tolist()
     col_width = 500/len(df4.columns)
     content.append(Graphs.draw_table(col_width,*data))
@@ -1133,7 +1156,7 @@ def brent_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inpu
     eval_df = eval_df.T
     # df重置索引
     eval_df = eval_df.reset_index()
-    # eval_df = eval_df.T
+    eval_df = eval_df.T
     # # 添加表格
     data = eval_df.values.tolist()
     col_width = 500/len(eval_df.columns)