添加埃森哲价格预测代码

aisenzhecode/沥青/沥青定性模型每日推送-ytj.ipynb

@@ -0,0 +1,530 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "import json\n",
+    "import xlrd\n",
+    "import xlwt\n",
+    "from datetime import datetime, timedelta \n",
+    "import time\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "# 变量定义\n",
+    "login_url = \"http://10.200.32.39/jingbo-api/api/server/login\"\n",
+    "search_url = \"http://10.200.32.39/jingbo-api/api/warehouse/dwDataItem/queryByItemNos\"\n",
+    "\n",
+    "login_push_url = \"http://10.200.32.39/jingbo-api/api/server/login\"\n",
+    "upload_url = \"http://10.200.32.39/jingbo-api/api/dw/dataValue/pushDataValueList\"\n",
+    "\n",
+    "login_data = {\n",
+    "    \"data\": {\n",
+    "        \"account\": \"api_dev\",\n",
+    "        \"password\": \"ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=\",\n",
+    "        \"tenantHashCode\": \"8a4577dbd919675758d57999a1e891fe\",\n",
+    "        \"terminal\": \"API\"\n",
+    "    },\n",
+    "    \"funcModule\": \"API\",\n",
+    "    \"funcOperation\": \"获取token\"\n",
+    "}\n",
+    "\n",
+    "login_push_data = {\n",
+    "    \"data\": {\n",
+    "        \"account\": \"api_dev\",\n",
+    "        \"password\": \"ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=\",\n",
+    "        \"tenantHashCode\": \"8a4577dbd919675758d57999a1e891fe\",\n",
+    "        \"terminal\": \"API\"\n",
+    "    },\n",
+    "    \"funcModule\": \"API\",\n",
+    "    \"funcOperation\": \"获取token\"\n",
+    "}\n",
+    "\n",
+    "read_file_path_name = \"定性模型数据项12-11.xls\"\n",
+    "one_cols = []\n",
+    "two_cols = []\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "def start(date=''):\n",
+    "    workbook = xlrd.open_workbook(read_file_path_name)\n",
+    "\n",
+    "\n",
+    "\n",
+    "    # 选择第一个表格\n",
+    "    sheet = workbook.sheet_by_index(0)\n",
+    "\n",
+    "    # 获取行数和列数\n",
+    "    num_rows = sheet.nrows\n",
+    "\n",
+    "\n",
+    "\n",
+    "    row_data = sheet.row_values(1)\n",
+    "    one_cols = row_data\n",
+    "\n",
+    "\n",
+    "    login_res = requests.post(url=login_url, json=login_data, timeout=(3, 5))\n",
+    "    text = json.loads(login_res.text)\n",
+    "    if text[\"status\"]:\n",
+    "        token = text[\"data\"][\"accessToken\"]\n",
+    "    else:\n",
+    "        print(\"获取认证失败\")\n",
+    "        token =  None\n",
+    "\n",
+    "    if date == '':\n",
+    "        now = datetime.now()\n",
+    "    else:\n",
+    "        now = date\n",
+    "    year = now.year\n",
+    "    month = now.month\n",
+    "    day = now.day\n",
+    "\n",
+    "    if month < 10:\n",
+    "        month = \"0\" + str(month)\n",
+    "    if day < 10:\n",
+    "        day = \"0\" + str(day)\n",
+    "    cur_time = str(year) + str(month) + str(day)\n",
+    "    cur_time2 = str(year) + \"-\" + str(month) + \"-\" + str(day)\n",
+    "    search_data = {\n",
+    "        \"data\": {\n",
+    "            \"date\": cur_time,\n",
+    "            \"dataItemNoList\": one_cols[1:]\n",
+    "        },\n",
+    "        \"funcModule\": \"数据项\",\n",
+    "        \"funcOperation\": \"查询\"\n",
+    "    }\n",
+    "    headers = {\"Authorization\": token}\n",
+    "    search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))\n",
+    "    search_value = json.loads(search_res.text)[\"data\"]\n",
+    "#     datas = search_value\n",
+    "    if search_value:\n",
+    "        datas = search_value\n",
+    "    else :\n",
+    "        datas = None\n",
+    "   \n",
+    "\n",
+    "    append_rows = [cur_time2]\n",
+    "    dataItemNo_dataValue = {}\n",
+    "#     for data_value in datas:\n",
+    "#         dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
+    "    for data_value in datas:\n",
+    "        if \"dataValue\" not in data_value:\n",
+    "            print(data_value)\n",
+    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n",
+    "        else:\n",
+    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
+    "    for value in one_cols[1:]:\n",
+    "        if value in dataItemNo_dataValue:\n",
+    "            append_rows.append(dataItemNo_dataValue[value])\n",
+    "        else:\n",
+    "            append_rows.append(\"\")\n",
+    "\n",
+    "    workbook = xlrd.open_workbook('定性模型数据项12-11.xls')\n",
+    "\n",
+    "    # 获取所有sheet的个数\n",
+    "    sheet_count = len(workbook.sheet_names())\n",
+    "\n",
+    "    # 获取所有sheet的名称\n",
+    "    sheet_names = workbook.sheet_names()\n",
+    "\n",
+    "    new_workbook = xlwt.Workbook()\n",
+    "    for i in range(sheet_count):\n",
+    "        # 获取当前sheet\n",
+    "        sheet = workbook.sheet_by_index(i)\n",
+    "\n",
+    "        # 获取sheet的行数和列数\n",
+    "        row_count = sheet.nrows\n",
+    "        col_count = sheet.ncols\n",
+    "        # 获取原有数据\n",
+    "        data = []\n",
+    "        for row in range(row_count):\n",
+    "            row_data = []\n",
+    "            for col in range(col_count):\n",
+    "                row_data.append(sheet.cell_value(row, col))\n",
+    "            data.append(row_data)\n",
+    "        # 创建xlwt的Workbook对象\n",
+    "        # 创建sheet\n",
+    "        new_sheet = new_workbook.add_sheet(sheet_names[i])\n",
+    "\n",
+    "        # 将原有的数据写入新的sheet\n",
+    "        for row in range(row_count):\n",
+    "            for col in range(col_count):\n",
+    "                new_sheet.write(row, col, data[row][col])\n",
+    "\n",
+    "        if i == 0:\n",
+    "            # 在新的sheet中添加数据\n",
+    "            for col in range(col_count):\n",
+    "                new_sheet.write(row_count, col, append_rows[col])\n",
+    "\n",
+    "    # 保存新的xls文件\n",
+    "    new_workbook.save(\"定性模型数据项12-11.xls\")\n",
+    "\n",
+    "    df = pd.read_excel('定性模型数据项12-11.xls')\n",
+    "    df=df.fillna(df.ffill())\n",
+    "    df1 = df[-2:].reset_index()\n",
+    "    if df1.loc[1,'70号沥青开工率'] > 0.3:\n",
+    "        a = (df1.loc[1,'70号沥青开工率']-0.2)*5/0.1\n",
+    "    else :\n",
+    "        a = 0\n",
+    "    b = df1.loc[1,'资金因素']\n",
+    "    if df1.loc[1,'昨日计划提货偏差']>0:\n",
+    "        c = df1.loc[1,'昨日计划提货偏差']*10/2000\n",
+    "    else :\n",
+    "        c = df1.loc[1,'昨日计划提货偏差']*10/3000\n",
+    "    d = df1.loc[1,'生产情况']\n",
+    "    if df1.loc[1,'基质沥青库存']/265007 >0.8:\n",
+    "        e = (df1.loc[1,'基质沥青库存'] - df1.loc[0,'基质沥青库存'])*10/-5000\n",
+    "    else : \n",
+    "        e = 0\n",
+    "    f = df1.loc[1,'下游客户价格预期']\n",
+    "    if abs(df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])>=100:\n",
+    "        g = (df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])*50/100\n",
+    "    else :\n",
+    "        g = 0\n",
+    "    h = df1.loc[1,'订单结构']\n",
+    "    x = round(0.08*a+0*b+0.15*c+0.08*d +0.03*e +0.08*f +0.4*g+0.18*h+df1.loc[0,'京博指导价'],2)\n",
+    "\n",
+    "\n",
+    "    login_res1 = requests.post(url=login_push_url, json=login_push_data, timeout=(3, 5))\n",
+    "    text1 = json.loads(login_res1.text)\n",
+    "    token_push = text1[\"data\"][\"accessToken\"]\n",
+    "\n",
+    "\n",
+    "    data1 = {\n",
+    "        \"funcModule\": \"数据表信息列表\",\n",
+    "        \"funcOperation\": \"新增\",\n",
+    "        \"data\": [\n",
+    "            {\"dataItemNo\": \"C01100036|Forecast_Price|DX|ACN\",\n",
+    "             \"dataDate\": cur_time,\n",
+    "             \"dataStatus\": \"add\",\n",
+    "             \"dataValue\": x\n",
+    "             }\n",
+    "\n",
+    "        ]\n",
+    "    }\n",
+    "    headers1 = {\"Authorization\": token_push}\n",
+    "    res = requests.post(url=upload_url, headers=headers1, json=data1, timeout=(3, 5))\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "def start_1():\n",
+    "    workbook = xlrd.open_workbook(read_file_path_name)\n",
+    "\n",
+    "\n",
+    "\n",
+    "    # 选择第一个表格\n",
+    "    sheet = workbook.sheet_by_index(0)\n",
+    "\n",
+    "    # 获取行数和列数\n",
+    "    num_rows = sheet.nrows\n",
+    "\n",
+    "\n",
+    "\n",
+    "    row_data = sheet.row_values(1)\n",
+    "    one_cols = row_data\n",
+    "\n",
+    "\n",
+    "    login_res = requests.post(url=login_url, json=login_data, timeout=(3, 5))\n",
+    "    text = json.loads(login_res.text)\n",
+    "    if text[\"status\"]:\n",
+    "        token = text[\"data\"][\"accessToken\"]\n",
+    "    else:\n",
+    "        print(\"获取认证失败\")\n",
+    "        token =  None\n",
+    "\n",
+    "\n",
+    "    now = datetime.now()  - timedelta(days=1)  \n",
+    "    year = now.year\n",
+    "    month = now.month\n",
+    "    day = now.day\n",
+    "\n",
+    "    if month < 10:\n",
+    "        month = \"0\" + str(month)\n",
+    "    if day < 10:\n",
+    "        day = \"0\" + str(day)\n",
+    "    cur_time = str(year) + str(month) + str(day)\n",
+    "    cur_time2 = str(year) + \"-\" + str(month) + \"-\" + str(day)\n",
+    "    search_data = {\n",
+    "        \"data\": {\n",
+    "            \"date\": cur_time,\n",
+    "            \"dataItemNoList\": one_cols[1:]\n",
+    "        },\n",
+    "        \"funcModule\": \"数据项\",\n",
+    "        \"funcOperation\": \"查询\"\n",
+    "    }\n",
+    "    headers = {\"Authorization\": token}\n",
+    "    search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))\n",
+    "    search_value = json.loads(search_res.text)[\"data\"]\n",
+    "#     datas = search_value\n",
+    "    if search_value:\n",
+    "        datas = search_value\n",
+    "    else :\n",
+    "        datas = None\n",
+    "    \n",
+    "   \n",
+    "\n",
+    "    append_rows = [cur_time2]\n",
+    "    dataItemNo_dataValue = {}\n",
+    "#     for data_value in datas:\n",
+    "#         dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
+    "    for data_value in datas:\n",
+    "        if \"dataValue\" not in data_value:\n",
+    "            print(data_value)\n",
+    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n",
+    "        else:\n",
+    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
+    "    for value in one_cols[1:]:\n",
+    "        if value in dataItemNo_dataValue:\n",
+    "            append_rows.append(dataItemNo_dataValue[value])\n",
+    "        else:\n",
+    "            append_rows.append(\"\")\n",
+    "\n",
+    "    workbook = xlrd.open_workbook('定性模型数据项12-11.xls')\n",
+    "\n",
+    "    # 获取所有sheet的个数\n",
+    "    sheet_count = len(workbook.sheet_names())\n",
+    "\n",
+    "    # 获取所有sheet的名称\n",
+    "    sheet_names = workbook.sheet_names()\n",
+    "\n",
+    "    new_workbook = xlwt.Workbook()\n",
+    "    for i in range(sheet_count):\n",
+    "        # 获取当前sheet\n",
+    "        sheet = workbook.sheet_by_index(i)\n",
+    "\n",
+    "        # 获取sheet的行数和列数\n",
+    "        row_count = sheet.nrows - 1\n",
+    "        col_count = sheet.ncols\n",
+    "        # 获取原有数据\n",
+    "        data = []\n",
+    "        for row in range(row_count):\n",
+    "            row_data = []\n",
+    "            for col in range(col_count):\n",
+    "                row_data.append(sheet.cell_value(row, col))\n",
+    "            data.append(row_data)\n",
+    "        # 创建xlwt的Workbook对象\n",
+    "        # 创建sheet\n",
+    "        new_sheet = new_workbook.add_sheet(sheet_names[i])\n",
+    "\n",
+    "        # 将原有的数据写入新的sheet\n",
+    "        for row in range(row_count):\n",
+    "            for col in range(col_count):\n",
+    "                new_sheet.write(row, col, data[row][col])\n",
+    "\n",
+    "        if i == 0:\n",
+    "            # 在新的sheet中添加数据\n",
+    "            for col in range(col_count):\n",
+    "                new_sheet.write(row_count, col, append_rows[col])\n",
+    "\n",
+    "    # 保存新的xls文件\n",
+    "    new_workbook.save(\"定性模型数据项12-11.xls\")\n",
+    "\n",
+    "\n",
+    "def start_2(date):\n",
+    "    workbook = xlrd.open_workbook(read_file_path_name)\n",
+    "\n",
+    "\n",
+    "\n",
+    "    # 选择第一个表格\n",
+    "    sheet = workbook.sheet_by_index(0)\n",
+    "\n",
+    "    # 获取行数和列数\n",
+    "    num_rows = sheet.nrows\n",
+    "\n",
+    "\n",
+    "\n",
+    "    row_data = sheet.row_values(1)\n",
+    "    one_cols = row_data\n",
+    "\n",
+    "\n",
+    "    login_res = requests.post(url=login_url, json=login_data, timeout=(3, 5))\n",
+    "    text = json.loads(login_res.text)\n",
+    "    if text[\"status\"]:\n",
+    "        token = text[\"data\"][\"accessToken\"]\n",
+    "    else:\n",
+    "        print(\"获取认证失败\")\n",
+    "        token =  None\n",
+    "\n",
+    "\n",
+    "    now = date\n",
+    "    year = now.year\n",
+    "    month = now.month\n",
+    "    day = now.day\n",
+    "\n",
+    "    if month < 10:\n",
+    "        month = \"0\" + str(month)\n",
+    "    if day < 10:\n",
+    "        day = \"0\" + str(day)\n",
+    "    cur_time = str(year) + str(month) + str(day)\n",
+    "    cur_time2 = str(year) + \"-\" + str(month) + \"-\" + str(day)\n",
+    "    search_data = {\n",
+    "        \"data\": {\n",
+    "            \"date\": cur_time,\n",
+    "            \"dataItemNoList\": one_cols[1:]\n",
+    "        },\n",
+    "        \"funcModule\": \"数据项\",\n",
+    "        \"funcOperation\": \"查询\"\n",
+    "    }\n",
+    "    headers = {\"Authorization\": token}\n",
+    "    search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))\n",
+    "    search_value = json.loads(search_res.text)[\"data\"]\n",
+    "#     datas = search_value\n",
+    "    if search_value:\n",
+    "        datas = search_value\n",
+    "    else :\n",
+    "        datas = None\n",
+    "   \n",
+    "\n",
+    "    append_rows = [cur_time2]\n",
+    "    dataItemNo_dataValue = {}\n",
+    "#     for data_value in datas:\n",
+    "#         dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
+    "    for data_value in datas:\n",
+    "        if \"dataValue\" not in data_value:\n",
+    "            print(data_value)\n",
+    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n",
+    "        else:\n",
+    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
+    "    for value in one_cols[1:]:\n",
+    "        if value in dataItemNo_dataValue:\n",
+    "            append_rows.append(dataItemNo_dataValue[value])\n",
+    "        else:\n",
+    "            append_rows.append(\"\")\n",
+    "\n",
+    "    workbook = xlrd.open_workbook('定性模型数据项12-11.xls')\n",
+    "\n",
+    "    # 获取所有sheet的个数\n",
+    "    sheet_count = len(workbook.sheet_names())\n",
+    "\n",
+    "    # 获取所有sheet的名称\n",
+    "    sheet_names = workbook.sheet_names()\n",
+    "\n",
+    "    new_workbook = xlwt.Workbook()\n",
+    "    for i in range(sheet_count):\n",
+    "        # 获取当前sheet\n",
+    "        sheet = workbook.sheet_by_index(i)\n",
+    "\n",
+    "        # 获取sheet的行数和列数\n",
+    "        row_count = sheet.nrows\n",
+    "        col_count = sheet.ncols\n",
+    "        # 获取原有数据\n",
+    "        data = []\n",
+    "        for row in range(row_count):\n",
+    "            row_data = []\n",
+    "            for col in range(col_count):\n",
+    "                row_data.append(sheet.cell_value(row, col))\n",
+    "            data.append(row_data)\n",
+    "        # 创建xlwt的Workbook对象\n",
+    "        # 创建sheet\n",
+    "        new_sheet = new_workbook.add_sheet(sheet_names[i])\n",
+    "\n",
+    "        # 将原有的数据写入新的sheet\n",
+    "        for row in range(row_count):\n",
+    "            for col in range(col_count):\n",
+    "                new_sheet.write(row, col, data[row][col])\n",
+    "\n",
+    "        if i == 0:\n",
+    "            # 在新的sheet中添加数据\n",
+    "            for col in range(col_count):\n",
+    "                new_sheet.write(row_count, col, append_rows[col])\n",
+    "\n",
+    "    # 保存新的xls文件\n",
+    "    new_workbook.save(\"定性模型数据项12-11.xls\")\n",
+    "    print('关闭文件')\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    pass\n",
+    "    # 需要单独运行放开\n",
+    "    \n",
+    "     # start_1()\n",
+    "\n",
+    "    # 每天定时12点运行\n",
+    "    # while True:\n",
+    "    #     # 获取当前时间\n",
+    "    #     current_time = time.strftime(\"%H:%M:%S\", time.localtime())\n",
+    "    #     current_time_1 = time.strftime(\"%H:%M:%S\", time.localtime())\n",
+    "\n",
+    "    #     # 判断当前时间是否为执行任务的时间点\n",
+    "    #     if current_time == \"12:00:00\":\n",
+    "    #         print(\"执行定时任务\")\n",
+    "    #         start()\n",
+    "\n",
+    "    #     # 休眠1秒钟，避免过多占用CPU资源\n",
+    "    #         time.sleep(1)\n",
+    "        \n",
+    "    #     elif current_time_1 == \"20:00:00\":\n",
+    "    #         print(\"更新数据\")\n",
+    "    #         start_1()\n",
+    "    #     time.sleep(1)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20241031\n",
+      "20241101\n"
+     ]
+    }
+   ],
+   "source": [
+    "from datetime import datetime, timedelta\n",
+    "\n",
+    "start_date = datetime(2024, 10, 31)\n",
+    "end_date = datetime(2024, 11, 2)\n",
+    "\n",
+    "while start_date < end_date:\n",
+    "    print(start_date.strftime('%Y%m%d'))\n",
+    "    start(start_date)\n",
+    "    start_date += timedelta(days=1)\n",
+    "    \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

aisenzhecode/沥青/沥青定量价格预测每日推送-ytj.ipynb

@@ -0,0 +1,913 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "        <script type=\"text/javascript\">\n",
+       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
+       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
+       "        if (typeof require !== 'undefined') {\n",
+       "        require.undef(\"plotly\");\n",
+       "        requirejs.config({\n",
+       "            paths: {\n",
+       "                'plotly': ['https://cdn.plot.ly/plotly-2.12.1.min']\n",
+       "            }\n",
+       "        });\n",
+       "        require(['plotly'], function(Plotly) {\n",
+       "            window._Plotly = Plotly;\n",
+       "        });\n",
+       "        }\n",
+       "        </script>\n",
+       "        "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "import json\n",
+    "import xlrd\n",
+    "import xlwt\n",
+    "from datetime import datetime\n",
+    "import time\n",
+    "# 变量定义\n",
+    "login_url = \"http://10.200.32.39/jingbo-api/api/server/login\"\n",
+    "search_url = \"http://10.200.32.39/jingbo-api/api/warehouse/dwDataItem/queryByItemNos\"\n",
+    "\n",
+    "login_push_url = \"http://10.200.32.39/jingbo-api/api/server/login\"\n",
+    "upload_url = \"http://10.200.32.39/jingbo-api/api/dw/dataValue/pushDataValueList\"\n",
+    "\n",
+    "login_data = {\n",
+    "    \"data\": {\n",
+    "        \"account\": \"api_dev\",\n",
+    "        \"password\": \"ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=\",\n",
+    "        \"tenantHashCode\": \"8a4577dbd919675758d57999a1e891fe\",\n",
+    "        \"terminal\": \"API\"\n",
+    "    },\n",
+    "    \"funcModule\": \"API\",\n",
+    "    \"funcOperation\": \"获取token\"\n",
+    "}\n",
+    "\n",
+    "login_push_data = {\n",
+    "    \"data\": {\n",
+    "        \"account\": \"api_dev\",\n",
+    "        \"password\": \"ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=\",\n",
+    "        \"tenantHashCode\": \"8a4577dbd919675758d57999a1e891fe\",\n",
+    "        \"terminal\": \"API\"\n",
+    "    },\n",
+    "    \"funcModule\": \"API\",\n",
+    "    \"funcOperation\": \"获取token\"\n",
+    "}\n",
+    "\n",
+    "read_file_path_name = \"沥青数据项.xls\"\n",
+    "one_cols = []\n",
+    "two_cols = []\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sn\n",
+    "import random\n",
+    "import time\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "from plotly import __version__\n",
+    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
+    "\n",
+    "from sklearn import preprocessing\n",
+    "\n",
+    "from pandas import Series,DataFrame\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import sklearn.datasets as datasets\n",
+    "\n",
+    "#导入机器学习算法模型\n",
+    "from sklearn.linear_model import Lasso\n",
+    "from xgboost import XGBRegressor\n",
+    "\n",
+    "import datetime\n",
+    "import statsmodels.api as sm\n",
+    "# from keras.preprocessing.sequence import TimeseriesGenerator\n",
+    "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
+    "\n",
+    "import plotly.express as px\n",
+    "import plotly.graph_objects as go\n",
+    "\n",
+    "import xgboost as xgb\n",
+    "from xgboost import plot_importance, plot_tree\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "from statsmodels.tools.eval_measures import mse,rmse\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from xgboost import XGBRegressor\n",
+    "import warnings\n",
+    "import pickle\n",
+    "\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "\n",
+    "#切割训练数据和样本数据\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "#用于模型评分\n",
+    "from sklearn.metrics import r2_score\n",
+    "\n",
+    "le = preprocessing.LabelEncoder()\n",
+    "\n",
+    "# print(__version__) # requires version >= 1.9.0\n",
+    "\n",
+    "\n",
+    "import cufflinks as cf\n",
+    "cf.go_offline()\n",
+    "\n",
+    "random.seed(100)\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# 数据获取\n",
+    "\n",
+    "def get_head_auth():\n",
+    "    login_res = requests.post(url=login_url, json=login_data, timeout=(3, 5))\n",
+    "    text = json.loads(login_res.text)\n",
+    "    if text[\"status\"]:\n",
+    "        token = text[\"data\"][\"accessToken\"]\n",
+    "        return token\n",
+    "    else:\n",
+    "        print(\"获取认证失败\")\n",
+    "        return None\n",
+    "\n",
+    "\n",
+    "def get_data_value(token, dataItemNoList,date=''):\n",
+    "\n",
+    "    search_data = {\n",
+    "        \"data\": {\n",
+    "            \"date\": get_cur_time(date)[0],\n",
+    "            \"dataItemNoList\": dataItemNoList\n",
+    "        },\n",
+    "        \"funcModule\": \"数据项\",\n",
+    "        \"funcOperation\": \"查询\"\n",
+    "    }\n",
+    "    headers = {\"Authorization\": token}\n",
+    "    search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))\n",
+    "    search_value = json.loads(search_res.text)[\"data\"]\n",
+    "    if search_value:\n",
+    "        return search_value\n",
+    "    else:\n",
+    "        print(\"今天没有新数据\")\n",
+    "        return None\n",
+    "\n",
+    "\n",
+    "# xls文件处理\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "def write_xls(data,date):\n",
+    "    # 创建一个Workbook对象\n",
+    "    workbook = xlwt.Workbook()\n",
+    "\n",
+    "    # 创建一个Sheet对象，可指定名称\n",
+    "    sheet = workbook.load('Sheet1')\n",
+    "\n",
+    "    # 写入数据行\n",
+    "    for row_index, row_data in enumerate(data):\n",
+    "        for col_index, cell_data in enumerate(row_data):\n",
+    "            sheet.write(row_index, col_index, cell_data)\n",
+    "\n",
+    "    # 保存Workbook到文件\n",
+    "    workbook.save(get_cur_time(date)[0] + '.xls')\n",
+    "\n",
+    "\n",
+    "def get_cur_time(date = ''):\n",
+    "    if date == '':\n",
+    "        import datetime\n",
+    "        now = datetime.datetime.now()\n",
+    "    else:\n",
+    "        now = date\n",
+    "    year = now.year\n",
+    "    month = now.month\n",
+    "    day = now.day\n",
+    "\n",
+    "    if month < 10:\n",
+    "        month = \"0\" + str(month)\n",
+    "    if day < 10:\n",
+    "        day = \"0\" + str(day)\n",
+    "    cur_time = str(year) + str(month) + str(day)\n",
+    "    cur_time2 = str(year) + \"-\" + str(month) + \"-\" + str(day)\n",
+    "#     cur_time = '20231007'\n",
+    "#     cur_time2 = '2023-10-07'\n",
+    "    return cur_time, cur_time2\n",
+    "\n",
+    "\n",
+    "def get_head_push_auth():\n",
+    "    login_res = requests.post(url=login_push_url, json=login_push_data, timeout=(3, 5))\n",
+    "    text = json.loads(login_res.text)\n",
+    "    if text[\"status\"]:\n",
+    "        token = text[\"data\"][\"accessToken\"]\n",
+    "        return token\n",
+    "    else:\n",
+    "        print(\"获取认证失败\")\n",
+    "        return None\n",
+    "\n",
+    "\n",
+    "\n",
+    "def upload_data_to_system(token_push,date):\n",
+    "    data = {\n",
+    "        \"funcModule\": \"数据表信息列表\",\n",
+    "        \"funcOperation\": \"新增\",\n",
+    "        \"data\": [\n",
+    "            {\"dataItemNo\": \"C01100036|Forecast_Price|ACN\",\n",
+    "             \"dataDate\": get_cur_time(date)[0],\n",
+    "             \"dataStatus\": \"add\",\n",
+    "             \"dataValue\": forecast_price()\n",
+    "             }\n",
+    "\n",
+    "        ]\n",
+    "    }\n",
+    "    headers = {\"Authorization\": token_push}\n",
+    "    res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5))\n",
+    "    print(res.text)\n",
+    "\n",
+    "    \n",
+    "# def upload_data_to_system(token):\n",
+    "#     data = {\n",
+    "#         \"funcModule\": \"数据表信息列表\",\n",
+    "#         \"funcOperation\": \"新增\",\n",
+    "#         \"data\": [\n",
+    "#             {\"dataItemNo\": \"C01100036|Forecast_ Price|ACN\",\n",
+    "#              \"dataDate\": '20230706',\n",
+    "#              \"dataStatus\": \"add\",\n",
+    "#              \"dataValue\": 3780.0\n",
+    "#              }\n",
+    "\n",
+    "#         ]\n",
+    "#     }\n",
+    "#     headers = {\"Authorization\": token}\n",
+    "#     res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5))\n",
+    "#     print(res.text)\n",
+    "\n",
+    "def forecast_price():\n",
+    "    df_test = pd.read_excel('沥青数据项.xls',sheet_name='数据项历史数据')\n",
+    "    df_test.drop([0],inplace=True)\n",
+    "    df_test[['汽油执行价','柴油执行价','齐鲁石化销量','齐鲁石化产量','齐鲁石化成交价','齐鲁石化库存','科力达销量',\n",
+    "            '科力达产量','科力达成交价','科力达库存','弘润销量','弘润产量','弘润成交价','弘润库存','市场成交价','京博指导价',\n",
+    "            '布伦特上周收盘价','布伦特昨日收盘价','布伦特收盘价','上期所沥青主力合约','隆重资讯沥青日开工率','隆重资讯沥青月库存',\n",
+    "            '隆重资讯沥青月产量','隆重资讯沥青表观消费量','隆重资讯社会库存率','厂区库容','京博提货量','即期成本','异地库库存',\n",
+    "            '京博签单量','京博库存量','京博产量','加权平均成交价']] = df_test[['汽油执行价','柴油执行价','齐鲁石化销量',\n",
+    "            '齐鲁石化产量','齐鲁石化成交价','齐鲁石化库存','科力达销量',\n",
+    "            '科力达产量','科力达成交价','科力达库存','弘润销量','弘润产量','弘润成交价','弘润库存','市场成交价','京博指导价',\n",
+    "            '布伦特上周收盘价','布伦特昨日收盘价','布伦特收盘价','上期所沥青主力合约','隆重资讯沥青日开工率','隆重资讯沥青月库存',\n",
+    "            '隆重资讯沥青月产量','隆重资讯沥青表观消费量','隆重资讯社会库存率','厂区库容','京博提货量','即期成本','异地库库存',\n",
+    "            '京博签单量','京博库存量','京博产量','加权平均成交价']].astype('float')\n",
+    "    # df_test['日期']=pd.to_datetime(df_test['日期'],  format='%d/%m/%Y',infer_datetime_format=True)\n",
+    "    df_test['日期']=pd.to_datetime(df_test['日期'],  format='%Y-%m-%d',infer_datetime_format=True)\n",
+    "\n",
+    "    #查看每个特征缺失值数量\n",
+    "    MisVal_Check=df_test.isnull().sum().sort_values(ascending=False)\n",
+    "    #去掉缺失值百分比>0.4的特征，去掉这些特征后的新表格命名为df_test_1\n",
+    "    df_MisVal_Check = pd.DataFrame(MisVal_Check,)#\n",
+    "    df_MisVal_Check_1=df_MisVal_Check.reset_index()\n",
+    "    df_MisVal_Check_1.columns=['Variable_Name','Missing_Number'] \n",
+    "    df_MisVal_Check_1['Missing_Number']=df_MisVal_Check_1['Missing_Number']/len(df_test)\n",
+    "    df_test_1=df_test.drop(df_MisVal_Check_1[df_MisVal_Check_1['Missing_Number']>0.4].Variable_Name,axis = 1)\n",
+    "\n",
+    "    #将缺失值补为前一个或者后一个数值\n",
+    "    df_test_1=df_test_1.fillna(df_test.ffill())\n",
+    "    df_test_1=df_test_1.fillna(df_test_1.bfill())\n",
+    "\n",
+    "    # 选择用于模型训练的列名称\n",
+    "    col_for_training = df_test_1.columns\n",
+    "    import joblib\n",
+    "    Best_model_DalyLGPrice = joblib.load(\"日度价格预测_最佳模型.pkl\")\n",
+    "    # 最新的一天为最后一行的数据\n",
+    "    df_test_1_Day = df_test_1.tail(1)\n",
+    "    # 移除不需要的列\n",
+    "    df_test_1_Day.index = df_test_1_Day[\"日期\"]\n",
+    "    df_test_1_Day = df_test_1_Day.drop([\"日期\"], axis= 1)\n",
+    "    df_test_1_Day=df_test_1_Day.drop('京博指导价',axis=1)\n",
+    "    df_test_1_Day=df_test_1_Day.dropna()\n",
+    "\n",
+    "    # df_test_1_Day\n",
+    "    #预测今日价格，显示至小数点后两位\n",
+    "    Ypredict_Today=Best_model_DalyLGPrice.predict(df_test_1_Day)\n",
+    "\n",
+    "    df_test_1_Day['日度预测价格']=Ypredict_Today\n",
+    "    print(df_test_1_Day['日度预测价格'])\n",
+    "    a = df_test_1_Day['日度预测价格']\n",
+    "    a = a[0]\n",
+    "    a = float(a)\n",
+    "    a = round(a,2)\n",
+    "    return a\n",
+    "def optimize_Model():\n",
+    "    from sklearn.model_selection import train_test_split\n",
+    "    from sklearn.impute  import SimpleImputer\n",
+    "    from sklearn.preprocessing import OrdinalEncoder\n",
+    "    from sklearn.feature_selection import SelectFromModel\n",
+    "    from sklearn.metrics import mean_squared_error, r2_score\n",
+    "\n",
+    "    pd.set_option('display.max_rows',40)  \n",
+    "    pd.set_option('display.max_columns',40)  \n",
+    "    df_test = pd.read_excel('沥青数据项.xls',sheet_name='数据项历史数据')\n",
+    "    df_test.drop([0],inplace=True)\n",
+    "    df_test[['汽油执行价','柴油执行价','齐鲁石化销量','齐鲁石化产量','齐鲁石化成交价','齐鲁石化库存','科力达销量',\n",
+    "            '科力达产量','科力达成交价','科力达库存','弘润销量','弘润产量','弘润成交价','弘润库存','市场成交价','京博指导价',\n",
+    "            '布伦特上周收盘价','布伦特昨日收盘价','布伦特收盘价','上期所沥青主力合约','隆重资讯沥青日开工率','隆重资讯沥青月库存',\n",
+    "            '隆重资讯沥青月产量','隆重资讯沥青表观消费量','隆重资讯社会库存率','厂区库容','京博提货量','即期成本','异地库库存',\n",
+    "            '京博签单量','京博库存量','京博产量','加权平均成交价']] = df_test[['汽油执行价','柴油执行价','齐鲁石化销量','齐鲁石化产量','齐鲁石化成交价','齐鲁石化库存','科力达销量',\n",
+    "            '科力达产量','科力达成交价','科力达库存','弘润销量','弘润产量','弘润成交价','弘润库存','市场成交价','京博指导价',\n",
+    "            '布伦特上周收盘价','布伦特昨日收盘价','布伦特收盘价','上期所沥青主力合约','隆重资讯沥青日开工率','隆重资讯沥青月库存',\n",
+    "            '隆重资讯沥青月产量','隆重资讯沥青表观消费量','隆重资讯社会库存率','厂区库容','京博提货量','即期成本','异地库库存',\n",
+    "            '京博签单量','京博库存量','京博产量','加权平均成交价']].astype('float')\n",
+    "    # df_test = pd.read_csv('定价模型数据收集20190901-20230615.csv',encoding = 'gbk',engine = 'python')\n",
+    "    # df_test['日期']=pd.to_datetime(df_test['日期'],  format='%m/%d/%Y',infer_datetime_format=True)\n",
+    "    df_test['日期']=pd.to_datetime(df_test['日期'],  format='%Y-%m-%d',infer_datetime_format=True)\n",
+    "    # df_test.tail(3)\n",
+    "    MisVal_Check=df_test.isnull().sum().sort_values(ascending=False)\n",
+    "    #去掉缺失值百分比>0.4的特征，去掉这些特征后的新表格命名为df_test_1\n",
+    "    df_MisVal_Check = pd.DataFrame(MisVal_Check,)#\n",
+    "    df_MisVal_Check_1=df_MisVal_Check.reset_index()\n",
+    "    df_MisVal_Check_1.columns=['Variable_Name','Missing_Number'] \n",
+    "    df_MisVal_Check_1['Missing_Number']=df_MisVal_Check_1['Missing_Number']/len(df_test)\n",
+    "    df_test_1=df_test.drop(df_MisVal_Check_1[df_MisVal_Check_1['Missing_Number']>0.4].Variable_Name,axis = 1)\n",
+    "    #将缺失值补为前一个或者后一个数值\n",
+    "    df_test_1=df_test_1.fillna(df_test.ffill())\n",
+    "    df_test_1=df_test_1.fillna(df_test_1.bfill())\n",
+    "    df_test_1[\"日期\"] = pd.to_datetime(df_test_1[\"日期\"])\n",
+    "    df_test_1.index = df_test_1[\"日期\"]\n",
+    "    df_test_1 = df_test_1.drop([\"日期\"], axis= 1)\n",
+    "    dataset1=df_test_1.drop('京博指导价',axis=1)#.astype(float)\n",
+    "\n",
+    "    y=df_test_1['京博指导价']\n",
+    "\n",
+    "    x=dataset1 \n",
+    "\n",
+    "    train = x\n",
+    "    target = y\n",
+    "\n",
+    "    #切割数据样本集合测试集\n",
+    "    X_train,x_test,y_train,y_true = train_test_split(train,target,test_size=0.2,random_state=0)\n",
+    "    \n",
+    "    \n",
+    "    from sklearn.linear_model import Lasso\n",
+    "    from xgboost import XGBRegressor\n",
+    "\n",
+    "    from datetime import datetime\n",
+    "    import statsmodels.api as sm\n",
+    "    # from keras.preprocessing.sequence import TimeseriesGenerator\n",
+    "    from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
+    "\n",
+    "    import plotly.express as px\n",
+    "    import plotly.graph_objects as go\n",
+    "\n",
+    "    import xgboost as xgb\n",
+    "    from xgboost import plot_importance, plot_tree\n",
+    "    from sklearn.metrics import mean_absolute_error\n",
+    "    from statsmodels.tools.eval_measures import mse,rmse\n",
+    "    from sklearn.model_selection import GridSearchCV\n",
+    "    from xgboost import XGBRegressor\n",
+    "    import warnings\n",
+    "    import pickle\n",
+    "\n",
+    "    from sklearn.metrics import mean_squared_error\n",
+    "\n",
+    "    #切割训练数据和样本数据\n",
+    "    from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "    #用于模型评分\n",
+    "    from sklearn.metrics import r2_score\n",
+    "\n",
+    "    #模型缩写\n",
+    "    Lasso =  Lasso(random_state=0)\n",
+    "    XGBR = XGBRegressor(random_state=0)\n",
+    "    Lasso.fit(X_train,y_train)\n",
+    "    XGBR.fit(X_train,y_train)\n",
+    "    y_pre_Lasso = Lasso.predict(x_test)\n",
+    "    y_pre_XGBR = XGBR.predict(x_test)\n",
+    "\n",
+    "    #计算Lasso、XGBR、RandomForestR、AdaBoostR、GradientBoostingR、BaggingRegressor各模型的R²\n",
+    "    Lasso_score = r2_score(y_true,y_pre_Lasso)\n",
+    "    XGBR_score=r2_score(y_true,y_pre_XGBR)\n",
+    "\n",
+    "    #计算Lasso、XGBR的MSE和RMSE\n",
+    "    Lasso_MSE=mean_squared_error(y_true, y_pre_Lasso)\n",
+    "    XGBR_MSE=mean_squared_error(y_true, y_pre_XGBR)\n",
+    "\n",
+    "    Lasso_RMSE=np.sqrt(Lasso_MSE)\n",
+    "    XGBR_RMSE=np.sqrt(XGBR_MSE)\n",
+    "    model_results = pd.DataFrame([['Lasso', Lasso_RMSE, Lasso_score],\n",
+    "                                 ['XgBoost', XGBR_RMSE, XGBR_score]],\n",
+    "                   columns = ['模型(Model)','均方根误差(RMSE)', 'R^2 score'])\n",
+    "    model_results1=model_results.set_index('模型(Model)')\n",
+    "\n",
+    "    model_results1\n",
+    "    def plot_feature_importance(importance,names,model_type):\n",
+    "        feature_importance = np.array(importance)\n",
+    "        feature_names = np.array(names)\n",
+    "\n",
+    "        data={'feature_names':feature_names,'feature_importance':feature_importance}\n",
+    "        fi_df = pd.DataFrame(data)\n",
+    "\n",
+    "        fi_df.sort_values(by=['feature_importance'], ascending=False,inplace=True)\n",
+    "\n",
+    "        plt.figure(figsize=(10,8))\n",
+    "        sn.barplot(x=fi_df['feature_importance'], y=fi_df['feature_names'])\n",
+    "\n",
+    "        plt.title(model_type + \" \"+'FEATURE IMPORTANCE')\n",
+    "        plt.xlabel('FEATURE IMPORTANCE')\n",
+    "        plt.ylabel('FEATURE NAMES')\n",
+    "        from pylab import mpl\n",
+    "    %pylab\n",
+    "    mpl.rcParams['font.sans-serif'] = ['SimHei']\n",
+    "    from xgboost import XGBRegressor\n",
+    "    from sklearn.model_selection import GridSearchCV\n",
+    "\n",
+    "    estimator = XGBRegressor(random_state=0,\n",
+    "        nthread=4,\n",
+    "        seed=0\n",
+    "    )\n",
+    "    parameters = {\n",
+    "        'max_depth': range (2, 11, 2),     # 树的最大深度\n",
+    "        'n_estimators': range (50, 101, 10),  # 迭代次数\n",
+    "        'learning_rate': [0.01, 0.03, 0.1, 0.3, 0.5, 1]\n",
+    "    }\n",
+    "\n",
+    "    grid_search_XGB = GridSearchCV(\n",
+    "        estimator=estimator,\n",
+    "        param_grid=parameters,\n",
+    "    #     n_jobs = 10,\n",
+    "        cv = 3,\n",
+    "        verbose=True\n",
+    "    )\n",
+    "\n",
+    "    grid_search_XGB.fit(X_train, y_train)\n",
+    "    print(\"Best score: %0.3f\" % grid_search_XGB.best_score_)\n",
+    "    print(\"Best parameters set:\")\n",
+    "    best_parameters = grid_search_XGB.best_estimator_.get_params()\n",
+    "    for param_name in sorted(parameters.keys()):\n",
+    "        print(\"\\t%s: %r\" % (param_name, best_parameters[param_name]))\n",
+    "    y_pred = grid_search_XGB.predict(x_test)\n",
+    "\n",
+    "    op_XGBR_score = r2_score(y_true,y_pred)\n",
+    "    op_XGBR_MSE= mean_squared_error(y_true, y_pred)\n",
+    "    op_XGBR_RMSE= np.sqrt(op_XGBR_MSE)\n",
+    "\n",
+    "    model_results2 = pd.DataFrame([['Optimized_Xgboost', op_XGBR_RMSE, op_XGBR_score]],\n",
+    "                   columns = ['模型(Model)', '均方根误差(RMSE)', 'R^2 score'])\n",
+    "    model_results2=model_results2.set_index('模型(Model)')\n",
+    "\n",
+    "    # results = model_results1.append(model_results2, ignore_index = False)\n",
+    "    results = pd.concat([model_results1,model_results2],ignore_index=True)\n",
+    "    results\n",
+    "    import pickle\n",
+    "\n",
+    "    Pkl_Filename = \"日度价格预测_最佳模型.pkl\"  \n",
+    "\n",
+    "    with open(Pkl_Filename, 'wb') as file:  \n",
+    "       pickle.dump(grid_search_XGB, file)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "    \n",
+    "\n",
+    "\n",
+    "   \n",
+    "\n",
+    "def read_xls_data():\n",
+    "    global one_cols, two_cols\n",
+    "    # 打开 XLS 文件\n",
+    "    workbook = xlrd.open_workbook(read_file_path_name)\n",
+    "\n",
+    "    # 获取所有表格名称\n",
+    "    # sheet_names = workbook.sheet_names()\n",
+    "\n",
+    "    # 选择第一个表格\n",
+    "    sheet = workbook.sheet_by_index(0)\n",
+    "\n",
+    "    # 获取行数和列数\n",
+    "    num_rows = sheet.nrows\n",
+    "    # num_cols = sheet.ncols\n",
+    "\n",
+    "    # 遍历每一行，获取单元格数据\n",
+    "    # for i in range(num_rows):\n",
+    "    #     row_data = sheet.row_values(i)\n",
+    "    #     one_cols.append(row_data)\n",
+    "    #     two_cols.append(row_data[1])\n",
+    "\n",
+    "    row_data = sheet.row_values(1)\n",
+    "    one_cols = row_data\n",
+    "\n",
+    "    # 关闭 XLS 文件\n",
+    "    # workbook.close()\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "def start():\n",
+    "    '''预测上传数据'''\n",
+    "    read_xls_data()\n",
+    "\n",
+    "    token = get_head_auth()\n",
+    "    if not token:\n",
+    "        return\n",
+    "    token_push = get_head_push_auth()\n",
+    "    if not token_push:\n",
+    "        return\n",
+    "\n",
+    "    datas = get_data_value(token, one_cols[1:])\n",
+    "    if not datas:\n",
+    "        return\n",
+    "\n",
+    "    # data_list = [two_cols, one_cols]\n",
+    "    append_rows = [get_cur_time()[1]]\n",
+    "    dataItemNo_dataValue = {}\n",
+    "    for data_value in datas:\n",
+    "        if \"dataValue\" not in data_value:\n",
+    "            print(data_value)\n",
+    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n",
+    "        else:\n",
+    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
+    "       \n",
+    "    for value in one_cols[1:]:\n",
+    "        if value in dataItemNo_dataValue:\n",
+    "            append_rows.append(dataItemNo_dataValue[value])\n",
+    "        else:\n",
+    "            append_rows.append(\"\")\n",
+    "    save_xls(append_rows)\n",
+    "    optimize_Model()\n",
+    "    upload_data_to_system(token_push)\n",
+    "    # data_list.append(three_cols)\n",
+    "    # write_xls(data_list)\n",
+    "\n",
+    "\n",
+    "def start_3(date):\n",
+    "    '''预测上传数据'''\n",
+    "    read_xls_data()\n",
+    "\n",
+    "    token = get_head_auth()\n",
+    "    if not token:\n",
+    "        return\n",
+    "    token_push = get_head_push_auth()\n",
+    "    if not token_push:\n",
+    "        return\n",
+    "\n",
+    "    datas = get_data_value(token, one_cols[1:],date)\n",
+    "    if not datas:\n",
+    "        return\n",
+    "\n",
+    "    # data_list = [two_cols, one_cols]\n",
+    "    append_rows = [get_cur_time(date)[1]]\n",
+    "    dataItemNo_dataValue = {}\n",
+    "    for data_value in datas:\n",
+    "        if \"dataValue\" not in data_value:\n",
+    "            print(data_value)\n",
+    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n",
+    "        else:\n",
+    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
+    "       \n",
+    "    for value in one_cols[1:]:\n",
+    "        if value in dataItemNo_dataValue:\n",
+    "            append_rows.append(dataItemNo_dataValue[value])\n",
+    "        else:\n",
+    "            append_rows.append(\"\")\n",
+    "    save_xls(append_rows)\n",
+    "    optimize_Model()\n",
+    "    upload_data_to_system(token_push,date)\n",
+    "    # data_list.append(three_cols)\n",
+    "    # write_xls(data_list)\n",
+    "\n",
+    "\n",
+    "\n",
+    "def start_1():\n",
+    "    '''更新数据'''\n",
+    "    read_xls_data()\n",
+    "\n",
+    "    token = get_head_auth()\n",
+    "    if not token:\n",
+    "        return\n",
+    "    \n",
+    "\n",
+    "    datas = get_data_value(token, one_cols[1:])\n",
+    "    if not datas:\n",
+    "        return\n",
+    "\n",
+    "    # data_list = [two_cols, one_cols]\n",
+    "    append_rows = [get_cur_time()[1]]\n",
+    "    dataItemNo_dataValue = {}\n",
+    "    for data_value in datas:\n",
+    "        if \"dataValue\" not in data_value:\n",
+    "            print(data_value)\n",
+    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n",
+    "        else:\n",
+    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
+    "       \n",
+    "    for value in one_cols[1:]:\n",
+    "        if value in dataItemNo_dataValue:\n",
+    "            append_rows.append(dataItemNo_dataValue[value])\n",
+    "        else:\n",
+    "            append_rows.append(\"\")\n",
+    "    save_xls_1(append_rows)\n",
+    "\n",
+    "    \n",
+    "    # data_list.append(three_cols)\n",
+    "    # write_xls(data_list)\n",
+    "\n",
+    "\n",
+    "def start_2(date):\n",
+    "    '''更新数据'''\n",
+    "    read_xls_data()\n",
+    "\n",
+    "    token = get_head_auth()\n",
+    "    if not token:\n",
+    "        return\n",
+    "    \n",
+    "\n",
+    "    datas = get_data_value(token, one_cols[1:],date)\n",
+    "    if not datas:\n",
+    "        return\n",
+    "\n",
+    "    # data_list = [two_cols, one_cols]\n",
+    "    append_rows = [get_cur_time(date=date)[1]]\n",
+    "    dataItemNo_dataValue = {}\n",
+    "    for data_value in datas:\n",
+    "        if \"dataValue\" not in data_value:\n",
+    "            print(data_value)\n",
+    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n",
+    "        else:\n",
+    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
+    "       \n",
+    "    for value in one_cols[1:]:\n",
+    "        if value in dataItemNo_dataValue:\n",
+    "            append_rows.append(dataItemNo_dataValue[value])\n",
+    "        else:\n",
+    "            append_rows.append(\"\")\n",
+    "    print('新增数据：',append_rows)\n",
+    "    save_xls_1(append_rows)\n",
+    "\n",
+    "    \n",
+    "    # data_list.append(three_cols)\n",
+    "    # write_xls(data_list)\n",
+    "    \n",
+    "def save_xls_1(append_rows):\n",
+    "\n",
+    "    # 打开xls文件\n",
+    "    workbook = xlrd.open_workbook('沥青数据项.xls')\n",
+    "\n",
+    "    # 获取所有sheet的个数\n",
+    "    sheet_count = len(workbook.sheet_names())\n",
+    "\n",
+    "    # 获取所有sheet的名称\n",
+    "    sheet_names = workbook.sheet_names()\n",
+    "\n",
+    "    new_workbook = xlwt.Workbook()\n",
+    "    for i in range(sheet_count):\n",
+    "        # 获取当前sheet\n",
+    "        sheet = workbook.sheet_by_index(i)\n",
+    "\n",
+    "        # 获取sheet的行数和列数\n",
+    "        row_count = sheet.nrows - 1\n",
+    "        col_count = sheet.ncols\n",
+    "        # 获取原有数据\n",
+    "        data = []\n",
+    "        for row in range(row_count):\n",
+    "            row_data = []\n",
+    "            for col in range(col_count):\n",
+    "                row_data.append(sheet.cell_value(row, col))\n",
+    "            data.append(row_data)\n",
+    "        # 创建xlwt的Workbook对象\n",
+    "        # 创建sheet\n",
+    "        new_sheet = new_workbook.add_sheet(sheet_names[i])\n",
+    "\n",
+    "        # 将原有的数据写入新的sheet\n",
+    "        for row in range(row_count):\n",
+    "            for col in range(col_count):\n",
+    "                new_sheet.write(row, col, data[row][col])\n",
+    "\n",
+    "        if i == 0:\n",
+    "            # 在新的sheet中添加数据\n",
+    "            for col in range(col_count):\n",
+    "                new_sheet.write(row_count, col, append_rows[col])\n",
+    "\n",
+    "    # 保存新的xls文件\n",
+    "    new_workbook.save(\"沥青数据项.xls\")    \n",
+    "\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "def check_data(dataItemNo):\n",
+    "    token = get_head_auth()\n",
+    "    if not token:\n",
+    "        return\n",
+    "\n",
+    "    datas = get_data_value(token, dataItemNo)\n",
+    "    if not datas:\n",
+    "        return\n",
+    "\n",
+    "\n",
+    "def save_xls(append_rows):\n",
+    "\n",
+    "    # 打开xls文件\n",
+    "    workbook = xlrd.open_workbook('沥青数据项.xls')\n",
+    "\n",
+    "    # 获取所有sheet的个数\n",
+    "    sheet_count = len(workbook.sheet_names())\n",
+    "\n",
+    "    # 获取所有sheet的名称\n",
+    "    sheet_names = workbook.sheet_names()\n",
+    "\n",
+    "    new_workbook = xlwt.Workbook()\n",
+    "    for i in range(sheet_count):\n",
+    "        # 获取当前sheet\n",
+    "        sheet = workbook.sheet_by_index(i)\n",
+    "\n",
+    "        # 获取sheet的行数和列数\n",
+    "        row_count = sheet.nrows\n",
+    "        col_count = sheet.ncols\n",
+    "        # 获取原有数据\n",
+    "        data = []\n",
+    "        for row in range(row_count):\n",
+    "            row_data = []\n",
+    "            for col in range(col_count):\n",
+    "                row_data.append(sheet.cell_value(row, col))\n",
+    "            data.append(row_data)\n",
+    "        # 创建xlwt的Workbook对象\n",
+    "        # 创建sheet\n",
+    "        new_sheet = new_workbook.add_sheet(sheet_names[i])\n",
+    "\n",
+    "        # 将原有的数据写入新的sheet\n",
+    "        for row in range(row_count):\n",
+    "            for col in range(col_count):\n",
+    "                new_sheet.write(row, col, data[row][col])\n",
+    "\n",
+    "        if i == 0:\n",
+    "            # 在新的sheet中添加数据\n",
+    "            for col in range(col_count):\n",
+    "                new_sheet.write(row_count, col, append_rows[col])\n",
+    "\n",
+    "    # 保存新的xls文件\n",
+    "    new_workbook.save(\"沥青数据项.xls\")\n",
+    "\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    pass\n",
+    "    # 需要单独运行放开\n",
+    "#      start()\n",
+    "\n",
+    "    # 每天定时12点运行\n",
+    "    # while True:\n",
+    "    #     # 获取当前时间\n",
+    "    #     current_time = time.strftime(\"%H:%M:%S\", time.localtime())\n",
+    "    #     current_time_1 = time.strftime(\"%H:%M:%S\", time.localtime())\n",
+    "\n",
+    "    #     # 判断当前时间是否为执行任务的时间点\n",
+    "    #     if current_time == \"12:00:00\":\n",
+    "    #         print(\"执行定时任务\")\n",
+    "    #         start()\n",
+    "\n",
+    "    #     # 休眠1秒钟，避免过多占用CPU资源\n",
+    "    #         time.sleep(1)\n",
+    "        \n",
+    "    #     elif current_time_1 == \"20:00:00\":\n",
+    "    #         print(\"更新数据\")\n",
+    "    #         start_1()\n",
+    "    #     time.sleep(1)\n",
+    "\n",
+    "\n",
+    "#     # 检测数据准确性, 需要检测放开\n",
+    "#     # check_data(\"100028098|LISTING_PRICE\")\n",
+    "#     # check_data(\"9137070016544622XB|DAY_Yield\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20241025\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Hello\\AppData\\Local\\Temp\\ipykernel_6896\\1185823687.py:299: UserWarning:\n",
+      "\n",
+      "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using matplotlib backend: <object object at 0x000001DD05367030>\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: ['random', 'datetime', 'plot', '__version__']\n",
+      "`%matplotlib` prevents importing * from pylab and numpy\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best score: 0.997\n",
+      "Best parameters set:\n",
+      "\tlearning_rate: 0.1\n",
+      "\tmax_depth: 10\n",
+      "\tn_estimators: 100\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Hello\\AppData\\Local\\Temp\\ipykernel_6896\\1185823687.py:239: 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\\Hello\\AppData\\Local\\Temp\\ipykernel_6896\\1185823687.py:273: 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": [
+      "日期\n",
+      "2024-10-25    3538.790283\n",
+      "Name: 日度预测价格, dtype: float32\n",
+      "{\"confirmFlg\":false,\"status\":true}\n",
+      "新增数据： ['2024-10-25', '', '', 0.2, 0.0, 3660.0, 1.5, 0.1, 0.0, 3450.0, 7.9, 0.2, 0.2, 3450.0, 0.3, '', 3500.0, 75.46, 72.67, '', '', '', '', '', '', '', '', '', 3065.6453, '', '', '', '', '']\n"
+     ]
+    }
+   ],
+   "source": [
+    "from datetime import datetime, timedelta\n",
+    "\n",
+    "start_date = datetime(2024, 10, 25)\n",
+    "end_date = datetime(2024, 10, 26)\n",
+    "\n",
+    "while start_date < end_date:\n",
+    "    print(start_date.strftime('%Y%m%d'))\n",
+    "    start_3(start_date)\n",
+    "    time.sleep(1)\n",
+    "    start_2(start_date)\n",
+    "    start_date += timedelta(days=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}