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PriceForecast/aisenzhecode/沥青/沥青定性模型每日推送-ytj.ipynb
workpc f095c6fa84 2025年6月4日原油发版
2025-06-04 18:04:15 +08:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 32,
"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",
"pd.set_option('display.max_columns', None)\n",
"\n",
"import numpy as np\n",
"# 变量定义\n",
"login_url = \"http://10.200.32.39/jingbo-api/api/server/login\"\n",
"login_push_url = \"http://10.200.32.39/jingbo-api/api/server/login\"\n",
"# query_data_list_item_nos_url\n",
"search_url = \"http://10.200.32.39/jingbo-api/api/warehouse/dwDataItem/queryByItemNos\" #jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos\n",
"upload_url = \"http://10.200.32.39/jingbo-api/api/dw/dataValue/pushDataValueList\"\n",
"queryDataListItemNos_url = \"http://10.200.32.39/jingbo-api//api/warehouse/dwDataItem/queryDataListItemNos\"\n",
"\n",
"\n",
"query_data_list_item_nos_data = {\n",
" \"funcModule\": \"数据项\",\n",
" \"funcOperation\": \"查询\",\n",
" \"data\": {\n",
" \"dateStart\": \"20200101\",\n",
" \"dateEnd\": \"20241231\",\n",
" \"dataItemNoList\": [\"Brentzdj\", \"Brentzgj\"] # 数据项编码,代表 brent最低价和最高价\n",
" }\n",
"}\n",
"\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",
"\n",
"read_file_path_name = \"定性模型数据项12-11.xlsx\"\n",
"one_cols = []\n",
"two_cols = []\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_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",
"def update_e_value(file_path, column_index, threshold):\n",
" \"\"\"\n",
" 数据修正需求：2025年1月8日\n",
" 如果如果今天的成本即期价跟昨天的成本价差正负1000以上，就按照昨天的成本价计算\n",
"\n",
" 更新Excel文件中指定列的值，如果新值与前一天的值变化大于阈值，则将新值改为前一天的值。\n",
"\n",
" :param file_path: Excel文件路径\n",
" :param column_index: 需要更新的列索引\n",
" :param threshold: 变化阈值\n",
" \"\"\"\n",
" # 读取Excel文件\n",
" # try:\n",
" # df = pd.read_excel(file_path, engine='openpyxl')\n",
" # except:\n",
" # df = pd.read_excel(file_path, engine='xlrd')\n",
" \n",
" df = pd.read_excel(file_path)\n",
" # 所有列列统一数据格式为float\n",
" df = df.applymap(lambda x: float(x) if isinstance(x, (int, float)) else x)\n",
" \n",
" # print(df.tail())\n",
" # 填充缺失值\n",
" df = df.fillna(method='ffill')\n",
"\n",
" # 获取昨天，前天数据\n",
" df1 = df[-3:-1]\n",
" print(df1)\n",
" # 获取前一天的指定列值\n",
" previous_value = df1.iloc[0, column_index]\n",
" print('前一天的',previous_value,type(previous_value))\n",
" # 获取当前的指定列值\n",
" current_value = df1.iloc[1, column_index]\n",
" print('现在的',current_value,type(current_value))\n",
" # 判断指定列值的变化是否大于阈值\n",
" if abs(current_value - previous_value) > threshold:\n",
" # 如果变化大于阈值，将当前的指定列值改为前一天的值\n",
" df.iloc[-2, column_index] = previous_value\n",
" print('修改了')\n",
" # print(df.tail())\n",
" # 将修改后的数据写回Excel文件\n",
" df.to_excel(file_path, index=False,engine='openpyxl')\n",
"\n",
"def getLogToken():\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",
" return token\n",
"\n",
"def updateExcelDatabak(date='',token=None):\n",
" workbook = xlrd.open_workbook(read_file_path_name)\n",
"\n",
" # 选择第一个表格\n",
" sheet = workbook.sheet_by_index(0)\n",
"\n",
" row_data = sheet.row_values(1)\n",
" one_cols = row_data\n",
"\n",
" cur_time,cur_time2 = getNow(date)\n",
" search_data = {\n",
" \"data\": {\n",
" \"date\": cur_time,\n",
" \"dataItemNoList\": one_cols[1:]\n",
" },\n",
" \"funcModule\": \"数据项\",\n",
" \"funcOperation\": \"查询\"\n",
" }\n",
" headers = {\"Authorization\": token}\n",
" search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))\n",
" search_value = json.loads(search_res.text)[\"data\"]\n",
"# datas = search_value\n",
" if search_value:\n",
" datas = search_value\n",
" else :\n",
" datas = None\n",
" \n",
"\n",
" append_rows = [cur_time2]\n",
" dataItemNo_dataValue = {}\n",
"# for data_value in datas:\n",
"# dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
" for data_value in datas:\n",
" if \"dataValue\" not in data_value:\n",
" print(data_value)\n",
" dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n",
" else:\n",
" dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
" for value in one_cols[1:]:\n",
" if value in dataItemNo_dataValue:\n",
" append_rows.append(dataItemNo_dataValue[value])\n",
" else:\n",
" append_rows.append(\"\")\n",
"\n",
" workbook = xlrd.open_workbook('定性模型数据项12-11.xlsx')\n",
"\n",
" # 获取所有sheet的个数\n",
" sheet_count = len(workbook.sheet_names())\n",
"\n",
" # 获取所有sheet的名称\n",
" sheet_names = workbook.sheet_names()\n",
"\n",
" new_workbook = xlwt.Workbook()\n",
" for i in range(sheet_count):\n",
" # 获取当前sheet\n",
" sheet = workbook.sheet_by_index(i)\n",
"\n",
" # 获取sheet的行数和列数\n",
" row_count = sheet.nrows\n",
" col_count = sheet.ncols\n",
" # 获取原有数据\n",
" data = []\n",
" for row in range(row_count):\n",
" row_data = []\n",
" for col in range(col_count):\n",
" row_data.append(sheet.cell_value(row, col))\n",
" data.append(row_data)\n",
" # 创建xlwt的Workbook对象\n",
" # 创建sheet\n",
" new_sheet = new_workbook.add_sheet(sheet_names[i])\n",
"\n",
" # 将原有的数据写入新的sheet\n",
" for row in range(row_count):\n",
" for col in range(col_count):\n",
" new_sheet.write(row, col, data[row][col])\n",
"\n",
" if i == 0:\n",
" \n",
" # 在新的sheet中添加数据\n",
" for col in range(col_count):\n",
" new_sheet.write(row_count, col, append_rows[col])\n",
"\n",
" # 保存新的xls文件\n",
" new_workbook.save(\"定性模型数据项12-11.xlsx\")\n",
"\n",
"def updateYesterdayExcelData(date='', token=None):\n",
" # 使用pandas读取Excel文件\n",
" df = pd.read_excel(read_file_path_name, engine='openpyxl')\n",
"\n",
" # 获取第二行的数据作为列名\n",
" one_cols = df.iloc[0,:].tolist()\n",
"\n",
" # 获取当前日期的前一天\n",
" if date == '':\n",
" previous_date = (datetime.now() - timedelta(days=1)).strftime('%Y-%m-%d')\n",
" else:\n",
" # 字符串转日期\n",
" previous_date = (datetime.strptime(date, \"%Y-%m-%d\")-timedelta(days=1)).strftime('%Y-%m-%d')\n",
" \n",
"\n",
" cur_time, cur_time2 = getNow(previous_date)\n",
" search_data = {\n",
" \"data\": {\n",
" \"date\": cur_time,\n",
" \"dataItemNoList\": one_cols[1:]\n",
" },\n",
" \"funcModule\": \"数据项\",\n",
" \"funcOperation\": \"查询\"\n",
" }\n",
" headers = {\"Authorization\": token}\n",
" search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))\n",
" search_value = json.loads(search_res.text)[\"data\"]\n",
" if search_value:\n",
" datas = search_value\n",
" else:\n",
" datas = None\n",
"\n",
" append_rows = [cur_time2]\n",
" dataItemNo_dataValue = {}\n",
" for data_value in datas:\n",
" if \"dataValue\" not in data_value:\n",
" print(data_value)\n",
" dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n",
" else:\n",
" dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
" for value in one_cols[1:]:\n",
" if value in dataItemNo_dataValue:\n",
" append_rows.append(dataItemNo_dataValue[value])\n",
" else:\n",
" append_rows.append(\"\")\n",
"\n",
" print('更新数据前')\n",
" print(df.tail(1))\n",
" # 检查日期是否已存在于数据中\n",
" if previous_date not in df['日期'].values:\n",
" # 将新的数据添加到DataFrame中\n",
" new_row = pd.DataFrame([append_rows], columns=df.columns.tolist())\n",
" df = pd.concat([df, new_row], ignore_index=True)\n",
" else:\n",
" # 更新现有数据\n",
" print('日期存在，即将更新')\n",
" print('新数据',append_rows[1:])\n",
" df.loc[df['日期'] == previous_date, df.columns.tolist()[1:]] = append_rows[1:]\n",
"\n",
" print('更新数据后')\n",
" print(df.tail(1))\n",
" # 使用pandas保存Excel文件\n",
" df.to_excel(\"定性模型数据项12-11.xlsx\", index=False, engine='openpyxl')\n",
"\n",
"\n",
"def updateExcelData(date='', token=None):\n",
" # 使用pandas读取Excel文件\n",
" df = pd.read_excel(read_file_path_name, engine='openpyxl')\n",
"\n",
" # 获取第一行的数据作为列名\n",
" # one_cols = df.columns.tolist()\n",
" \n",
" # 获取第二行的数据作为列名\n",
" one_cols = df.iloc[0,:].tolist()\n",
"\n",
" cur_time, cur_time2 = getNow(date)\n",
" search_data = {\n",
" \"data\": {\n",
" \"date\": cur_time,\n",
" \"dataItemNoList\": one_cols[1:]\n",
" },\n",
" \"funcModule\": \"数据项\",\n",
" \"funcOperation\": \"查询\"\n",
" }\n",
" headers = {\"Authorization\": token}\n",
" search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))\n",
" search_value = json.loads(search_res.text)[\"data\"]\n",
" if search_value:\n",
" datas = search_value\n",
" else:\n",
" datas = None\n",
"\n",
" append_rows = [cur_time2]\n",
" dataItemNo_dataValue = {}\n",
" for data_value in datas:\n",
" if \"dataValue\" not in data_value:\n",
" print(data_value)\n",
" dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n",
" else:\n",
" dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
" for value in one_cols[1:]:\n",
" if value in dataItemNo_dataValue:\n",
" append_rows.append(dataItemNo_dataValue[value])\n",
" else:\n",
" append_rows.append(\"\")\n",
"\n",
" # 将新的数据添加到DataFrame中\n",
" new_row = pd.DataFrame([append_rows], columns=df.columns.tolist())\n",
" df = pd.concat([df, new_row], ignore_index=True)\n",
" # df = df.append(pd.Series(append_rows), ignore_index=True)\n",
"\n",
" # 使用pandas保存Excel文件\n",
" df.to_excel(\"定性模型数据项12-11.xlsx\", index=False, engine='openpyxl')\n",
"\n",
"\n",
"def qualitativeModel():\n",
" df = pd.read_excel('定性模型数据项12-11.xlsx')\n",
"\n",
" df=df.fillna(df.ffill())\n",
" df1 = df[-3:-1].reset_index()\n",
" print(df1)\n",
" '''\n",
" # if df1.loc[1,'70号沥青开工率'] > 0.3: \n",
" 2025年1月8日 修改：\n",
" 复盘分析后发现2024-7月开始，开工率数据从0.28 变为了28 ，改为下面的判断规则\n",
" '''\n",
" if df1.loc[1,'70号沥青开工率'] / 100 > 0.3:\n",
" a = -(df1.loc[1,'70号沥青开工率'] / 100 -0.2)*5/0.1\n",
" else :\n",
" a = 0\n",
" b = df1.loc[1,'资金因素']\n",
" \n",
" print('昨日计划提货偏差改之前',df1.loc[1,'昨日计划提货偏差'])\n",
" # 昨日计划提货偏差 = 京博产量 - 计划产量\n",
" df1.loc[1,'昨日计划提货偏差'] = df1.loc[1,'京博产量'] - df1.loc[1,'计划产量']\n",
" \n",
" print('昨日计划提货偏差改之后',df1.loc[1,'昨日计划提货偏差'])\n",
" if df1.loc[1,'昨日计划提货偏差']>0:\n",
" c = df1.loc[1,'昨日计划提货偏差']*10/2000\n",
" else :\n",
" c = df1.loc[1,'昨日计划提货偏差']*10/3000\n",
" \n",
" # 生产情况 = (京博产量 - 计划产量)/500*5\n",
" d = (df1.loc[1,'京博产量'] - df1.loc[1,'计划产量']) / 500 * 5\n",
" \n",
" if df1.loc[1,'基质沥青库存']/265007 >0.8:\n",
" e = (df1.loc[1,'基质沥青库存'] - df1.loc[0,'基质沥青库存'])*10/-5000\n",
" else : \n",
" e = 0\n",
"# f = df1.loc[1,'下游客户价格预期']\n",
" f = 1 # 2025年1月23日修改：价格预期都按1计算\n",
" if abs(df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])>=100:\n",
" g = (df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])*50/100\n",
" else :\n",
" g = 0\n",
" h = df1.loc[1,'订单结构']\n",
" x = round(0.08*a+0*b+0.15*c+0.08*d +0.03*e +0.08*f +0.4*g+0.18*h+df1.loc[0,'京博指导价'],2)\n",
" return x\n",
"\n",
"\n",
"def getNow(date='',offset=0):\n",
" if date == '':\n",
" now = datetime.now() - timedelta(days=offset)\n",
" else:\n",
" try:\n",
" date = datetime.strptime(date, \"%Y-%m-%d\")\n",
" except:\n",
" date = datetime.strptime(date, \"%Y%m%d\")\n",
" now = date\n",
"\n",
" year = now.year\n",
" month = now.month\n",
" day = now.day\n",
"\n",
" if month < 10:\n",
" month = \"0\" + str(month)\n",
" if day < 10:\n",
" day = \"0\" + str(day)\n",
" cur_time = str(year) + str(month) + str(day)\n",
" cur_time2 = str(year) + \"-\" + str(month) + \"-\" + str(day)\n",
" return cur_time,cur_time2\n",
"\n",
"def pushData(cur_time,x,token_push):\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",
" headers1 = {\"Authorization\": token_push}\n",
" res = requests.post(url=upload_url, headers=headers1, json=data1, timeout=(3, 5))\n",
" \n",
"def start_2(date='',token=None):\n",
" workbook = xlrd.open_workbook(read_file_path_name)\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",
" row_data = sheet.row_values(1)\n",
" one_cols = row_data\n",
"\n",
" cur_time,cur_time2 = getNow(date)\n",
" \n",
" \n",
" \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.xlsx')\n",
"\n",
" # 获取所有sheet的个数\n",
" sheet_count = len(workbook.sheet_names())\n",
"\n",
" # 获取所有sheet的名称\n",
" sheet_names = workbook.sheet_names()\n",
"\n",
" new_workbook = xlwt.Workbook()\n",
" for i in range(sheet_count):\n",
" # 获取当前sheet\n",
" sheet = workbook.sheet_by_index(i)\n",
"\n",
" # 获取sheet的行数和列数\n",
" row_count = sheet.nrows\n",
" col_count = sheet.ncols\n",
" # 获取原有数据\n",
" data = []\n",
" for row in range(row_count):\n",
" row_data = []\n",
" for col in range(col_count):\n",
" row_data.append(sheet.cell_value(row, col))\n",
" data.append(row_data)\n",
" # 创建xlwt的Workbook对象\n",
" # 创建sheet\n",
" new_sheet = new_workbook.add_sheet(sheet_names[i])\n",
"\n",
" # 将原有的数据写入新的sheet\n",
" for row in range(row_count):\n",
" for col in range(col_count):\n",
" new_sheet.write(row, col, data[row][col])\n",
"\n",
" if i == 0:\n",
" \n",
" # 在新的sheet中添加数据\n",
" for col in range(col_count):\n",
" new_sheet.write(row_count, col, append_rows[col])\n",
"\n",
" # 保存新的xls文件\n",
" new_workbook.save(\"定性模型数据项12-11.xlsx\")\n",
"\n",
" update_e_value('定性模型数据项12-11.xlsx', 8, 1000)\n",
"\n",
" df = pd.read_excel('定性模型数据项12-11.xlsx')\n",
"\n",
" df=df.fillna(df.ffill())\n",
" df1 = df[-2:].reset_index()\n",
" '''\n",
" # if df1.loc[1,'70号沥青开工率'] > 0.3: \n",
" 2025年1月8日 修改：\n",
" 复盘分析后发现2024-7月开始，开工率数据从0.28 变为了28 ，改为下面的判断规则\n",
" '''\n",
" if df1.loc[1,'70号沥青开工率'] > 30:\n",
" a = (df1.loc[1,'70号沥青开工率']-0.2)*5/0.1\n",
" else :\n",
" a = 0\n",
" b = df1.loc[1,'资金因素']\n",
" if df1.loc[1,'昨日计划提货偏差']>0:\n",
" c = df1.loc[1,'昨日计划提货偏差']*10/2000\n",
" else :\n",
" c = df1.loc[1,'昨日计划提货偏差']*10/3000\n",
" d = df1.loc[1,'生产情况']\n",
" if df1.loc[1,'基质沥青库存']/265007 >0.8:\n",
" e = (df1.loc[1,'基质沥青库存'] - df1.loc[0,'基质沥青库存'])*10/-5000\n",
" else : \n",
" e = 0\n",
"# f = df1.loc[1,'下游客户价格预期']\n",
" f = 1 # 2025年1月23日修改：价格预期都按1计算\n",
" if abs(df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])>=100:\n",
" g = (df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])*50/100\n",
" else :\n",
" g = 0\n",
" h = df1.loc[1,'订单结构']\n",
" x = round(0.08*a+0*b+0.15*c+0.08*d +0.03*e +0.08*f +0.4*g+0.18*h+df1.loc[0,'京博指导价'],2)\n",
"\n",
" 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",
"def start(now=None):\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 now is None:\n",
" now = datetime.now()\n",
" year = now.year\n",
" month = now.month\n",
" day = now.day\n",
"\n",
" if month < 10:\n",
" month = \"0\" + str(month)\n",
" if day < 10:\n",
" day = \"0\" + str(day)\n",
" cur_time = str(year) + str(month) + str(day)\n",
" cur_time2 = str(year) + \"-\" + str(month) + \"-\" + str(day)\n",
" search_data = {\n",
" \"data\": {\n",
" \"date\": cur_time,\n",
" \"dataItemNoList\": one_cols[1:]\n",
" },\n",
" \"funcModule\": \"数据项\",\n",
" \"funcOperation\": \"查询\"\n",
" }\n",
" headers = {\"Authorization\": token}\n",
" search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))\n",
" search_value = json.loads(search_res.text)[\"data\"]\n",
"# datas = search_value\n",
" if search_value:\n",
" datas = search_value\n",
" else :\n",
" datas = None\n",
" \n",
"\n",
" append_rows = [cur_time2]\n",
" dataItemNo_dataValue = {}\n",
"# for data_value in datas:\n",
"# dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
" for data_value in datas:\n",
" if \"dataValue\" not in data_value:\n",
" print(data_value)\n",
" dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n",
" else:\n",
" dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
" for value in one_cols[1:]:\n",
" if value in dataItemNo_dataValue:\n",
" append_rows.append(dataItemNo_dataValue[value])\n",
" else:\n",
" append_rows.append(\"\")\n",
"\n",
" workbook = xlrd.open_workbook('定性模型数据项12-11.xlsx')\n",
"\n",
" # 获取所有sheet的个数\n",
" sheet_count = len(workbook.sheet_names())\n",
"\n",
" # 获取所有sheet的名称\n",
" sheet_names = workbook.sheet_names()\n",
"\n",
" new_workbook = xlwt.Workbook()\n",
" for i in range(sheet_count):\n",
" # 获取当前sheet\n",
" sheet = workbook.sheet_by_index(i)\n",
"\n",
" # 获取sheet的行数和列数\n",
" row_count = sheet.nrows\n",
" col_count = sheet.ncols\n",
" # 获取原有数据\n",
" data = []\n",
" for row in range(row_count):\n",
" row_data = []\n",
" for col in range(col_count):\n",
" row_data.append(sheet.cell_value(row, col))\n",
" data.append(row_data)\n",
" # 创建xlwt的Workbook对象\n",
" # 创建sheet\n",
" new_sheet = new_workbook.add_sheet(sheet_names[i])\n",
"\n",
" # 将原有的数据写入新的sheet\n",
" for row in range(row_count):\n",
" for col in range(col_count):\n",
" new_sheet.write(row, col, data[row][col])\n",
"\n",
" if i == 0:\n",
" # 在新的sheet中添加数据\n",
" for col in range(col_count):\n",
" new_sheet.write(row_count, col, append_rows[col])\n",
"\n",
" # 保存新的xls文件\n",
" new_workbook.save(\"定性模型数据项12-11.xlsx\")\n",
" \n",
" \n",
" update_e_value('定性模型数据项12-11.xlsx', 8, 1000)\n",
"\n",
" df = pd.read_excel('定性模型数据项12-11.xlsx')\n",
" df=df.fillna(df.ffill())\n",
" df1 = df[-2:].reset_index()\n",
" print(df1)\n",
" # if df1.loc[1,'70号沥青开工率'] > 0.3: -- 2025年1月9日 发版更改\n",
" if df1.loc[1,'70号沥青开工率'] / 100 > 0.3:\n",
" a = (df1.loc[1,'70号沥青开工率'] / 100 -0.2)*5/0.1\n",
" else :\n",
" a = 0\n",
" b = df1.loc[1,'资金因素']\n",
" if df1.loc[1,'昨日计划提货偏差']>0:\n",
" c = df1.loc[1,'昨日计划提货偏差']*10/2000\n",
" else :\n",
" c = df1.loc[1,'昨日计划提货偏差']*10/3000\n",
" d = df1.loc[1,'生产情况']\n",
" if df1.loc[1,'基质沥青库存']/265007 >0.8:\n",
" e = (df1.loc[1,'基质沥青库存'] - df1.loc[0,'基质沥青库存'])*10/-5000\n",
" else : \n",
" e = 0\n",
"# f = df1.loc[1,'下游客户价格预期']\n",
" f = 1 # 2025年1月23日修改：价格预期都按1计算\n",
" if abs(df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])>=100:\n",
" g = (df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])*50/100\n",
" else :\n",
" g = 0\n",
" h = df1.loc[1,'订单结构']\n",
" x = round(0.08*a+0*b+0.15*c+0.08*d +0.03*e +0.08*f +0.4*g+0.18*h+df1.loc[0,'京博指导价'],2)\n",
" \n",
"\n",
" # login_res1 = requests.post(url=login_url, json=login_data, timeout=(3, 30))\n",
" # text1 = json.loads(login_res1.text)\n",
" # token_push = text1[\"data\"][\"accessToken\"]\n",
"\n",
"\n",
" # data1 = {\n",
" # \"funcModule\": \"数据表信息列表\",\n",
" # \"funcOperation\": \"新增\",\n",
" # \"data\": [\n",
" # {\"dataItemNo\": \"C01100036|Forecast_Price|DX|ACN\",\n",
" # \"dataDate\": cur_time,\n",
" # \"dataStatus\": \"add\",\n",
" # \"dataValue\": x\n",
" # }\n",
"\n",
" # ]\n",
" # }\n",
" # headers1 = {\"Authorization\": token_push}\n",
" # res = requests.post(url=upload_url, headers=headers1, json=data1, timeout=(3, 5))\n",
" \n",
"\n",
"\n",
"def start_test():\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()\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.xlsx')\n",
"\n",
" # 获取所有sheet的个数\n",
" sheet_count = len(workbook.sheet_names())\n",
"\n",
" # 获取所有sheet的名称\n",
" sheet_names = workbook.sheet_names()\n",
"\n",
" new_workbook = xlwt.Workbook()\n",
" for i in range(sheet_count):\n",
" # 获取当前sheet\n",
" sheet = workbook.sheet_by_index(i)\n",
"\n",
" # 获取sheet的行数和列数\n",
" row_count = sheet.nrows\n",
" col_count = sheet.ncols\n",
" # 获取原有数据\n",
" data = []\n",
" for row in range(row_count):\n",
" row_data = []\n",
" for col in range(col_count):\n",
" row_data.append(sheet.cell_value(row, col))\n",
" data.append(row_data)\n",
" # 创建xlwt的Workbook对象\n",
" # 创建sheet\n",
" new_sheet = new_workbook.add_sheet(sheet_names[i])\n",
"\n",
" # 将原有的数据写入新的sheet\n",
" for row in range(row_count):\n",
" for col in range(col_count):\n",
" new_sheet.write(row, col, data[row][col])\n",
"\n",
" if i == 0:\n",
" # 在新的sheet中添加数据\n",
" for col in range(col_count):\n",
" new_sheet.write(row_count, col, append_rows[col])\n",
"\n",
" # 保存新的xls文件\n",
" new_workbook.save(\"定性模型数据项12-11.xlsx\")\n",
" \n",
" update_e_value('定性模型数据项12-11.xlsx', 8, 1000)\n",
"\n",
" df = pd.read_excel('定性模型数据项12-11.xlsx')\n",
" df=df.fillna(df.ffill())\n",
" df1 = df[-2:].reset_index()\n",
" # if df1.loc[1,'70号沥青开工率'] > 0.3: -- 2025年1月9日 发版更改\n",
" if df1.loc[1,'70号沥青开工率'] / 100 > 0.3:\n",
" a = (df1.loc[1,'70号沥青开工率'] / 100 -0.2)*5/0.1\n",
" else :\n",
" a = 0\n",
" b = df1.loc[1,'资金因素']\n",
" if df1.loc[1,'昨日计划提货偏差']>0:\n",
" c = df1.loc[1,'昨日计划提货偏差']*10/2000\n",
" else :\n",
" c = df1.loc[1,'昨日计划提货偏差']*10/3000\n",
" d = df1.loc[1,'生产情况']\n",
" if df1.loc[1,'基质沥青库存']/265007 >0.8:\n",
" e = (df1.loc[1,'基质沥青库存'] - df1.loc[0,'基质沥青库存'])*10/-5000\n",
" else : \n",
" e = 0\n",
"# f = df1.loc[1,'下游客户价格预期']\n",
" f = 1 # 2025年1月23日修改：价格预期都按1计算\n",
" if abs(df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])>=100:\n",
" g = (df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])*50/100\n",
" else :\n",
" g = 0\n",
" h = df1.loc[1,'订单结构']\n",
" x = round(0.08*a+0*b+0.15*c+0.08*d +0.03*e +0.08*f +0.4*g+0.18*h+df1.loc[0,'京博指导价'],2)\n",
" \n",
"\n",
" # login_res1 = requests.post(url=login_url, json=login_data, timeout=(3, 30))\n",
" # text1 = json.loads(login_res1.text)\n",
" # token_push = text1[\"data\"][\"accessToken\"]\n",
"\n",
"\n",
" # data1 = {\n",
" # \"funcModule\": \"数据表信息列表\",\n",
" # \"funcOperation\": \"新增\",\n",
" # \"data\": [\n",
" # {\"dataItemNo\": \"C01100036|Forecast_Price|DX|ACN\",\n",
" # \"dataDate\": cur_time,\n",
" # \"dataStatus\": \"add\",\n",
" # \"dataValue\": x\n",
" # }\n",
"\n",
" # ]\n",
" # }\n",
" # headers1 = {\"Authorization\": token_push}\n",
" # res = requests.post(url=upload_url, headers=headers1, json=data1, timeout=(3, 5))\n",
" \n",
"\n",
"\n",
"\n",
"\n",
"def start_1():\n",
" workbook = xlrd.open_workbook(read_file_path_name)\n",
"\n",
"\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.xlsx')\n",
"\n",
" # 获取所有sheet的个数\n",
" sheet_count = len(workbook.sheet_names())\n",
"\n",
" # 获取所有sheet的名称\n",
" sheet_names = workbook.sheet_names()\n",
"\n",
" new_workbook = xlwt.Workbook()\n",
" for i in range(sheet_count):\n",
" # 获取当前sheet\n",
" sheet = workbook.sheet_by_index(i)\n",
"\n",
" # 获取sheet的行数和列数\n",
" row_count = sheet.nrows - 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.xlsx\")\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"def get_queryDataListItemNos_value(token, url, dataItemNoList, dateStart, dateEnd):\n",
"\n",
" search_data = {\n",
" \"funcModule\": \"数据项\",\n",
" \"funcOperation\": \"查询\",\n",
" \"data\": {\n",
" \"dateStart\": dateStart,\n",
" \"dateEnd\": dateEnd,\n",
" \"dataItemNoList\": dataItemNoList # 数据项编码,代表 brent最低价和最高价\n",
" }\n",
" }\n",
"\n",
" headers = {\"Authorization\": token}\n",
" search_res = requests.post(url=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",
" return None\n",
"\n",
"\n",
"\n",
"def save_queryDataListItemNos_xls(data_df,dataItemNoList):\n",
"\n",
" current_year_month = datetime.now().strftime('%Y-%m')\n",
" grouped = data_df.groupby(\"dataDate\")\n",
"\n",
"\n",
" df_old = pd.read_excel('定性模型数据项12-11.xlsx')\n",
" df_old0 = df_old[:1]\n",
" result_dict = {df_old0.iloc[0][col] : col for col in df_old0.columns}\n",
" df_old1 = df_old[1:].copy()\n",
"\n",
" df_old1[\"日期\"] = pd.to_datetime(df_old1[\"日期\"])\n",
" # 删除日期列为本月的数据\n",
" df_old1 = df_old1[~df_old1[\"日期\"].dt.strftime('%Y-%m').eq(current_year_month)]\n",
" df_old1[\"日期\"] = df_old1[\"日期\"].dt.strftime('%Y-%m-%d')\n",
"\n",
"\n",
" list_data = []\n",
" for date, group in grouped:\n",
" dict_data = {\"日期\": date}\n",
" for index, row in group.iterrows():\n",
" dict_data[result_dict[row['dataItemNo']]] = row['dataValue']\n",
" list_data.append(dict_data)\n",
"\n",
" df_current_year_month = pd.DataFrame(list_data)\n",
" df_current_year_month\n",
"\n",
" df_merged = pd.concat([df_old0, df_old1, df_current_year_month], ignore_index=True)\n",
"\n",
" df_merged.to_excel('定性模型数据项12-11.xlsx', index=False)\n",
"\n",
"\n",
"\n",
"def queryDataListItemNos(date=None,token=None):\n",
" df = pd.read_excel('定性模型数据项12-11.xlsx')\n",
" dataItemNoList = df.iloc[0].tolist()[1:]\n",
"\n",
" if token is None:\n",
" token = getLogToken()\n",
" if token is None:\n",
" print(\"获取token失败\")\n",
" return\n",
"\n",
" # 获取当前日期\n",
" if date is None:\n",
" date = datetime.now()\n",
" current_date = date\n",
"\n",
" # 获取当月1日\n",
" first_day_of_month = current_date.replace(day=1)\n",
"\n",
" # 格式化为 YYYYMMDD 格式\n",
" dateEnd = current_date.strftime('%Y%m%d')\n",
" dateStart = first_day_of_month.strftime('%Y%m%d')\n",
"\n",
" search_value = get_queryDataListItemNos_value(token, queryDataListItemNos_url, dataItemNoList, dateStart, dateEnd)\n",
" # print(\"search_value\",search_value)\n",
"\n",
"\n",
" data_df = pd.DataFrame(search_value)\n",
"\n",
" data_df[\"dataDate\"] = pd.to_datetime(data_df[\"dataDate\"])\n",
" data_df[\"dataDate\"] = data_df[\"dataDate\"].dt.strftime('%Y-%m-%d')\n",
" save_queryDataListItemNos_xls(data_df,dataItemNoList)\n",
"\n",
"def main(start_date=None,token=None,token_push=None):\n",
" if start_date is None:\n",
" start_date = datetime.now()\n",
" if token is None:\n",
" token = get_head_auth()\n",
" if token_push is None:\n",
" token_push = get_head_push_auth()\n",
" date = start_date.strftime('%Y%m%d')\n",
" print(date)\n",
" updateExcelData(date,token)\n",
" queryDataListItemNos(token=token)\n",
" update_e_value('定性模型数据项12-11.xlsx', 8, 1000)\n",
" x = qualitativeModel()\n",
" print('**************************************************预测结果：',x)\n",
" cur_time,cur_time2 = getNow(date)\n",
" pushData(cur_time,x,token)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"### 原始代码备份\n",
"\n",
"if __name__ == \"__main__\":\n",
" pass\n",
"\n",
" # 需要单独运行放开\n",
" # start_1()\n",
" # start()\n",
"\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",
"\n",
"\n",
"\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)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"运行中...\n",
"更新前一天数据\n",
"更新数据前\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"1428 2025-05-06 3650 29.7819 1 -798.61 6000 90799.9 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"1428 2934.18 1 4876.81 7364.99 \n",
"日期存在，即将更新\n",
"新数据 [3650.0, '', '', -798.61, '', 90799.9179153, '', 2752.8654, '', '', 7364.987]\n",
"更新数据后\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"1428 2025-05-06 3650 29.7819 1 -798.61 6000 90799.9 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"1428 2934.18 1 4876.81 7364.99 \n",
"20250507\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"1427 2025-05-05 3650 29.3624 1 -798.61 6000 90799.9 3630 \n",
"1428 2025-05-06 3650 32.2987 1 -454.19 6000 94300.4 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"1427 2752.87 1 4876.81 7364.99 \n",
"1428 2934.18 1 4876.81 6793.88 \n",
"前一天的 2752.8654 <class 'float'>\n",
"现在的 2934.179 <class 'float'>\n",
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"0 1427 2025-05-05 3650 29.3624 1 -798.61 6000 90799.9 3630 \n",
"1 1428 2025-05-06 3650 32.2987 1 -454.19 6000 94300.4 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"0 2752.87 1 4876.81 7364.99 \n",
"1 2934.18 1 4876.81 6793.88 \n",
"昨日计划提货偏差改之前 -454.19\n",
"昨日计划提货偏差改之后 1917.0684\n",
"**************************************************预测结果： 3689.0\n",
"更新前一天数据\n",
"更新数据前\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"1429 2025-05-07 3650 32.2987 1 -454.19 6000 94300.4 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"1429 3036.84 1 4876.81 6793.88 \n",
"日期存在，即将更新\n",
"新数据 [3650.0, 32.2987, '', -454.19, '', 94300.352488, '', 2934.179, '', '', 6793.8778]\n",
"更新数据后\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"1429 2025-05-07 3650 32.2987 1 -454.19 6000 94300.4 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"1429 3036.84 1 4876.81 6793.88 \n",
"20250508\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"1428 2025-05-06 3650 32.2987 1 -454.19 6000 94300.4 3630 \n",
"1429 2025-05-07 3600 33.9765 1 693.59 6000 97550.8 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"1428 2934.18 1 4876.81 6793.88 \n",
"1429 3036.84 1 4876.81 7237.56 \n",
"前一天的 2934.179 <class 'float'>\n",
"现在的 3036.8392 <class 'float'>\n",
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"0 1428 2025-05-06 3650 32.2987 1 -454.19 6000 94300.4 3630 \n",
"1 1429 2025-05-07 3600 33.9765 1 693.59 6000 97550.8 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"0 2934.18 1 4876.81 6793.88 \n",
"1 3036.84 1 4876.81 7237.56 \n",
"昨日计划提货偏差改之前 693.59\n",
"昨日计划提货偏差改之后 2360.7506000000003\n",
"**************************************************预测结果： 3673.89\n",
"更新前一天数据\n",
"更新数据前\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"1430 2025-05-08 3600 33.9765 1 693.59 6000 97550.8 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"1430 2940.67 1 4876.81 7237.56 \n",
"日期存在，即将更新\n",
"新数据 [3600.0, 33.9765, '', 693.59, '', 97550.7881218, '', 3036.8392, '', '', 7237.56]\n",
"更新数据后\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"1430 2025-05-08 3600 33.9765 1 693.59 6000 97550.8 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"1430 2940.67 1 4876.81 7237.56 \n",
"20250509\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"1429 2025-05-07 3600 33.9765 1 693.59 6000 97550.8 3630 \n",
"1430 2025-05-08 3600 34.396 1 329.51 6000 100086 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"1429 3036.84 1 4876.81 7237.56 \n",
"1430 2940.67 1 4876.81 7711.98 \n",
"前一天的 3036.8392 <class 'float'>\n",
"现在的 2940.6669 <class 'float'>\n",
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"0 1429 2025-05-07 3600 33.9765 1 693.59 6000 97550.8 3630 \n",
"1 1430 2025-05-08 3600 34.396 1 329.51 6000 100086 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"0 3036.84 1 4876.81 7237.56 \n",
"1 2940.67 1 4876.81 7711.98 \n",
"昨日计划提货偏差改之前 329.51\n",
"昨日计划提货偏差改之后 2835.1686\n",
"**************************************************预测结果： 3604.08\n",
"更新前一天数据\n",
"更新数据前\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1431 2025-05-09 3600 34.396 1 329.51 6000 100086 3630 3077.15 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1431 1 4876.81 7711.98 \n",
"日期存在，即将更新\n",
"新数据 [3600.0, 34.396, '', 329.51, '', 100085.7741619, '', 2940.6669, '', '', 7711.978]\n",
"更新数据后\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1431 2025-05-09 3600 34.396 1 329.51 6000 100086 3630 3077.15 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1431 1 4876.81 7711.98 \n",
"20250510\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1430 2025-05-08 3600 34.396 1 329.51 6000 100086 3630 2940.67 \n",
"1431 2025-05-09 3620 36.9128 1 -1636.9 6000 101412 3630 3077.15 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1430 1 4876.81 7711.98 \n",
"1431 1 4876.81 7246.98 \n",
"前一天的 2940.6669 <class 'float'>\n",
"现在的 3077.1512 <class 'float'>\n",
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"0 1430 2025-05-08 3600 34.396 1 329.51 6000 100086 3630 \n",
"1 1431 2025-05-09 3620 36.9128 1 -1636.9 6000 101412 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"0 2940.67 1 4876.81 7711.98 \n",
"1 3077.15 1 4876.81 7246.98 \n",
"昨日计划提货偏差改之前 -1636.9\n",
"昨日计划提货偏差改之后 2370.1705999999995\n",
"**************************************************预测结果： 3630.55\n",
"更新前一天数据\n",
"更新数据前\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1432 2025-05-10 3620 36.9128 1 -1636.9 6000 101412 3630 3152.87 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1432 1 4876.81 7246.98 \n",
"日期存在，即将更新\n",
"新数据 [3620.0, 36.9128, '', -1636.9, '', 101412.2754832, '', 3077.1512, '', '', 7246.98]\n",
"更新数据后\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1432 2025-05-10 3620 36.9128 1 -1636.9 6000 101412 3630 3152.87 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1432 1 4876.81 7246.98 \n",
"20250511\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1431 2025-05-09 3620 36.9128 1 -1636.9 6000 101412 3630 3077.15 \n",
"1432 2025-05-10 3620 36.9128 1 3087.72 6000 104559 3630 3152.87 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1431 1 4876.81 7246.98 \n",
"1432 1 4876.81 7347.37 \n",
"前一天的 3077.1512 <class 'float'>\n",
"现在的 3152.8717 <class 'float'>\n",
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"0 1431 2025-05-09 3620 36.9128 1 -1636.9 6000 101412 3630 \n",
"1 1432 2025-05-10 3620 36.9128 1 3087.72 6000 104559 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"0 3077.15 1 4876.81 7246.98 \n",
"1 3152.87 1 4876.81 7347.37 \n",
"昨日计划提货偏差改之前 3087.72\n",
"昨日计划提货偏差改之后 2470.5621\n",
"**************************************************预测结果： 3623.41\n",
"更新前一天数据\n",
"更新数据前\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1433 2025-05-11 3620 36.9128 1 3087.72 6000 104559 3630 3151.4 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1433 1 4876.81 7347.37 \n",
"日期存在，即将更新\n",
"新数据 [3620.0, '', '', 3087.72, '', 104559.3953618, '', 3152.8717, '', '', 7347.3715]\n",
"更新数据后\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1433 2025-05-11 3620 36.9128 1 3087.72 6000 104559 3630 3151.4 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1433 1 4876.81 7347.37 \n",
"20250512\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1432 2025-05-10 3620 36.9128 1 3087.72 6000 104559 3630 3152.87 \n",
"1433 2025-05-11 3620 36.9128 1 2942.36 6000 106829 3630 3151.4 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1432 1 4876.81 7347.37 \n",
"1433 1 4876.81 7365.94 \n",
"前一天的 3152.8717 <class 'float'>\n",
"现在的 3151.3966 <class 'float'>\n",
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"0 1432 2025-05-10 3620 36.9128 1 3087.72 6000 104559 3630 \n",
"1 1433 2025-05-11 3620 36.9128 1 2942.36 6000 106829 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"0 3152.87 1 4876.81 7347.37 \n",
"1 3151.4 1 4876.81 7365.94 \n",
"昨日计划提货偏差改之前 2942.36\n",
"昨日计划提货偏差改之后 2489.1256000000003\n",
"**************************************************预测结果： 3623.44\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"更新前一天数据\n",
"更新数据前\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1434 2025-05-12 3620 36.9128 1 2942.36 6000 106829 3630 3171.09 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1434 1 4876.81 7365.94 \n",
"日期存在，即将更新\n",
"新数据 [3620.0, '', '', 2942.36, '', 106829.1756043, '', 3151.3966, '', '', 7365.935]\n",
"更新数据后\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1434 2025-05-12 3620 36.9128 1 2942.36 6000 106829 3630 3171.09 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1434 1 4876.81 7365.94 \n",
"20250513\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1433 2025-05-11 3620 36.9128 1 2942.36 6000 106829 3630 3151.4 \n",
"1434 2025-05-12 3620 34.396 1 251.7 6000 106775 3630 3171.09 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1433 1 4876.81 7365.94 \n",
"1434 1 4876.81 7267.18 \n",
"前一天的 3151.3966 <class 'float'>\n",
"现在的 3171.0934 <class 'float'>\n",
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"0 1433 2025-05-11 3620 36.9128 1 2942.36 6000 106829 3630 \n",
"1 1434 2025-05-12 3620 34.396 1 251.7 6000 106775 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"0 3151.4 1 4876.81 7365.94 \n",
"1 3171.09 1 4876.81 7267.18 \n",
"昨日计划提货偏差改之前 251.7\n",
"昨日计划提货偏差改之后 2390.3720999999996\n",
"**************************************************预测结果： 3623.39\n",
"更新前一天数据\n",
"更新数据前\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1435 2025-05-13 3620 34.396 1 251.7 6000 106775 3630 3191.69 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1435 1 4876.81 7267.18 \n",
"日期存在，即将更新\n",
"新数据 [3620.0, 34.396, '', 251.7, '', 106775.4686698, '', 3171.0934, '', '', 7267.1815]\n",
"更新数据后\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1435 2025-05-13 3620 34.396 1 251.7 6000 106775 3630 3191.69 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1435 1 4876.81 7267.18 \n",
"20250514\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1434 2025-05-12 3620 34.396 1 251.7 6000 106775 3630 3171.09 \n",
"1435 2025-05-13 3600 34.396 1 -256.33 6000 106220 3630 3191.69 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1434 1 4876.81 7267.18 \n",
"1435 1 4876.81 7248.69 \n",
"前一天的 3171.0934 <class 'float'>\n",
"现在的 3191.6925 <class 'float'>\n",
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"0 1434 2025-05-12 3620 34.396 1 251.7 6000 106775 3630 \n",
"1 1435 2025-05-13 3600 34.396 1 -256.33 6000 106220 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"0 3171.09 1 4876.81 7267.18 \n",
"1 3191.69 1 4876.81 7248.69 \n",
"昨日计划提货偏差改之前 -256.33\n",
"昨日计划提货偏差改之后 2371.8786\n",
"**************************************************预测结果： 3623.36\n",
"更新前一天数据\n",
"更新数据前\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1436 2025-05-14 3600 34.396 1 -256.33 6000 106220 3630 3318.29 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1436 1 4876.81 7248.69 \n",
"日期存在，即将更新\n",
"新数据 [3600.0, 34.396, '', -256.33, '', 106220.4490483, '', 3191.6925, '', '', 7248.688]\n",
"更新数据后\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1436 2025-05-14 3600 34.396 1 -256.33 6000 106220 3630 3318.29 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1436 1 4876.81 7248.69 \n",
"20250515\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1435 2025-05-13 3600 34.396 1 -256.33 6000 106220 3630 3191.69 \n",
"1436 2025-05-14 3600 33.557 1 -1015.89 6000 106321 3630 3318.29 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1435 1 4876.81 7248.69 \n",
"1436 1 4876.81 7200.57 \n",
"前一天的 3191.6925 <class 'float'>\n",
"现在的 3318.2936 <class 'float'>\n",
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"0 1435 2025-05-13 3600 34.396 1 -256.33 6000 106220 3630 \n",
"1 1436 2025-05-14 3600 33.557 1 -1015.89 6000 106321 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"0 3191.69 1 4876.81 7248.69 \n",
"1 3318.29 1 4876.81 7200.57 \n",
"昨日计划提货偏差改之前 -1015.89\n",
"昨日计划提货偏差改之后 2323.7650999999996\n",
"**************************************************预测结果： 3628.64\n",
"更新前一天数据\n",
"更新数据前\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1437 2025-05-15 3600 33.557 1 -1015.89 6000 106321 3630 3170.14 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1437 1 4876.81 7200.57 \n",
"日期存在，即将更新\n",
"新数据 [3600.0, 33.557, '', -1015.89, '', 106321.4042938, '', 3318.2936, '', '', 7200.5745]\n",
"更新数据后\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1437 2025-05-15 3600 33.557 1 -1015.89 6000 106321 3630 3170.14 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1437 1 4876.81 7200.57 \n",
"20250516\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1436 2025-05-14 3600 33.557 1 -1015.89 6000 106321 3630 3318.29 \n",
"1437 2025-05-15 3600 33.9765 1 1408.4 6000 105595 3630 3170.14 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1436 1 4876.81 7200.57 \n",
"1437 1 4876.81 7298.53 \n",
"前一天的 3318.2936 <class 'float'>\n",
"现在的 3170.1365 <class 'float'>\n",
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"0 1436 2025-05-14 3600 33.557 1 -1015.89 6000 106321 3630 \n",
"1 1437 2025-05-15 3600 33.9765 1 1408.4 6000 105595 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"0 3318.29 1 4876.81 7200.57 \n",
"1 3170.14 1 4876.81 7298.53 \n",
"昨日计划提货偏差改之前 1408.4\n",
"昨日计划提货偏差改之后 2421.7255999999998\n",
"**************************************************预测结果： 3573.82\n",
"更新前一天数据\n",
"更新数据前\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1438 2025-05-16 3620 33.9765 1 1408.4 6000 105595 3630 3164.18 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1438 1 4876.81 7298.53 \n",
"日期存在，即将更新\n",
"新数据 [3600.0, 33.9765, '', 1408.4, '', 105595.49, '', 3170.1365, '', '', 7298.535]\n",
"更新数据后\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1438 2025-05-16 3620 33.9765 1 1408.4 6000 105595 3630 3164.18 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1438 1 4876.81 7298.53 \n",
"20250517\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1437 2025-05-15 3600 33.9765 1 1408.4 6000 105595 3630 3170.14 \n",
"1438 2025-05-16 3620 34.8154 1 -1308.63 6000 107985 3630 3164.18 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1437 1 4876.81 7298.53 \n",
"1438 1 4876.81 7407.17 \n",
"前一天的 3170.1365 <class 'float'>\n",
"现在的 3164.177 <class 'float'>\n",
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"0 1437 2025-05-15 3600 33.9765 1 1408.4 6000 105595 3630 \n",
"1 1438 2025-05-16 3620 34.8154 1 -1308.63 6000 107985 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"0 3170.14 1 4876.81 7298.53 \n",
"1 3164.18 1 4876.81 7407.17 \n",
"昨日计划提货偏差改之前 -1308.63\n",
"昨日计划提货偏差改之后 2530.3635999999997\n",
"**************************************************预测结果： 3603.59\n",
"更新前一天数据\n",
"更新数据前\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1439 2025-05-17 3620 34.8154 1 -1308.63 6000 107985 3630 3246.69 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1439 1 4876.81 7407.17 \n",
"日期存在，即将更新\n",
"新数据 [3620.0, 34.8154, '', -1308.63, '', 107984.71, '', 3164.177, '', '', 7407.173]\n",
"更新数据后\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1439 2025-05-17 3620 34.8154 1 -1308.63 6000 107985 3630 3246.69 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1439 1 4876.81 7407.17 \n",
"20250518\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1438 2025-05-16 3620 34.8154 1 -1308.63 6000 107985 3630 3164.18 \n",
"1439 2025-05-17 3620 34.8154 1 3334.9 6000 109984 3630 3246.69 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1438 1 4876.81 7407.17 \n",
"1439 1 4876.81 7029.96 \n",
"前一天的 3164.177 <class 'float'>\n",
"现在的 3246.6882 <class 'float'>\n",
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"0 1438 2025-05-16 3620 34.8154 1 -1308.63 6000 107985 3630 \n",
"1 1439 2025-05-17 3620 34.8154 1 3334.9 6000 109984 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"0 3164.18 1 4876.81 7407.17 \n",
"1 3246.69 1 4876.81 7029.96 \n",
"昨日计划提货偏差改之前 3334.9\n",
"昨日计划提货偏差改之后 2153.1485999999995\n",
"**************************************************预测结果： 3623.0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"更新前一天数据\n",
"更新数据前\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1440 2025-05-18 3620 34.8154 1 3334.9 6000 109984 3630 3249.93 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1440 1 4876.81 7029.96 \n",
"日期存在，即将更新\n",
"新数据 [3620.0, '', '', 3334.9, '', 109984.19, '', 3246.6882, '', '', 7029.958]\n",
"更新数据后\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1440 2025-05-18 3620 34.8154 1 3334.9 6000 109984 3630 3249.93 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1440 1 4876.81 7029.96 \n",
"20250519\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1439 2025-05-17 3620 34.8154 1 3334.9 6000 109984 3630 3246.69 \n",
"1440 2025-05-18 3620 34.8154 1 793.84 6000 110418 3630 3249.93 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1439 1 4876.81 7029.96 \n",
"1440 1 4876.81 6533.09 \n",
"前一天的 3246.6882 <class 'float'>\n",
"现在的 3249.9268 <class 'float'>\n",
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"0 1439 2025-05-17 3620 34.8154 1 3334.9 6000 109984 3630 \n",
"1 1440 2025-05-18 3620 34.8154 1 793.84 6000 110418 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"0 3246.69 1 4876.81 7029.96 \n",
"1 3249.93 1 4876.81 6533.09 \n",
"昨日计划提货偏差改之前 793.84\n",
"昨日计划提货偏差改之后 1656.2806\n",
"**************************************************预测结果： 3622.23\n",
"更新前一天数据\n",
"更新数据前\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1441 2025-05-19 3600 34.8154 1 793.84 6000 110418 3630 3236.47 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1441 1 4876.81 6533.09 \n",
"日期存在，即将更新\n",
"新数据 [3620.0, '', '', 793.84, '', 110418.01, '', 3249.9268, '', '', 6533.09]\n",
"更新数据后\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1441 2025-05-19 3600 34.8154 1 793.84 6000 110418 3630 3236.47 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1441 1 4876.81 6533.09 \n",
"20250520\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1440 2025-05-18 3620 31.4597 1 793.84 6000 110418 3630 3249.93 \n",
"1441 2025-05-19 3600 31.4597 1 1246.01 6000 111150 3630 3236.47 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1440 1 4876.81 6533.09 \n",
"1441 1 4876.81 6817.39 \n",
"前一天的 3249.9268 <class 'float'>\n",
"现在的 3236.4706 <class 'float'>\n",
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"0 1440 2025-05-18 3620 31.4597 1 793.84 6000 110418 3630 \n",
"1 1441 2025-05-19 3600 31.4597 1 1246.01 6000 111150 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"0 3249.93 1 4876.81 6533.09 \n",
"1 3236.47 1 4876.81 6817.39 \n",
"昨日计划提货偏差改之前 1246.01\n",
"昨日计划提货偏差改之后 1940.5766000000003\n",
"**************************************************预测结果： 3622.81\n",
"更新前一天数据\n",
"更新数据前\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1442 2025-05-20 3600 31.4597 1 1246.01 6000 111150 3630 3248.42 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1442 1 4876.81 6817.39 \n",
"日期存在，即将更新\n",
"新数据 [3600.0, 31.4597, '', 1246.01, '', 111149.96, '', 3236.4706, '', '', 6817.386]\n",
"更新数据后\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1442 2025-05-20 3600 31.4597 1 1246.01 6000 111150 3630 3248.42 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1442 1 4876.81 6817.39 \n",
"20250521\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1441 2025-05-19 3600 31.4597 1 1246.01 6000 111150 3630 3236.47 \n",
"1442 2025-05-20 3600 28.104 1 1406.68 6000 111294 3630 3248.42 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1441 1 4876.81 6817.39 \n",
"1442 1 4876.81 6772.02 \n",
"前一天的 3236.4706 <class 'float'>\n",
"现在的 3248.4206 <class 'float'>\n",
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"0 1441 2025-05-19 3600 31.4597 1 1246.01 6000 111150 3630 \n",
"1 1442 2025-05-20 3600 28.104 1 1406.68 6000 111294 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"0 3236.47 1 4876.81 6817.39 \n",
"1 3248.42 1 4876.81 6772.02 \n",
"昨日计划提货偏差改之前 1406.68\n",
"昨日计划提货偏差改之后 1895.2120999999997\n",
"**************************************************预测结果： 3603.2\n",
"更新前一天数据\n",
"更新数据前\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1443 2025-05-21 3600 28.104 1 1406.68 6000 111294 3630 3303.26 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1443 1 4876.81 6772.02 \n",
"日期存在，即将更新\n",
"新数据 [3600.0, 28.104, '', 1406.68, '', 111293.57, '', 3248.4206, '', '', 6772.0215]\n",
"更新数据后\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1443 2025-05-21 3600 28.104 1 1406.68 6000 111294 3630 3303.26 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1443 1 4876.81 6772.02 \n",
"20250522\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1442 2025-05-20 3600 28.104 1 1406.68 6000 111294 3630 3248.42 \n",
"1443 2025-05-21 3600 28.104 1 265.5 6000 110523 3630 3303.26 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1442 1 4876.81 6772.02 \n",
"1443 1 4876.81 7338.12 \n",
"前一天的 3248.4206 <class 'float'>\n",
"现在的 3303.2591 <class 'float'>\n",
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"0 1442 2025-05-20 3600 28.104 1 1406.68 6000 111294 3630 \n",
"1 1443 2025-05-21 3600 28.104 1 265.5 6000 110523 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"0 3248.42 1 4876.81 6772.02 \n",
"1 3303.26 1 4876.81 7338.12 \n",
"昨日计划提货偏差改之前 265.5\n",
"昨日计划提货偏差改之后 2461.3066\n",
"**************************************************预测结果： 3604.08\n",
"更新前一天数据\n",
"更新数据前\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1444 2025-05-22 3600 28.104 1 265.5 6000 110523 3630 -61269.9 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1444 1 4876.81 7338.12 \n",
"日期存在，即将更新\n",
"新数据 [3600.0, 28.104, '', 265.5, '', 110522.98, '', 3303.2591, '', '', 7338.116]\n",
"更新数据后\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1444 2025-05-22 3600 28.104 1 265.5 6000 110523 3630 -61269.9 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1444 1 4876.81 7338.12 \n",
"20250523\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1443 2025-05-21 3600 28.104 1 265.5 6000 110523 3630 3303.26 \n",
"1444 2025-05-22 3600 24.7483 1 2446.95 6000 110929 3630 -61269.9 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1443 1 4876.81 7338.12 \n",
"1444 1 4876.81 7444.25 \n",
"前一天的 3303.2591 <class 'float'>\n",
"现在的 -61269.9011 <class 'float'>\n",
"修改了\n",
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"0 1443 2025-05-21 3600 28.104 1 265.5 6000 110523 3630 \n",
"1 1444 2025-05-22 3600 24.7483 1 2446.95 6000 110929 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"0 3303.26 1 4876.81 7338.12 \n",
"1 3303.26 1 4876.81 7444.25 \n",
"昨日计划提货偏差改之前 2446.95\n",
"昨日计划提货偏差改之后 2567.4371\n",
"**************************************************预测结果： 3604.24\n",
"更新前一天数据\n",
"更新数据前\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1445 2025-05-23 3600 24.7483 1 2446.95 6000 110929 3630 -65021.4 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1445 1 4876.81 7444.25 \n",
"日期存在，即将更新\n",
"新数据 [3600.0, 24.7483, '', 2446.95, '', 110928.95, '', -61269.9011, '', '', 7444.2465]\n",
"更新数据后\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1445 2025-05-23 3600 24.7483 1 2446.95 6000 110929 3630 -65021.4 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1445 1 4876.81 7444.25 \n",
"20250524\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1444 2025-05-22 3600 24.7483 1 2446.95 6000 110929 3630 -61269.9 \n",
"1445 2025-05-23 3600 26.0067 1 318.59 6000 109961 3630 -65021.4 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1444 1 4876.81 7444.25 \n",
"1445 1 4876.81 7546.95 \n",
"前一天的 -61269.9011 <class 'float'>\n",
"现在的 -65021.4299 <class 'float'>\n",
"修改了\n",
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"0 1444 2025-05-22 3600 24.7483 1 2446.95 6000 110929 3630 \n",
"1 1445 2025-05-23 3600 26.0067 1 318.59 6000 109961 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"0 -61269.9 1 4876.81 7444.25 \n",
"1 -61269.9 1 4876.81 7546.95 \n",
"昨日计划提货偏差改之前 318.59\n",
"昨日计划提货偏差改之后 2670.1436000000003\n",
"**************************************************预测结果： 3604.4\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"更新前一天数据\n",
"更新数据前\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1446 2025-05-24 3600 26.0067 1 318.59 6000 109961 3630 -70579.8 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1446 1 4876.81 7546.95 \n",
"日期存在，即将更新\n",
"新数据 [3600.0, 26.0067, '', 318.59, '', 109961.08, '', -65021.4299, '', '', 7546.953]\n",
"更新数据后\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1446 2025-05-24 3600 26.0067 1 318.59 6000 109961 3630 -70579.8 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1446 1 4876.81 7546.95 \n",
"20250525\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1445 2025-05-23 3600 26.0067 1 318.59 6000 109961 3630 -65021.4 \n",
"1446 2025-05-24 3600 26.0067 1 2949.09 6000 110078 3630 -70579.8 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1445 1 4876.81 7546.95 \n",
"1446 1 4876.81 6911.72 \n",
"前一天的 -65021.4299 <class 'float'>\n",
"现在的 -70579.7584 <class 'float'>\n",
"修改了\n",
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"0 1445 2025-05-23 3600 26.0067 1 318.59 6000 109961 3630 \n",
"1 1446 2025-05-24 3600 26.0067 1 2949.09 6000 110078 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"0 -65021.4 1 4876.81 7546.95 \n",
"1 -65021.4 1 4876.81 6911.72 \n",
"昨日计划提货偏差改之前 2949.09\n",
"昨日计划提货偏差改之后 2034.9106000000002\n",
"**************************************************预测结果： 3603.41\n",
"更新前一天数据\n",
"更新数据前\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1447 2025-05-25 3600 26.0067 1 2949.09 6000 110078 3630 -65108.7 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1447 1 4876.81 6911.72 \n",
"日期存在，即将更新\n",
"新数据 [3600.0, '', '', 2949.09, '', 110078.22, '', -70579.7584, '', '', 6911.72]\n",
"更新数据后\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1447 2025-05-25 3600 26.0067 1 2949.09 6000 110078 3630 -65108.7 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1447 1 4876.81 6911.72 \n",
"20250526\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1446 2025-05-24 3600 26.0067 1 2949.09 6000 110078 3630 -70579.8 \n",
"1447 2025-05-25 3600 26.0067 1 3076.16 6000 110169 3630 -65108.7 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1446 1 4876.81 6911.72 \n",
"1447 1 4876.81 6912.92 \n",
"前一天的 -70579.7584 <class 'float'>\n",
"现在的 -65108.6941 <class 'float'>\n",
"修改了\n",
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"0 1446 2025-05-24 3600 26.0067 1 2949.09 6000 110078 3630 \n",
"1 1447 2025-05-25 3600 26.0067 1 3076.16 6000 110169 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"0 -70579.8 1 4876.81 6911.72 \n",
"1 -70579.8 1 4876.81 6912.92 \n",
"昨日计划提货偏差改之前 3076.16\n",
"昨日计划提货偏差改之后 2036.1106\n",
"**************************************************预测结果： 3603.42\n",
"更新前一天数据\n",
"更新数据前\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1448 2025-05-26 3600 26.0067 1 3076.16 6000 110169 3630 -62606.2 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1448 1 4876.81 6912.92 \n",
"日期存在，即将更新\n",
"新数据 [3600.0, '', '', 3076.16, '', 110168.92, '', -65108.6941, '', '', 6912.92]\n",
"更新数据后\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1448 2025-05-26 3600 26.0067 1 3076.16 6000 110169 3630 -62606.2 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1448 1 4876.81 6912.92 \n",
"20250527\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1447 2025-05-25 3600 28.943 1 3076.16 6000 110169 3630 -65108.7 \n",
"1448 2025-05-26 3600 28.104 1 -1096.24 6000 110398 3630 -62606.2 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1447 1 4876.81 6912.92 \n",
"1448 1 4876.81 7359.64 \n",
"前一天的 -65108.6941 <class 'float'>\n",
"现在的 -62606.1562 <class 'float'>\n",
"修改了\n",
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"0 1447 2025-05-25 3600 28.943 1 3076.16 6000 110169 3630 \n",
"1 1448 2025-05-26 3600 28.104 1 -1096.24 6000 110398 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"0 -65108.7 1 4876.81 6912.92 \n",
"1 -65108.7 1 4876.81 7359.64 \n",
"昨日计划提货偏差改之前 -1096.24\n",
"昨日计划提货偏差改之后 2482.8306000000002\n",
"**************************************************预测结果： 3604.11\n",
"更新前一天数据\n",
"更新数据前\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1449 2025-05-27 3600 28.104 1 -1096.24 6000 110398 3630 -63415.4 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1449 1 4876.81 7359.64 \n",
"日期存在，即将更新\n",
"新数据 [3600.0, 28.104, '', -1096.24, '', 110398.08, '', -62606.1562, '', '', 7359.64]\n",
"更新数据后\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1449 2025-05-27 3600 28.104 1 -1096.24 6000 110398 3630 -63415.4 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1449 1 4876.81 7359.64 \n",
"20250528\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1448 2025-05-26 3600 28.104 1 -1096.24 6000 110398 3630 -62606.2 \n",
"1449 2025-05-27 3600 31.4597 1 1608.74 6000 109292 3630 -63415.4 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1448 1 4876.81 7359.64 \n",
"1449 1 4876.81 7709.12 \n",
"前一天的 -62606.1562 <class 'float'>\n",
"现在的 -63415.4022 <class 'float'>\n",
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"0 1448 2025-05-26 3600 28.104 1 -1096.24 6000 110398 3630 \n",
"1 1449 2025-05-27 3600 31.4597 1 1608.74 6000 109292 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"0 -62606.2 1 4876.81 7359.64 \n",
"1 -63415.4 1 4876.81 7709.12 \n",
"昨日计划提货偏差改之前 1608.74\n",
"昨日计划提货偏差改之后 2832.3106\n",
"**************************************************预测结果： 3442.34\n",
"更新前一天数据\n",
"更新数据前\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1450 2025-05-28 3600 31.4597 1 1608.74 6000 109292 3630 3045.17 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1450 1 4876.81 7709.12 \n",
"日期存在，即将更新\n",
"新数据 [3600.0, 31.4597, '', 1608.74, '', 109291.7, '', -63415.4022, '', '', 7709.12]\n",
"更新数据后\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1450 2025-05-28 3600 31.4597 1 1608.74 6000 109292 3630 3045.17 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1450 1 4876.81 7709.12 \n",
"20250529\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1449 2025-05-27 3600 31.4597 1 1608.74 6000 109292 3630 -63415.4 \n",
"1450 2025-05-28 3620 32.2987 1 -1155.78 6000 109867 3630 3045.17 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1449 1 4876.81 7709.12 \n",
"1450 1 4876.81 7515.76 \n",
"前一天的 -63415.4022 <class 'float'>\n",
"现在的 3045.1686 <class 'float'>\n",
"修改了\n",
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"0 1449 2025-05-27 3600 31.4597 1 1608.74 6000 109292 3630 \n",
"1 1450 2025-05-28 3620 32.2987 1 -1155.78 6000 109867 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"0 -63415.4 1 4876.81 7709.12 \n",
"1 -63415.4 1 4876.81 7515.76 \n",
"昨日计划提货偏差改之前 -1155.78\n",
"昨日计划提货偏差改之后 2638.9506\n",
"**************************************************预测结果： 3603.86\n",
"更新前一天数据\n",
"更新数据前\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1451 2025-05-29 3620 32.2987 1 -1155.78 6000 109867 3630 3142.84 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1451 1 4876.81 7515.76 \n",
"日期存在，即将更新\n",
"新数据 [3620.0, 32.2987, '', -1155.78, '', 109866.97, '', 3045.1686, '', '', 7515.76]\n",
"更新数据后\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1451 2025-05-29 3620 32.2987 1 -1155.78 6000 109867 3630 3142.84 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1451 1 4876.81 7515.76 \n",
"20250530\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1450 2025-05-28 3620 32.2987 1 -1155.78 6000 109867 3630 3045.17 \n",
"1451 2025-05-29 3620 31.4597 1 1468.98 6000 110016 3630 3142.84 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1450 1 4876.81 7515.76 \n",
"1451 1 4876.81 7321.16 \n",
"前一天的 3045.1686 <class 'float'>\n",
"现在的 3142.8424 <class 'float'>\n",
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"0 1450 2025-05-28 3620 32.2987 1 -1155.78 6000 109867 3630 \n",
"1 1451 2025-05-29 3620 31.4597 1 1468.98 6000 110016 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"0 3045.17 1 4876.81 7515.76 \n",
"1 3142.84 1 4876.81 7321.16 \n",
"昨日计划提货偏差改之前 1468.98\n",
"昨日计划提货偏差改之后 2444.3505999999998\n",
"**************************************************预测结果： 3623.59\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"更新前一天数据\n",
"更新数据前\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1452 2025-05-30 3650 31.4597 1 1468.98 6000 110016 3630 3002.11 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1452 1 4876.81 7321.16 \n",
"日期存在，即将更新\n",
"新数据 [3620.0, 31.4597, '', 1468.98, '', 110015.78, '', 3142.8424, '', '', 7321.16]\n",
"更新数据后\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1452 2025-05-30 3650 31.4597 1 1468.98 6000 110016 3630 3002.11 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1452 1 4876.81 7321.16 \n",
"20250531\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1451 2025-05-29 3620 31.4597 1 1468.98 6000 110016 3630 3142.84 \n",
"1452 2025-05-30 3650 31.4597 1 200.53 6000 109452 3630 3002.11 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1451 1 4876.81 7321.16 \n",
"1452 1 4876.81 7210.15 \n",
"前一天的 3142.8424 <class 'float'>\n",
"现在的 3002.1107 <class 'float'>\n",
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"0 1451 2025-05-29 3620 31.4597 1 1468.98 6000 110016 3630 \n",
"1 1452 2025-05-30 3650 31.4597 1 200.53 6000 109452 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"0 3142.84 1 4876.81 7321.16 \n",
"1 3002.11 1 4876.81 7210.15 \n",
"昨日计划提货偏差改之前 200.53\n",
"昨日计划提货偏差改之后 2333.3405999999995\n",
"**************************************************预测结果： 3595.27\n",
"更新前一天数据\n",
"更新数据前\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1453 2025-05-31 3650 31.4597 1 200.53 6000 109452 3630 2931.21 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1453 1 4876.81 7210.15 \n",
"日期存在，即将更新\n",
"新数据 [3650.0, 31.4597, '', 200.53, '', 109452.46, '', 3002.1107, '', '', 7210.15]\n",
"更新数据后\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1453 2025-05-31 3650 31.4597 1 200.53 6000 109452 3630 2931.21 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1453 1 4876.81 7210.15 \n",
"20250601\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1452 2025-05-30 3650 31.4597 1 200.53 6000 109452 3630 3002.11 \n",
"1453 2025-05-31 3650 31.4597 1 200.53 6000 109452 3630 2931.21 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1452 1 4876.81 7210.15 \n",
"1453 1 4876.81 7210.15 \n",
"前一天的 3002.1107 <class 'float'>\n",
"现在的 2931.2118 <class 'float'>\n",
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"0 1452 2025-05-30 3650 31.4597 1 200.53 6000 109452 3630 \n",
"1 1453 2025-05-31 3650 31.4597 1 200.53 6000 109452 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"0 3002.11 1 4876.81 7210.15 \n",
"1 2931.21 1 4876.81 7210.15 \n",
"昨日计划提货偏差改之前 200.53\n",
"昨日计划提货偏差改之后 2333.3405999999995\n",
"**************************************************预测结果： 3653.42\n",
"更新前一天数据\n",
"更新数据前\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1454 2025-06-01 3650 31.4597 1 200.53 6000 109452 3630 2940.43 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1454 1 4876.81 7210.15 \n",
"日期存在，即将更新\n",
"新数据 [3650.0, '', '', 4837.68, '', 109979.05, '', 2931.2118, '', '', 6263.17]\n",
"更新数据后\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1454 2025-06-01 3650 31.4597 1 200.53 6000 109452 3630 2940.43 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1454 1 4876.81 7210.15 \n",
"20250602\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1453 2025-05-31 3650 31.4597 1 4837.68 6000 109979 3630 2931.21 \n",
"1454 2025-06-01 3620 31.4597 1 2998.04 6000 112404 3630 2940.43 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1453 1 4876.81 6263.17 \n",
"1454 1 4876.81 8453.8 \n",
"前一天的 2931.2118 <class 'float'>\n",
"现在的 2940.4318 <class 'float'>\n",
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"0 1453 2025-05-31 3650 31.4597 1 4837.68 6000 109979 3630 \n",
"1 1454 2025-06-01 3620 31.4597 1 2998.04 6000 112404 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"0 2931.21 1 4876.81 6263.17 \n",
"1 2940.43 1 4876.81 8453.8 \n",
"昨日计划提货偏差改之前 2998.04\n",
"昨日计划提货偏差改之后 3576.9925999999996\n",
"**************************************************预测结果： 3655.35\n",
"更新前一天数据\n",
"更新数据前\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1455 2025-06-02 3620 31.4597 1 2998.04 6000 112404 3630 3088.17 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1455 1 4876.81 8453.8 \n",
"日期存在，即将更新\n",
"新数据 [3620.0, '', '', 2998.04, '', 112404.0, '', 2940.4318, '', '', 8453.802]\n",
"更新数据后\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1455 2025-06-02 3620 31.4597 1 2998.04 6000 112404 3630 3088.17 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1455 1 4876.81 8453.8 \n",
"20250603\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1454 2025-06-01 3620 31.4597 1 2998.04 6000 112404 3630 2940.43 \n",
"1455 2025-06-02 3650 31.4597 1 -361.55 6000 113137 3630 3088.17 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1454 1 4876.81 8453.8 \n",
"1455 1 4876.81 7274.36 \n",
"前一天的 2940.4318 <class 'float'>\n",
"现在的 3088.1711 <class 'float'>\n",
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"0 1454 2025-06-01 3620 31.4597 1 2998.04 6000 112404 3630 \n",
"1 1455 2025-06-02 3650 31.4597 1 -361.55 6000 113137 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"0 2940.43 1 4876.81 8453.8 \n",
"1 3088.17 1 4876.81 7274.36 \n",
"昨日计划提货偏差改之前 -361.55\n",
"昨日计划提货偏差改之后 2397.5505999999996\n",
"**************************************************预测结果： 3653.07\n",
"更新前一天数据\n",
"更新数据前\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1456 2025-06-03 3650 31.4597 1 -361.55 6000 113137 3630 3144.69 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1456 1 4876.81 7274.36 \n",
"日期存在，即将更新\n",
"新数据 [3650.0, '', '', -361.55, '', 113137.24, '', 3088.1711, '', '', 7274.36]\n",
"更新数据后\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1456 2025-06-03 3650 31.4597 1 -361.55 6000 113137 3630 3144.69 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1456 1 4876.81 7274.36 \n",
"20250604\n",
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
"1455 2025-06-02 3650 33.557 1 -361.55 6000 113137 3630 3088.17 \n",
"1456 2025-06-03 3650 34.8154 1 -676.29 6000 113920 3630 3144.69 \n",
"\n",
" 订单结构 计划产量 京博产量 \n",
"1455 1 4876.81 7274.36 \n",
"1456 1 4876.81 7404.7 \n",
"前一天的 3088.1711 <class 'float'>\n",
"现在的 3144.6905 <class 'float'>\n",
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
"0 1455 2025-06-02 3650 33.557 1 -361.55 6000 113137 3630 \n",
"1 1456 2025-06-03 3650 34.8154 1 -676.29 6000 113920 3630 \n",
"\n",
" 即期成本 订单结构 计划产量 京博产量 \n",
"0 3088.17 1 4876.81 7274.36 \n",
"1 3144.69 1 4876.81 7404.7 \n",
"昨日计划提货偏差改之前 -676.29\n",
"昨日计划提货偏差改之后 2527.8866\n",
"**************************************************预测结果： 3653.59\n"
]
}
],
"source": [
"if __name__ == \"__main__\":\n",
" print(\"运行中...\")\n",
" # 每天定时12点运行\n",
" while True:\n",
" # 获取当前时间\n",
" current_time = time.strftime(\"%H:%M:%S\", time.localtime())\n",
" try:\n",
" # 判断当前时间是否为执行任务的时间点\n",
" if current_time == \"12:00:00\":\n",
" main()\n",
" elif current_time == \"20:00:00\":\n",
" print(\"更新前一天数据\")\n",
" token = getLogToken()\n",
" updateYesterdayExcelData(token=token)\n",
" time.sleep(1)\n",
" except Exception as e:\n",
" print(f\"执行失败: {e}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# # 重新上传定性数据\n",
"\n",
"\n",
"\n",
"# start_date = datetime(2025,5,1)\n",
"# end_date = datetime(2025, 5, 7)\n",
"# token = getLogToken()\n",
"# while start_date < end_date:\n",
"# main(start_date,token)\n",
"# start_date += timedelta(days=1)\n",
"# time.sleep(5)\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# # 调试更新数据\n",
"# date = '2025-01-24'\n",
"# token = getLogToken()\n",
"# updateYesterdayExcelData(date=date,token=token)\n",
"# print('更新完了')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# # 快速调试线上\n",
"# def main():\n",
"# # 获取当前日期\n",
"# date = datetime.now().date()\n",
"# date = date.strftime('%Y%m%d')\n",
"# # 获取登录token\n",
"# token = getLogToken()\n",
"# updateExcelData(date,token)\n",
"# update_e_value('定性模型数据项12-11.xlsx', 8, 1000)\n",
"# x = qualitativeModel()\n",
"# print('预测结果：',x)\n",
"# cur_time,cur_time2 = getNow(date)\n",
"# pushData(cur_time,x,token)\n",
"\n",
"# current_time = time.strftime(\"%H:%M:%S\", time.localtime())\n",
"# main()\n",
"# token = getLogToken()\n",
"# updateYesterdayExcelData(token=token)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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