import requests import json import xlrd import xlwt from datetime import datetime, timedelta import time import pandas as pd pd.set_option('display.max_columns', None) import numpy as np # 变量定义 login_url = "http://10.200.32.39/jingbo-api/api/server/login" login_push_url = "http://10.200.32.39/jingbo-api/api/server/login" # query_data_list_item_nos_url search_url = "http://10.200.32.39/jingbo-api/api/warehouse/dwDataItem/queryByItemNos" #jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos upload_url = "http://10.200.32.39/jingbo-api/api/dw/dataValue/pushDataValueList" queryDataListItemNos_url = "http://10.200.32.39/jingbo-api//api/warehouse/dwDataItem/queryDataListItemNos" query_data_list_item_nos_data = { "funcModule": "数据项", "funcOperation": "查询", "data": { "dateStart": "20200101", "dateEnd": "20241231", "dataItemNoList": ["Brentzdj", "Brentzgj"] # 数据项编码,代表 brent最低价和最高价 } } login_data = { "data": { "account": "api_dev", "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", "tenantHashCode": "8a4577dbd919675758d57999a1e891fe", "terminal": "API" }, "funcModule": "API", "funcOperation": "获取token" } login_push_data = { "data": { "account": "api_dev", "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", "tenantHashCode": "8a4577dbd919675758d57999a1e891fe", "terminal": "API" }, "funcModule": "API", "funcOperation": "获取token" } read_file_path_name = "定性模型数据项12-11.xlsx" one_cols = [] two_cols = [] def get_head_auth(): login_res = requests.post(url=login_url, json=login_data, timeout=(3, 5)) text = json.loads(login_res.text) if text["status"]: token = text["data"]["accessToken"] return token else: print("获取认证失败") return None def get_head_push_auth(): login_res = requests.post(url=login_push_url, json=login_push_data, timeout=(3, 5)) text = json.loads(login_res.text) if text["status"]: token = text["data"]["accessToken"] return token else: print("获取认证失败") return None def update_e_value(file_path, column_index, threshold): """ 数据修正需求：2025年1月8日 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 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。 :param file_path: Excel文件路径 :param column_index: 需要更新的列索引 :param threshold: 变化阈值 """ # 读取Excel文件 # try: # df = pd.read_excel(file_path, engine='openpyxl') # except: # df = pd.read_excel(file_path, engine='xlrd') df = pd.read_excel(file_path) # 所有列列统一数据格式为float df = df.applymap(lambda x: float(x) if isinstance(x, (int, float)) else x) # print(df.tail()) # 填充缺失值 df = df.fillna(method='ffill') # 获取昨天，前天数据 df1 = df[-3:-1] print(df1) # 获取前一天的指定列值 previous_value = df1.iloc[0, column_index] print('前一天的',previous_value,type(previous_value)) # 获取当前的指定列值 current_value = df1.iloc[1, column_index] print('现在的',current_value,type(current_value)) # 判断指定列值的变化是否大于阈值 if abs(current_value - previous_value) > threshold: # 如果变化大于阈值，将当前的指定列值改为前一天的值 df.iloc[-2, column_index] = previous_value print('修改了') # print(df.tail()) # 将修改后的数据写回Excel文件 df.to_excel(file_path, index=False,engine='openpyxl') def getLogToken(): login_res = requests.post(url=login_url, json=login_data, timeout=(3, 5)) text = json.loads(login_res.text) if text["status"]: token = text["data"]["accessToken"] else: print("获取认证失败") token = None return token def updateExcelDatabak(date='',token=None): workbook = xlrd.open_workbook(read_file_path_name) # 选择第一个表格 sheet = workbook.sheet_by_index(0) row_data = sheet.row_values(1) one_cols = row_data cur_time,cur_time2 = getNow(date) search_data = { "data": { "date": cur_time, "dataItemNoList": one_cols[1:] }, "funcModule": "数据项", "funcOperation": "查询" } headers = {"Authorization": token} search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5)) search_value = json.loads(search_res.text)["data"] # datas = search_value if search_value: datas = search_value else : datas = None append_rows = [cur_time2] dataItemNo_dataValue = {} # for data_value in datas: # dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"] for data_value in datas: if "dataValue" not in data_value: print(data_value) dataItemNo_dataValue[data_value["dataItemNo"]] = "" else: dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"] for value in one_cols[1:]: if value in dataItemNo_dataValue: append_rows.append(dataItemNo_dataValue[value]) else: append_rows.append("") workbook = xlrd.open_workbook('定性模型数据项12-11.xlsx') # 获取所有sheet的个数 sheet_count = len(workbook.sheet_names()) # 获取所有sheet的名称 sheet_names = workbook.sheet_names() new_workbook = xlwt.Workbook() for i in range(sheet_count): # 获取当前sheet sheet = workbook.sheet_by_index(i) # 获取sheet的行数和列数 row_count = sheet.nrows col_count = sheet.ncols # 获取原有数据 data = [] for row in range(row_count): row_data = [] for col in range(col_count): row_data.append(sheet.cell_value(row, col)) data.append(row_data) # 创建xlwt的Workbook对象 # 创建sheet new_sheet = new_workbook.add_sheet(sheet_names[i]) # 将原有的数据写入新的sheet for row in range(row_count): for col in range(col_count): new_sheet.write(row, col, data[row][col]) if i == 0: # 在新的sheet中添加数据 for col in range(col_count): new_sheet.write(row_count, col, append_rows[col]) # 保存新的xls文件 new_workbook.save("定性模型数据项12-11.xlsx") def updateYesterdayExcelData(date='', token=None): # 使用pandas读取Excel文件 df = pd.read_excel(read_file_path_name, engine='openpyxl') # 获取第二行的数据作为列名 one_cols = df.iloc[0,:].tolist() # 获取当前日期的前一天 if date == '': previous_date = (datetime.now() - timedelta(days=1)).strftime('%Y-%m-%d') else: # 字符串转日期 previous_date = (datetime.strptime(date, "%Y-%m-%d")-timedelta(days=1)).strftime('%Y-%m-%d') cur_time, cur_time2 = getNow(previous_date) search_data = { "data": { "date": cur_time, "dataItemNoList": one_cols[1:] }, "funcModule": "数据项", "funcOperation": "查询" } headers = {"Authorization": token} search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5)) search_value = json.loads(search_res.text)["data"] if search_value: datas = search_value else: datas = None append_rows = [cur_time2] dataItemNo_dataValue = {} for data_value in datas: if "dataValue" not in data_value: print(data_value) dataItemNo_dataValue[data_value["dataItemNo"]] = "" else: dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"] for value in one_cols[1:]: if value in dataItemNo_dataValue: append_rows.append(dataItemNo_dataValue[value]) else: append_rows.append("") print('更新数据前') print(df.tail(1)) # 检查日期是否已存在于数据中 if previous_date not in df['日期'].values: # 将新的数据添加到DataFrame中 new_row = pd.DataFrame([append_rows], columns=df.columns.tolist()) df = pd.concat([df, new_row], ignore_index=True) else: # 更新现有数据 print('日期存在，即将更新') print('新数据',append_rows[1:]) df.loc[df['日期'] == previous_date, df.columns.tolist()[1:]] = append_rows[1:] print('更新数据后') print(df.tail(1)) # 使用pandas保存Excel文件 df.to_excel("定性模型数据项12-11.xlsx", index=False, engine='openpyxl') def updateExcelData(date='', token=None): # 使用pandas读取Excel文件 df = pd.read_excel(read_file_path_name, engine='openpyxl') # 获取第一行的数据作为列名 # one_cols = df.columns.tolist() # 获取第二行的数据作为列名 one_cols = df.iloc[0,:].tolist() cur_time, cur_time2 = getNow(date) search_data = { "data": { "date": cur_time, "dataItemNoList": one_cols[1:] }, "funcModule": "数据项", "funcOperation": "查询" } headers = {"Authorization": token} search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5)) search_value = json.loads(search_res.text)["data"] if search_value: datas = search_value else: datas = None append_rows = [cur_time2] dataItemNo_dataValue = {} for data_value in datas: if "dataValue" not in data_value: print(data_value) dataItemNo_dataValue[data_value["dataItemNo"]] = "" else: dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"] for value in one_cols[1:]: if value in dataItemNo_dataValue: append_rows.append(dataItemNo_dataValue[value]) else: append_rows.append("") # 将新的数据添加到DataFrame中 new_row = pd.DataFrame([append_rows], columns=df.columns.tolist()) df = pd.concat([df, new_row], ignore_index=True) # df = df.append(pd.Series(append_rows), ignore_index=True) # 使用pandas保存Excel文件 df.to_excel("定性模型数据项12-11.xlsx", index=False, engine='openpyxl') def qualitativeModel(): df = pd.read_excel('定性模型数据项12-11.xlsx') df=df.fillna(df.ffill()) df1 = df[-3:-1].reset_index() print(df1) ''' # if df1.loc[1,'70号沥青开工率'] > 0.3: 2025年1月8日 修改： 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 ''' if df1.loc[1,'70号沥青开工率'] / 100 > 0.3: a = -(df1.loc[1,'70号沥青开工率'] / 100 -0.2)*5/0.1 else : a = 0 b = df1.loc[1,'资金因素'] print('昨日计划提货偏差改之前',df1.loc[1,'昨日计划提货偏差']) # 昨日计划提货偏差 = 京博产量 - 计划产量 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'] print('昨日计划提货偏差改之后',df1.loc[1,'昨日计划提货偏差']) if df1.loc[1,'昨日计划提货偏差']>0: c = df1.loc[1,'昨日计划提货偏差']*10/2000 else : c = df1.loc[1,'昨日计划提货偏差']*10/3000 # 生产情况 = (京博产量 - 计划产量)/500*5 d = (df1.loc[1,'京博产量'] - df1.loc[1,'计划产量']) / 500 * 5 if df1.loc[1,'基质沥青库存']/265007 >0.8: e = (df1.loc[1,'基质沥青库存'] - df1.loc[0,'基质沥青库存'])*10/-5000 else : e = 0 # f = df1.loc[1,'下游客户价格预期'] f = 1 # 2025年1月23日修改：价格预期都按1计算 if abs(df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])>=100: g = (df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])*50/100 else : g = 0 h = df1.loc[1,'订单结构'] 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) return x def getNow(date='',offset=0): if date == '': now = datetime.now() - timedelta(days=offset) else: try: date = datetime.strptime(date, "%Y-%m-%d") except: date = datetime.strptime(date, "%Y%m%d") now = date year = now.year month = now.month day = now.day if month < 10: month = "0" + str(month) if day < 10: day = "0" + str(day) cur_time = str(year) + str(month) + str(day) cur_time2 = str(year) + "-" + str(month) + "-" + str(day) return cur_time,cur_time2 def pushData(cur_time,x,token_push): data1 = { "funcModule": "数据表信息列表", "funcOperation": "新增", "data": [ {"dataItemNo": "C01100036|Forecast_Price|DX|ACN", "dataDate": cur_time, "dataStatus": "add", "dataValue": x } ] } headers1 = {"Authorization": token_push} res = requests.post(url=upload_url, headers=headers1, json=data1, timeout=(3, 5)) def start_2(date='',token=None): workbook = xlrd.open_workbook(read_file_path_name) # 选择第一个表格 sheet = workbook.sheet_by_index(0) # 获取行数和列数 num_rows = sheet.nrows row_data = sheet.row_values(1) one_cols = row_data cur_time,cur_time2 = getNow(date) search_data = { "data": { "date": cur_time, "dataItemNoList": one_cols[1:] }, "funcModule": "数据项", "funcOperation": "查询" } headers = {"Authorization": token} search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5)) search_value = json.loads(search_res.text)["data"] # datas = search_value if search_value: datas = search_value else : datas = None append_rows = [cur_time2] dataItemNo_dataValue = {} # for data_value in datas: # dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"] for data_value in datas: if "dataValue" not in data_value: print(data_value) dataItemNo_dataValue[data_value["dataItemNo"]] = "" else: dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"] for value in one_cols[1:]: if value in dataItemNo_dataValue: append_rows.append(dataItemNo_dataValue[value]) else: append_rows.append("") workbook = xlrd.open_workbook('定性模型数据项12-11.xlsx') # 获取所有sheet的个数 sheet_count = len(workbook.sheet_names()) # 获取所有sheet的名称 sheet_names = workbook.sheet_names() new_workbook = xlwt.Workbook() for i in range(sheet_count): # 获取当前sheet sheet = workbook.sheet_by_index(i) # 获取sheet的行数和列数 row_count = sheet.nrows col_count = sheet.ncols # 获取原有数据 data = [] for row in range(row_count): row_data = [] for col in range(col_count): row_data.append(sheet.cell_value(row, col)) data.append(row_data) # 创建xlwt的Workbook对象 # 创建sheet new_sheet = new_workbook.add_sheet(sheet_names[i]) # 将原有的数据写入新的sheet for row in range(row_count): for col in range(col_count): new_sheet.write(row, col, data[row][col]) if i == 0: # 在新的sheet中添加数据 for col in range(col_count): new_sheet.write(row_count, col, append_rows[col]) # 保存新的xls文件 new_workbook.save("定性模型数据项12-11.xlsx") update_e_value('定性模型数据项12-11.xlsx', 8, 1000) df = pd.read_excel('定性模型数据项12-11.xlsx') df=df.fillna(df.ffill()) df1 = df[-2:].reset_index() ''' # if df1.loc[1,'70号沥青开工率'] > 0.3: 2025年1月8日 修改： 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 ''' if df1.loc[1,'70号沥青开工率'] > 30: a = (df1.loc[1,'70号沥青开工率']-0.2)*5/0.1 else : a = 0 b = df1.loc[1,'资金因素'] if df1.loc[1,'昨日计划提货偏差']>0: c = df1.loc[1,'昨日计划提货偏差']*10/2000 else : c = df1.loc[1,'昨日计划提货偏差']*10/3000 d = df1.loc[1,'生产情况'] if df1.loc[1,'基质沥青库存']/265007 >0.8: e = (df1.loc[1,'基质沥青库存'] - df1.loc[0,'基质沥青库存'])*10/-5000 else : e = 0 # f = df1.loc[1,'下游客户价格预期'] f = 1 # 2025年1月23日修改：价格预期都按1计算 if abs(df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])>=100: g = (df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])*50/100 else : g = 0 h = df1.loc[1,'订单结构'] 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) login_res1 = requests.post(url=login_push_url, json=login_push_data, timeout=(3, 5)) text1 = json.loads(login_res1.text) token_push = text1["data"]["accessToken"] data1 = { "funcModule": "数据表信息列表", "funcOperation": "新增", "data": [ {"dataItemNo": "C01100036|Forecast_Price|DX|ACN", "dataDate": cur_time, "dataStatus": "add", "dataValue": x } ] } headers1 = {"Authorization": token_push} # res = requests.post(url=upload_url, headers=headers1, json=data1, timeout=(3, 5)) def start(now=None): workbook = xlrd.open_workbook(read_file_path_name) # 选择第一个表格 sheet = workbook.sheet_by_index(0) # 获取行数和列数 num_rows = sheet.nrows row_data = sheet.row_values(1) one_cols = row_data login_res = requests.post(url=login_url, json=login_data, timeout=(3, 5)) text = json.loads(login_res.text) if text["status"]: token = text["data"]["accessToken"] else: print("获取认证失败") token = None if now is None: now = datetime.now() year = now.year month = now.month day = now.day if month < 10: month = "0" + str(month) if day < 10: day = "0" + str(day) cur_time = str(year) + str(month) + str(day) cur_time2 = str(year) + "-" + str(month) + "-" + str(day) search_data = { "data": { "date": cur_time, "dataItemNoList": one_cols[1:] }, "funcModule": "数据项", "funcOperation": "查询" } headers = {"Authorization": token} search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5)) search_value = json.loads(search_res.text)["data"] # datas = search_value if search_value: datas = search_value else : datas = None append_rows = [cur_time2] dataItemNo_dataValue = {} # for data_value in datas: # dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"] for data_value in datas: if "dataValue" not in data_value: print(data_value) dataItemNo_dataValue[data_value["dataItemNo"]] = "" else: dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"] for value in one_cols[1:]: if value in dataItemNo_dataValue: append_rows.append(dataItemNo_dataValue[value]) else: append_rows.append("") workbook = xlrd.open_workbook('定性模型数据项12-11.xlsx') # 获取所有sheet的个数 sheet_count = len(workbook.sheet_names()) # 获取所有sheet的名称 sheet_names = workbook.sheet_names() new_workbook = xlwt.Workbook() for i in range(sheet_count): # 获取当前sheet sheet = workbook.sheet_by_index(i) # 获取sheet的行数和列数 row_count = sheet.nrows col_count = sheet.ncols # 获取原有数据 data = [] for row in range(row_count): row_data = [] for col in range(col_count): row_data.append(sheet.cell_value(row, col)) data.append(row_data) # 创建xlwt的Workbook对象 # 创建sheet new_sheet = new_workbook.add_sheet(sheet_names[i]) # 将原有的数据写入新的sheet for row in range(row_count): for col in range(col_count): new_sheet.write(row, col, data[row][col]) if i == 0: # 在新的sheet中添加数据 for col in range(col_count): new_sheet.write(row_count, col, append_rows[col]) # 保存新的xls文件 new_workbook.save("定性模型数据项12-11.xlsx") update_e_value('定性模型数据项12-11.xlsx', 8, 1000) df = pd.read_excel('定性模型数据项12-11.xlsx') df=df.fillna(df.ffill()) df1 = df[-2:].reset_index() print(df1) # if df1.loc[1,'70号沥青开工率'] > 0.3: -- 2025年1月9日 发版更改 if df1.loc[1,'70号沥青开工率'] / 100 > 0.3: a = (df1.loc[1,'70号沥青开工率'] / 100 -0.2)*5/0.1 else : a = 0 b = df1.loc[1,'资金因素'] if df1.loc[1,'昨日计划提货偏差']>0: c = df1.loc[1,'昨日计划提货偏差']*10/2000 else : c = df1.loc[1,'昨日计划提货偏差']*10/3000 d = df1.loc[1,'生产情况'] if df1.loc[1,'基质沥青库存']/265007 >0.8: e = (df1.loc[1,'基质沥青库存'] - df1.loc[0,'基质沥青库存'])*10/-5000 else : e = 0 # f = df1.loc[1,'下游客户价格预期'] f = 1 # 2025年1月23日修改：价格预期都按1计算 if abs(df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])>=100: g = (df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])*50/100 else : g = 0 h = df1.loc[1,'订单结构'] 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) # login_res1 = requests.post(url=login_url, json=login_data, timeout=(3, 30)) # text1 = json.loads(login_res1.text) # token_push = text1["data"]["accessToken"] # data1 = { # "funcModule": "数据表信息列表", # "funcOperation": "新增", # "data": [ # {"dataItemNo": "C01100036|Forecast_Price|DX|ACN", # "dataDate": cur_time, # "dataStatus": "add", # "dataValue": x # } # ] # } # headers1 = {"Authorization": token_push} # res = requests.post(url=upload_url, headers=headers1, json=data1, timeout=(3, 5)) def start_test(): workbook = xlrd.open_workbook(read_file_path_name) # 选择第一个表格 sheet = workbook.sheet_by_index(0) # 获取行数和列数 num_rows = sheet.nrows row_data = sheet.row_values(1) one_cols = row_data login_res = requests.post(url=login_url, json=login_data, timeout=(3, 5)) text = json.loads(login_res.text) if text["status"]: token = text["data"]["accessToken"] else: print("获取认证失败") token = None now = datetime.now() year = now.year month = now.month day = now.day if month < 10: month = "0" + str(month) if day < 10: day = "0" + str(day) cur_time = str(year) + str(month) + str(day) cur_time2 = str(year) + "-" + str(month) + "-" + str(day) search_data = { "data": { "date": cur_time, "dataItemNoList": one_cols[1:] }, "funcModule": "数据项", "funcOperation": "查询" } headers = {"Authorization": token} search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5)) search_value = json.loads(search_res.text)["data"] # datas = search_value if search_value: datas = search_value else : datas = None append_rows = [cur_time2] dataItemNo_dataValue = {} # for data_value in datas: # dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"] for data_value in datas: if "dataValue" not in data_value: print(data_value) dataItemNo_dataValue[data_value["dataItemNo"]] = "" else: dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"] for value in one_cols[1:]: if value in dataItemNo_dataValue: append_rows.append(dataItemNo_dataValue[value]) else: append_rows.append("") workbook = xlrd.open_workbook('定性模型数据项12-11.xlsx') # 获取所有sheet的个数 sheet_count = len(workbook.sheet_names()) # 获取所有sheet的名称 sheet_names = workbook.sheet_names() new_workbook = xlwt.Workbook() for i in range(sheet_count): # 获取当前sheet sheet = workbook.sheet_by_index(i) # 获取sheet的行数和列数 row_count = sheet.nrows col_count = sheet.ncols # 获取原有数据 data = [] for row in range(row_count): row_data = [] for col in range(col_count): row_data.append(sheet.cell_value(row, col)) data.append(row_data) # 创建xlwt的Workbook对象 # 创建sheet new_sheet = new_workbook.add_sheet(sheet_names[i]) # 将原有的数据写入新的sheet for row in range(row_count): for col in range(col_count): new_sheet.write(row, col, data[row][col]) if i == 0: # 在新的sheet中添加数据 for col in range(col_count): new_sheet.write(row_count, col, append_rows[col]) # 保存新的xls文件 new_workbook.save("定性模型数据项12-11.xlsx") update_e_value('定性模型数据项12-11.xlsx', 8, 1000) df = pd.read_excel('定性模型数据项12-11.xlsx') df=df.fillna(df.ffill()) df1 = df[-2:].reset_index() # if df1.loc[1,'70号沥青开工率'] > 0.3: -- 2025年1月9日 发版更改 if df1.loc[1,'70号沥青开工率'] / 100 > 0.3: a = (df1.loc[1,'70号沥青开工率'] / 100 -0.2)*5/0.1 else : a = 0 b = df1.loc[1,'资金因素'] if df1.loc[1,'昨日计划提货偏差']>0: c = df1.loc[1,'昨日计划提货偏差']*10/2000 else : c = df1.loc[1,'昨日计划提货偏差']*10/3000 d = df1.loc[1,'生产情况'] if df1.loc[1,'基质沥青库存']/265007 >0.8: e = (df1.loc[1,'基质沥青库存'] - df1.loc[0,'基质沥青库存'])*10/-5000 else : e = 0 # f = df1.loc[1,'下游客户价格预期'] f = 1 # 2025年1月23日修改：价格预期都按1计算 if abs(df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])>=100: g = (df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])*50/100 else : g = 0 h = df1.loc[1,'订单结构'] 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) # login_res1 = requests.post(url=login_url, json=login_data, timeout=(3, 30)) # text1 = json.loads(login_res1.text) # token_push = text1["data"]["accessToken"] # data1 = { # "funcModule": "数据表信息列表", # "funcOperation": "新增", # "data": [ # {"dataItemNo": "C01100036|Forecast_Price|DX|ACN", # "dataDate": cur_time, # "dataStatus": "add", # "dataValue": x # } # ] # } # headers1 = {"Authorization": token_push} # res = requests.post(url=upload_url, headers=headers1, json=data1, timeout=(3, 5)) def start_1(): workbook = xlrd.open_workbook(read_file_path_name) # 选择第一个表格 sheet = workbook.sheet_by_index(0) # 获取行数和列数 num_rows = sheet.nrows row_data = sheet.row_values(1) one_cols = row_data login_res = requests.post(url=login_url, json=login_data, timeout=(3, 5)) text = json.loads(login_res.text) if text["status"]: token = text["data"]["accessToken"] else: print("获取认证失败") token = None now = datetime.now() - timedelta(days=1) year = now.year month = now.month day = now.day if month < 10: month = "0" + str(month) if day < 10: day = "0" + str(day) cur_time = str(year) + str(month) + str(day) cur_time2 = str(year) + "-" + str(month) + "-" + str(day) search_data = { "data": { "date": cur_time, "dataItemNoList": one_cols[1:] }, "funcModule": "数据项", "funcOperation": "查询" } headers = {"Authorization": token} search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5)) search_value = json.loads(search_res.text)["data"] # datas = search_value if search_value: datas = search_value else : datas = None append_rows = [cur_time2] dataItemNo_dataValue = {} # for data_value in datas: # dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"] for data_value in datas: if "dataValue" not in data_value: print(data_value) dataItemNo_dataValue[data_value["dataItemNo"]] = "" else: dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"] for value in one_cols[1:]: if value in dataItemNo_dataValue: append_rows.append(dataItemNo_dataValue[value]) else: append_rows.append("") workbook = xlrd.open_workbook('定性模型数据项12-11.xlsx') # 获取所有sheet的个数 sheet_count = len(workbook.sheet_names()) # 获取所有sheet的名称 sheet_names = workbook.sheet_names() new_workbook = xlwt.Workbook() for i in range(sheet_count): # 获取当前sheet sheet = workbook.sheet_by_index(i) # 获取sheet的行数和列数 row_count = sheet.nrows - 1 col_count = sheet.ncols # 获取原有数据 data = [] for row in range(row_count): row_data = [] for col in range(col_count): row_data.append(sheet.cell_value(row, col)) data.append(row_data) # 创建xlwt的Workbook对象 # 创建sheet new_sheet = new_workbook.add_sheet(sheet_names[i]) # 将原有的数据写入新的sheet for row in range(row_count): for col in range(col_count): new_sheet.write(row, col, data[row][col]) if i == 0: # 在新的sheet中添加数据 for col in range(col_count): new_sheet.write(row_count, col, append_rows[col]) # 保存新的xls文件 new_workbook.save("定性模型数据项12-11.xlsx") def get_queryDataListItemNos_value(token, url, dataItemNoList, dateStart, dateEnd): search_data = { "funcModule": "数据项", "funcOperation": "查询", "data": { "dateStart": dateStart, "dateEnd": dateEnd, "dataItemNoList": dataItemNoList # 数据项编码,代表 brent最低价和最高价 } } headers = {"Authorization": token} search_res = requests.post(url=url, headers=headers, json=search_data, timeout=(3, 5)) search_value = json.loads(search_res.text)["data"] if search_value: return search_value else: return None def save_queryDataListItemNos_xls(data_df,dataItemNoList): current_year_month = datetime.now().strftime('%Y-%m') grouped = data_df.groupby("dataDate") df_old = pd.read_excel('定性模型数据项12-11.xlsx') df_old0 = df_old[:1] result_dict = {df_old0.iloc[0][col] : col for col in df_old0.columns} df_old1 = df_old[1:].copy() df_old1["日期"] = pd.to_datetime(df_old1["日期"]) # 删除日期列为本月的数据 df_old1 = df_old1[~df_old1["日期"].dt.strftime('%Y-%m').eq(current_year_month)] df_old1["日期"] = df_old1["日期"].dt.strftime('%Y-%m-%d') list_data = [] for date, group in grouped: dict_data = {"日期": date} for index, row in group.iterrows(): dict_data[result_dict[row['dataItemNo']]] = row['dataValue'] list_data.append(dict_data) df_current_year_month = pd.DataFrame(list_data) df_current_year_month df_merged = pd.concat([df_old0, df_old1, df_current_year_month], ignore_index=True) df_merged.to_excel('定性模型数据项12-11.xlsx', index=False) def queryDataListItemNos(date=None,token=None): df = pd.read_excel('定性模型数据项12-11.xlsx') dataItemNoList = df.iloc[0].tolist()[1:] if token is None: token = getLogToken() if token is None: print("获取token失败") return # 获取当前日期 if date is None: date = datetime.now() current_date = date # 获取当月1日 first_day_of_month = current_date.replace(day=1) # 格式化为 YYYYMMDD 格式 dateEnd = current_date.strftime('%Y%m%d') dateStart = first_day_of_month.strftime('%Y%m%d') search_value = get_queryDataListItemNos_value(token, queryDataListItemNos_url, dataItemNoList, dateStart, dateEnd) # print("search_value",search_value) data_df = pd.DataFrame(search_value) data_df["dataDate"] = pd.to_datetime(data_df["dataDate"]) data_df["dataDate"] = data_df["dataDate"].dt.strftime('%Y-%m-%d') save_queryDataListItemNos_xls(data_df,dataItemNoList) def main(start_date=None,token=None,token_push=None): if start_date is None: start_date = datetime.now() if token is None: token = get_head_auth() if token_push is None: token_push = get_head_push_auth() date = start_date.strftime('%Y%m%d') print(date) updateExcelData(date,token) queryDataListItemNos(token=token) update_e_value('定性模型数据项12-11.xlsx', 8, 1000) x = qualitativeModel() print('**************************************************预测结果：',x) cur_time,cur_time2 = getNow(date) pushData(cur_time,x,token) if __name__ == "__main__": print("运行中...") main()