d

rpt_result/GetFIinfoFromJson.py

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Time    : 2020/2/11 19:43
+# @Author  : fanhui
+# @File    : GetFIinfoFromJson.py
+# @Software: PyCharm
+
+#变化率添加符号
+def AddSign(factor):
+    factor=factor
+    if "-" in factor and"%"in factor:
+        factor=factor.replace("-", "") + "↓"
+    elif "%"in factor and "-"not in factor:
+        factor = factor+"↑"
+    # print(factor)
+    return factor
+
+
+def GetFIinfoFromJson(GetAllinfo):
+# 获取传入的参数
+    GetAllinfo = GetAllinfo
+    Else_info={
+        "Detection_zone": GetAllinfo.get("Detection_zone",None),
+        "CreatTime": GetAllinfo.get("CreatTime",None),
+        "company_name": GetAllinfo.get("company_name",None),
+        "Task_Num": GetAllinfo.get("Task_Num",None)
+    }
+
+    FIinfo=GetAllinfo.get("fi",None)#如果key不存在，会返回None,因此需进行判断
+    Fi_dict={}
+    if FIinfo:
+        Total_trade_income=FIinfo[0].get("Total_trade_income",None)
+        Bq_time=''
+        Sq_time=''
+        if Total_trade_income:
+            print(eval(Total_trade_income),type(eval(Total_trade_income)))
+            keys_list = []
+            Total_trade_income=eval(Total_trade_income)#str转dict,用eval
+            for t_k,t_v in Total_trade_income.items():
+                keys_list.append(t_k)
+            Bq_time=keys_list[0]
+            Sq_time=keys_list[1]
+        Total_profit = eval(FIinfo[1].get("Total_profit"))
+        Net_profit = eval(FIinfo[2].get("Net_profit"))
+        Total_Liabilities = eval(FIinfo[3].get("Total_Liabilities"))
+        Owner_rights = eval(FIinfo[4].get("Owner_rights"))
+        Total_sales = eval(FIinfo[5].get("Total_sales"))
+        Total_assets = eval(FIinfo[6].get("Total_assets"))
+        Total_liabilities_and_Owner_rights = eval(FIinfo[7].get("Total_liabilities_and_Owner_rights"))
+        sales_expense=eval(FIinfo[8].get("sales_expense"))
+        Net_interest_rate = eval(FIinfo[9].get("Net_interest_rate"))
+        Equity_ratio = eval(FIinfo[10].get("Equity_ratio"))
+        Gearing_ratio = eval(FIinfo[11].get("Gearing_ratio"))
+        sales_growth_rate = eval(FIinfo[12].get("sales_growth_rate"))
+        Net_profit_growth_rate = eval(FIinfo[13].get("Net_profit_growth_rate"))
+        print(type(FIinfo[14].get("total_assets_growth_rate")),FIinfo[14].get("total_assets_growth_rate"))
+        total_assets_growth_rate = eval(FIinfo[14].get("total_assets_growth_rate"))
+        Fi_dict["Bq_time"] = Bq_time
+        Fi_dict["Sq_time"] = Sq_time
+        Fi_dict["Bq_Total_trade_income"]=Total_trade_income[Bq_time]
+        Fi_dict["Sq_Total_trade_income"] = Total_trade_income[Sq_time]
+        Fi_dict["change_rate_1"] = AddSign(Total_trade_income["change_rate"])
+        Fi_dict["Bq_Total_profit"]=Total_profit[Bq_time]
+        Fi_dict["Sq_Total_profit"]=Total_profit[Sq_time]
+        Fi_dict["change_rate_2"]=AddSign(Total_profit["change_rate"])
+        Fi_dict["Bq_Net_profit"]=Net_profit[Bq_time]
+        Fi_dict["Sq_Net_profit"]=Net_profit[Sq_time]
+        Fi_dict["change_rate_3"]=AddSign(Net_profit["change_rate"])
+        Fi_dict["Bq_Total_Liabilities"]=Total_Liabilities[Bq_time]
+        Fi_dict["Sq_Total_Liabilities"]=Total_Liabilities[Sq_time]
+        Fi_dict["change_rate_4"]=AddSign(Total_Liabilities["change_rate"])
+        Fi_dict["Bq_Owner_rights"]=Owner_rights[Bq_time]
+        Fi_dict["Sq_Owner_rights"]=Owner_rights[Sq_time]
+        Fi_dict["change_rate_5"]=AddSign(Owner_rights["change_rate"])
+        Fi_dict["Bq_Total_sales"]=Total_sales[Bq_time]
+        Fi_dict["Sq_Total_sales"]=Total_sales[Sq_time]
+        Fi_dict["change_rate_6"]=AddSign(Total_sales["change_rate"])
+        Fi_dict["Bq_Total_assets"]=Total_assets[Bq_time]
+        Fi_dict["Sq_Total_assets"]=Total_assets[Sq_time]
+        Fi_dict["change_rate_7"]=AddSign(Total_assets["change_rate"])
+        Fi_dict["Bq_Total_liabilities_and_Owner_rights"]=Total_liabilities_and_Owner_rights[Bq_time]
+        Fi_dict["Sq_Total_liabilities_and_Owner_rights"]=Total_liabilities_and_Owner_rights[Sq_time]
+        Fi_dict["Bq_sales_expense"] = sales_expense[Bq_time]
+        Fi_dict["Sq_sales_expense"] = sales_expense[Sq_time]
+        Fi_dict["Bq_Net_interest_rate"] = Net_interest_rate[Bq_time]
+        Fi_dict["Sq_Net_interest_rate"] = Net_interest_rate[Sq_time]
+        Fi_dict["Bq_Equity_ratio"] = Equity_ratio[Bq_time]
+        Fi_dict["Sq_Equity_ratio"] = Equity_ratio[Sq_time]
+        Fi_dict["Bq_Gearing_ratio"] = Gearing_ratio[Bq_time]
+        Fi_dict["Sq_Gearing_ratio"] = Gearing_ratio[Sq_time]
+        Fi_dict["Bq_sales_growth_rate"] = sales_growth_rate[Bq_time]
+        Fi_dict["Sq_sales_growth_rate"] = sales_growth_rate[Sq_time]
+        Fi_dict["Bq_Net_profit_growth_rate"] = Net_profit_growth_rate[Bq_time]
+        Fi_dict["Sq_Net_profit_growth_rate"] = Net_profit_growth_rate[Sq_time]
+        Fi_dict["Bq_total_assets_growth_rate"] = total_assets_growth_rate[Bq_time]
+        Fi_dict["Sq_total_assets_growth_rate"] = total_assets_growth_rate[Sq_time]
+    return Else_info,Fi_dict

rpt_result/GetRiskinfoFromJson.py

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Time    : 2019/12/26 17:33
+# @Author  : fanhui
+# @File    : sql_tax_test.py
+# @Software: PyCharm
+
+import os, sys
+from flask import request, Flask,jsonify
+from flask_cors import *
+import json
+
+server = Flask(__name__)
+interface_path = os.path.dirname(__file__)
+sys.path.insert(0, interface_path)  # 将当前文件的父目录加入临时系统变量
+server.config['JSON_AS_ASCII'] = False
+CORS(server, supports_credentials=True)  # 跨域请求
+
+# 获取传入的参数，建立大字典
+def CreatBdictFromJson(GetAllinfo):
+    # 读取指标文件，获取权重及描述信息
+    file = open('tax_index.txt', 'r', encoding='utf-8')
+    lines = file.readlines()
+    dict_all = {}
+    for line in lines:
+        linesplit = line.strip().split('\t')
+        tax_index = {
+            "factors_code": linesplit[3].replace('"', ""),
+            "factors_name": linesplit[4].replace('"', ""),
+            "s_score": int(linesplit[5].replace('"', "")),
+            "factors_describe": linesplit[6].replace('"', ""),
+            "status": 0
+        }
+        dict_all[linesplit[4].replace('"', "")] = tax_index
+    # print(dict_all)
+    # 获取传入的参数
+    GetAllinfo = GetAllinfo
+    GetAllinfo_new = eval(GetAllinfo.get("ri", None))  # 如果key不存在，会返回None,因此需进行判断
+    for factors_name, value in dict_all.items():
+        try:
+            dict_all[factors_name]["status"] = GetAllinfo_new[factors_name]
+        except:
+            pass
+    # print(dict_all)
+
+    # 计算总体风险异常比例、以及被监控风险比例和被稽查风险比例
+    qualified = []  # 合格
+    disqualified = []  # 不合格
+    for k, v in dict_all.items():
+        s_status = v['status']
+        s_score = v['s_score']
+        if s_status == 1:
+            qualified.append(s_score)
+        if s_status == 2:
+            disqualified.append(s_score)
+    A_count = sum(qualified) + sum(disqualified)  # 计算所有合格与不合格对应指标权重之和（分母）
+    B_count = sum(disqualified)  # 计算所有不合格对应指标权重之和（分子）
+    r1 = float("%.2f" % (B_count / A_count))  # 计算风险异常比例
+    print(r1, type(r1))
+    t_r1 = str(int(r1 * 100)) + "%"
+    t_r2 = str(int((r1 - 0.29) * 100)) + "%"
+    t_r3 = str(int((r1 - 0.49) * 100)) + "%"
+    standard1 = 0.3
+    standard2 = 0.5
+    # 建立第一层规则
+    rules = {r1 <= standard1: {"t_r1": t_r1, "t_r2": "1%", "t_r3": "1%"},
+             r1 > standard1 and r1 <= standard2: {"t_r1": t_r1, "t_r2": t_r2, "t_r3": "1%"},
+             r1 > standard2: {"t_r1": t_r1, "t_r2": t_r2, "t_r3": t_r3}}
+    ALLRisk = {}
+    for k1, v1 in rules.items():
+        if k1:
+            ALLRisk = v1
+    print(ALLRisk,"+++++++++++++")
+    return dict_all, ALLRisk
+
+
+# 输入计算出的风险异常比例，返回提示结果
+def RiskAbnormal(t_r1):
+    RiskA = {}
+    t_r1_s = str(int(t_r1.replace("%", "")) / 100)
+    rules_1 = {"t_r1_s<='0.3'": "企业风险异常同比其他企业比例较低",
+               "t_r1_s>'0.3'": "企业风险异常同比其他企业比例明显偏高"
+               }
+    for k1, v1 in rules_1.items():
+        if eval(str(k1)):
+            RiskA = {"score":t_r1,"describe":v1}
+    return RiskA
+
+# 输入计算出的被检测风险值，判断被监测风险结果
+def MonitoredRisk(t_r2):
+    MRisk = {}
+    t_r2_s = str(int(t_r2.replace("%", "")) / 100)
+    rules_2 = {"t_r2_s<='0.1'": "企业被监控的风险同比其他企业比例较低",
+               "t_r2_s>'0.1' and t_r2_s<='0.5'": "经检测，企业有一定可能性被选为税务监控对象",
+               "t_r2_s>'0.5'": "经检测，企业有很大可能性被选为税务监控对象"
+               }
+    for k2, v2 in rules_2.items():
+        if eval(str(k2)):
+            MRisk = {"score": t_r2, "describe": v2}
+    return MRisk
+
+# 输入计算出的被稽查风险值，判断被稽查风险结果
+def AuditedRisk(t_r3):
+    ARisk = {}
+    t_r3_s = str(int(t_r3.replace("%", "")) / 100)
+    rules_3 = {"t_r3_s<='0.1'": "企业被税务稽查的可能性较低",
+               "t_r3_s>'0.1' and t_r3_s<='0.3'": "企业有一定可能性被税务稽查",
+               "t_r3_s>'0.3'": "企业有很大可能性被税务稽查"
+               }
+    for k3, v3 in rules_3.items():
+        if eval(str(k3)):
+            ARisk = {"score":t_r3,"describe":v3}
+    return ARisk
+
+# 输入总体计算的风险异常比例，得到提示语（也可与RiskAbnormal函数合并）
+def information(t_r1):
+    info = {}
+    t_r1_s = str(int(t_r1.replace("%", "")) / 100)
+    rules_0 = {"t_r1_s<='0.3'": "贵企业的风险异常情况同比其他企业较少，如需更多更详细的诊断服务，请联系我们>>",
+               "t_r1_s>'0.3' and t_r1_s<='0.6'": "企业的风险异常情况同比其他企业明显偏多，有较大可能性成为税务系统监控的对象，请企业的财税人员参考以下详细检测情况进行自查，如需进行更详细的诊断服务请联系我们>>",
+               "t_r1_s>'0.6' and t_r1_s<='0.8'": "如果税务局选择企业所在行业进行行业稽查，该企业有一定可能性成为被选案例，请财税人员尽快参照以下比例进行自查，如需进行更详细的诊断服务请联系我们>>",
+               "t_r1_s>'0.8'": "如果税务局选择企业所在行业进行行业稽查，该企业有很大可能性成为被选案例，请财税人员尽快参照以下比例进行自查，如需进行更详细的诊断服务请联系我们>>"
+               }
+    for k0, v0 in rules_0.items():
+        if eval(str(k0)):
+            info = {"score":t_r1,"describe":v0}
+    return info
+
+
+# 建立不同风险监测项与指标的对应关系
+tax_desc = {
+    "IT_YCSR": ["所得税行业预警税负率",
+                "销售（营业）收入变动率",
+                "收入利润率",
+                "销售成本大于销售收入",
+                "当期新增其他应收款/销售收入>80%",
+                "期末存货大于实收资本差异幅度异常",
+                "成本费用利润率",
+                "利润总额变动率",
+                "投资利润率",
+                "预付账款为负数",
+                "应收账款为负数",
+                "应付账款为负数",
+                "总资产周转率",
+                "预收账款过大异常",
+                "资产利润率",
+                "纳税人营业外支出变动率与营业外收入变动率弹性系数异常",
+                "纳税人流动资产变动率与营业收入变动率弹性系数异常",
+                "纳税人期末存货与当期累计收入差异幅度异常",
+                "账面存货率过高将会导致系统预警",
+                "收入负担变动率",
+                "应税所得率变动率",
+                "应纳税所得额变动率",
+                "行业预警利润率",
+                "主营业务收入变动率",
+                "主营业务成本变动率",
+                "其他业务利润变动率",
+                "主营业务利润变动率",
+                "纳税人主营业务收入变动率与主营业务利润变动率弹性系数异常",
+                "纳税人主营业务收入变动率与主营业务成本变动率弹性系数异常"],
+    "IT_XZCB": ["所得税行业预警税负率",
+                "销售成本大于销售收入",
+                "成本费用利润率",
+                "期末存货大于实收资本差异幅度异常",
+                "收入成本率",
+                "产品销售（营业）成本变动率",
+                "纳税人期末存货与当期累计收入差异幅度异常",
+                "账面存货率过高将会导致系统预警",
+                "总资产周转率",
+                "资产负债率",
+                "主营业务成本变动率",
+                "纳税人固定资产综合折旧率变动异常将会导致系统预警",
+                "纳税人无形资产综合摊销率变动异常",
+                "高比例出口的亏损企业",
+                "收入负担变动率",
+                "应税所得率变动率",
+                "应纳税所得额变动率",
+                "行业预警利润率",
+                "其他业务利润变动率",
+                "主营业务利润变动率",
+                "纳税人主营业务收入变动率与主营业务利润变动率弹性系数异常"],
+    "IT_XZFY": ["所得税行业预警税负率",
+                "销售成本大于销售收入",
+                "成本费用利润率",
+                "期末存货大于实收资本差异幅度异常",
+                "收入费用率",
+                "期间费用变动率",
+                "纳税人期末存货与当期累计收入差异幅度异常",
+                "账面存货率过高将会导致系统预警",
+                "总资产周转率",
+                "资产负债率",
+                "成本费用率",
+                "费用率变动率",
+                "营业（管理、财务）费用变动率",
+                "主营业务成本变动率",
+                "高比例出口的亏损企业",
+                "收入负担变动率",
+                "应税所得率变动率",
+                "应纳税所得额变动率",
+                "行业预警利润率",
+                "其他业务利润变动率",
+                "主营业务利润变动率",
+                "纳税人主营业务收入变动率与主营业务利润变动率弹性系数异常",
+                "纳税人期间费用变动率与主营业务收入变动率弹性系数异常",
+                "广告费和业务宣传费异常",
+                "业务招待费异常"],
+    "VAT_XKFP": ["销售额变动率",
+                 "销售毛利（益）率",
+                 "税收负担变动率",
+                 "税收负担率",
+                 "增值税专用发票用量变动异常",
+                 "增值税一股纳税人税负变动异常",
+                 "增值税普通发票用量变动异常",
+                 "进项税额变动率∶应纳税额变动率"],
+    "VAT_YCXX": ["销售额变动率",
+                 "税收负担变动率",
+                 "税收负担率",
+                 "存货变动率∶销售成本变动率",
+                 "存货变动率∶销售收入变动率",
+                 "存货周转率∶资金周转率",
+                 "增值税一股纳税人税负变动异常",
+                 "纳税人销售额变动率与应纳税额变动率弹性系数异常",
+                 "进项税额变动率∶应纳税额变动率",
+                 "应收账款变动率∶销项税额变动率",
+                 "预收账款大于零且存货大于预收账款又无留抵",
+                 "进项税额变动率高于销售税额变动率",
+                 "在建工程增加额",
+                 "长期投资增加额分析",
+                 "企业期末预收账款变动率与销售收入变动率弹性系数异常",
+                 "纳税人销售毛利率变动率与税负率变动率弹性系统异常"],
+    "VAT_XZJX": ["销售毛利（益）率",
+                 "税收负担变动率",
+                 "税收负担率",
+                 "增值税一股纳税人税负变动异常",
+                 "纳税人销售额变动率与应纳税额变动率弹性系数异常",
+                 "应付账款变动率∶进项税额变动率",
+                 "进项税额变动率高于销售税额变动率",
+                 "在建工程增加额",
+                 "长期投资增加额分析",
+                 "企业期末预收账款变动率与销售收入变动率弹性系数异常",
+                 "应税销售额变动率",
+                 "纳税人销售毛利率变动率与税负率变动率弹性系统异常"],
+    "CT_FX": ["其他应付款变动额",
+              "应付帐款变动额",
+              "营业外支出变动额",
+              "长期投资变动额"],
+    "ALLT_FX": ["消费税申报收入与增值税申报收入差异",
+                "所得税申报收入与增值税申报收入差异",
+                "长期零申报"]
+}
+
+
+# 建立细分税种风险类别（根据传入的不合格指标之和进行比较）
+def RiskCompareRule(wx_part):
+    risk_describe = ''
+    if wx_part and wx_part != None:
+        risk_relus = {
+            wx_part >= 5 : "高危风险",
+            # wx_part >= 5 and wx_part <= 10: "高危风险",
+            wx_part >= 3 and wx_part <= 4: "重度风险",
+            wx_part == 2: "中度风险",
+            wx_part <= 1: "轻微风险"
+        }
+        for k, v in risk_relus.items():
+            if k:
+                risk_describe = v
+    else:
+        risk_describe = "数据缺失"
+    return risk_describe
+#风险描述去重
+def getUniqueItems(iterable):
+    seen = set()
+    result = []
+    for item in iterable:
+        if item not in seen:
+            seen.add(item)
+            result.append(item)
+    return result
+
+
+# 计算所得税隐藏收入风险异常(对所属指标不合格降序排列，展示前三的风险描述信息，计算不合格总和及风险比例）
+def IT_YCSR_Risk(dict_all):
+    IT_YCSR_list = tax_desc.get("IT_YCSR")  #获取所得税隐藏收入指标名称
+    IT_YCSR_dict = {}  #建立所得税隐藏收入字典
+    for f1 in IT_YCSR_list:
+        IT_YCSR_dict[f1] = dict_all[f1]
+    #判断所有指标是否为缺失
+    Risk_not_null={}
+    for k1, v1 in IT_YCSR_dict.items():
+        s_status1 = v1["status"]
+        if s_status1 >0:
+            Risk_not_null[k1]=IT_YCSR_dict[k1]
+    if Risk_not_null:
+        satisfied = []
+        dissatisfied = []
+        part_s1_str_dict = {}
+        for k_part1, v_part1 in IT_YCSR_dict.items():
+            s_s1 = v_part1["s_score"]
+            s_str = v_part1["factors_describe"]
+            s_status1 = v_part1["status"]
+            if s_status1 == 1:
+                satisfied.append(s_s1)
+            if s_status1 == 2:
+                part_s1_str_dict[s_s1] = s_str
+                dissatisfied.append(s_s1)
+        A_count = sum(satisfied) + sum(dissatisfied)  # 计算合格与不合格对应指标权重之和（分母）
+        B_count = sum(dissatisfied)  # 计算不合格对应指标权重之和（分子）
+        risk_IT_YCSR = float("%.2f" % (B_count / A_count))  # 计算风险异常比例
+        Risk_IT_YCSR_score = str(int(risk_IT_YCSR * 100)) + "%"  # 格式转化
+        part_s1_str_dict = sorted(part_s1_str_dict.items(), key=lambda x: x[0], reverse=True)  # 按照权重排序
+        p1_describe = []  # 获取风险权重前三提示
+        for pt in part_s1_str_dict:
+            p1_describe.append(pt[1])
+        p1_describe = getUniqueItems(p1_describe)  #风险描述去重
+        if len(p1_describe) > 3:
+            p1_describe = p1_describe[:3]
+        # 风险类别获取
+        risk_describe = RiskCompareRule(B_count)
+    else:
+        Risk_IT_YCSR_score="0%"
+        p1_describe="贵公司提供的检测数据不能支撑此风险项检测，您可以尝试按季度或年度检测"
+        risk_describe="数据缺失"
+    Risk_IT_YCSR={
+        "score":Risk_IT_YCSR_score,
+        "describe":p1_describe,
+        "risk":risk_describe
+    }
+    return Risk_IT_YCSR
+
+
+# 计算所得税虚增费用风险异常
+def IT_XZFY_Risk(dict_all):
+    IT_XZFY_list = tax_desc.get("IT_XZFY")  #获取所得税虚增费用指标名称
+    IT_XZFY_dict = {}  #建立所得税虚增费用字典
+    for f2 in IT_XZFY_list:
+        IT_XZFY_dict[f2] = dict_all[f2]
+    #判断所有指标是否为缺失
+    Risk_not_null={}
+    for k2, v2 in IT_XZFY_dict.items():
+        s_status2 = v2["status"]
+        if s_status2 >0:
+            Risk_not_null[k2]=IT_XZFY_dict[k2]
+    if Risk_not_null:
+        satisfied = []
+        dissatisfied = []
+        part_s2_str_dict = {}
+        for k_part2, v_part2 in IT_XZFY_dict.items():
+            s_s2 = v_part2["s_score"]
+            s_str2 = v_part2["factors_describe"]
+            s_status2 = v_part2["status"]
+            if s_status2 == 1:
+                satisfied.append(s_s2)
+            if s_status2 == 2:
+                part_s2_str_dict[s_s2] = s_str2
+                dissatisfied.append(s_s2)
+        A_count = sum(satisfied) + sum(dissatisfied)  # 计算合格与不合格对应指标权重之和（分母）
+        B_count = sum(dissatisfied)  # 计算不合格对应指标权重之和（分子）
+        risk_IT_XZFY = float("%.2f" % (B_count / A_count))  # 计算风险异常比例
+        Risk_IT_XZFY_score = str(int(risk_IT_XZFY * 100)) + "%"  # 格式转化
+        part_s2_str_dict = sorted(part_s2_str_dict.items(), key=lambda x: x[0], reverse=True)  # 按照权重排序
+        p2_describe = []  # 获取风险权重前三提示
+        for pt in part_s2_str_dict:
+            p2_describe.append(pt[1])
+        p2_describe = getUniqueItems(p2_describe)
+        if len(p2_describe) > 3:
+            p2_describe = p2_describe[:3]
+        # 风险类别获取
+        risk_describe = RiskCompareRule(B_count)
+    else:
+        Risk_IT_XZFY_score="0%"
+        p2_describe="贵公司提供的检测数据不能支撑此风险项检测，您可以尝试按季度或年度检测"
+        risk_describe="数据缺失"
+    Risk_IT_XZFY={
+        "score":Risk_IT_XZFY_score,
+        "describe":p2_describe,
+        "risk":risk_describe
+    }
+    return Risk_IT_XZFY
+
+# 计算所得税虚增成本风险异常
+def IT_XZCB_Risk(dict_all):
+    IT_XZCB_list = tax_desc.get("IT_XZCB")  #获取所得税虚增成本指标名称
+    IT_XZCB_dict = {}  #建立所得税虚增成本字典
+    for f1 in IT_XZCB_list:
+        IT_XZCB_dict[f1] = dict_all[f1]
+    #判断所有指标是否为缺失
+    Risk_not_null={}
+    for k1, v1 in IT_XZCB_dict.items():
+        s_status1 = v1["status"]
+        if s_status1 >0:
+            Risk_not_null[k1]=IT_XZCB_dict[k1]
+    if Risk_not_null:
+        satisfied = []
+        dissatisfied = []
+        part_s1_str_dict = {}
+        for k_part1, v_part1 in IT_XZCB_dict.items():
+            s_s1 = v_part1["s_score"]
+            s_str = v_part1["factors_describe"]
+            s_status1 = v_part1["status"]
+            if s_status1 == 1:
+                satisfied.append(s_s1)
+            if s_status1 == 2:
+                part_s1_str_dict[s_s1] = s_str
+                dissatisfied.append(s_s1)
+        A_count = sum(satisfied) + sum(dissatisfied)  # 计算合格与不合格对应指标权重之和（分母）
+        B_count = sum(dissatisfied)  # 计算不合格对应指标权重之和（分子）
+        risk_IT_XZCB = float("%.2f" % (B_count / A_count))  # 计算风险异常比例
+        Risk_IT_XZCB_score = str(int(risk_IT_XZCB * 100)) + "%"  # 格式转化
+        part_s1_str_dict = sorted(part_s1_str_dict.items(), key=lambda x: x[0], reverse=True)  # 按照权重排序
+        p1_describe = []  # 获取风险权重前三提示
+        for pt in part_s1_str_dict:
+            p1_describe.append(pt[1])
+        p1_describe = getUniqueItems(p1_describe)
+        if len(p1_describe) > 3:
+            p1_describe = p1_describe[:3]
+        # 风险类别获取
+        risk_describe = RiskCompareRule(B_count)
+    else:
+        Risk_IT_XZCB_score="0%"
+        p1_describe="贵公司提供的检测数据不能支撑此风险项检测，您可以尝试按季度或年度检测"
+        risk_describe="数据缺失"
+    Risk_IT_XZCB={
+        "score":Risk_IT_XZCB_score,
+        "describe":p1_describe,
+        "risk":risk_describe
+    }
+    return Risk_IT_XZCB
+
+
+# 计算增值税虚开发票风险异常
+def VAT_XKFP_Risk(dict_all):
+    VAT_XKFP_list = tax_desc.get("VAT_XKFP")  #获取增值税虚开发票指标名称
+    VAT_XKFP_dict = {}  #建立增值税虚开发票字典
+    for f1 in VAT_XKFP_list:
+        VAT_XKFP_dict[f1] = dict_all[f1]
+    #判断所有指标是否为缺失
+    Risk_not_null={}
+    for k1, v1 in VAT_XKFP_dict.items():
+        s_status1 = v1["status"]
+        if s_status1 >0:
+            Risk_not_null[k1]=VAT_XKFP_dict[k1]
+    if Risk_not_null:
+        satisfied = []
+        dissatisfied = []
+        part_s1_str_dict = {}
+        for k_part1, v_part1 in VAT_XKFP_dict.items():
+            s_s1 = v_part1["s_score"]
+            s_str = v_part1["factors_describe"]
+            s_status1 = v_part1["status"]
+            if s_status1 == 1:
+                satisfied.append(s_s1)
+            if s_status1 == 2:
+                part_s1_str_dict[s_s1] = s_str
+                dissatisfied.append(s_s1)
+        A_count = sum(satisfied) + sum(dissatisfied)  # 计算合格与不合格对应指标权重之和（分母）
+        B_count = sum(dissatisfied)  # 计算不合格对应指标权重之和（分子）
+        risk_VAT_XKFP = float("%.2f" % (B_count / A_count))  # 计算风险异常比例
+        Risk_VAT_XKFP_score = str(int(risk_VAT_XKFP * 100)) + "%"  # 格式转化
+        part_s1_str_dict = sorted(part_s1_str_dict.items(), key=lambda x: x[0], reverse=True)  # 按照权重排序
+        p1_describe = []  # 获取风险权重前三提示
+        for pt in part_s1_str_dict:
+            p1_describe.append(pt[1])
+        p1_describe = getUniqueItems(p1_describe)
+        if len(p1_describe) > 3:
+            p1_describe = p1_describe[:3]
+        # 风险类别获取
+        risk_describe = RiskCompareRule(B_count)
+    else:
+        Risk_VAT_XKFP_score="0%"
+        p1_describe="贵公司提供的检测数据不能支撑此风险项检测，您可以尝试按季度或年度检测"
+        risk_describe="数据缺失"
+    Risk_VAT_XKFP={
+        "score":Risk_VAT_XKFP_score,
+        "describe":p1_describe,
+        "risk":risk_describe
+    }
+    return Risk_VAT_XKFP
+
+
+# 计算增值税虚增进项风险异常
+def VAT_XZJX_Risk(dict_all):
+    VAT_XZJX_list = tax_desc.get("VAT_XZJX")  #获取增值税虚增进项指标名称
+    VAT_XZJX_dict = {}  #建立增值税虚增进项字典
+    for f1 in VAT_XZJX_list:
+        VAT_XZJX_dict[f1] = dict_all[f1]
+    #判断所有指标是否为缺失
+    Risk_not_null={}
+    for k1, v1 in VAT_XZJX_dict.items():
+        s_status1 = v1["status"]
+        if s_status1 >0:
+            Risk_not_null[k1]=VAT_XZJX_dict[k1]
+    if Risk_not_null:
+        satisfied = []
+        dissatisfied = []
+        part_s1_str_dict = {}
+        for k_part1, v_part1 in VAT_XZJX_dict.items():
+            s_s1 = v_part1["s_score"]
+            s_str = v_part1["factors_describe"]
+            s_status1 = v_part1["status"]
+            if s_status1 == 1:
+                satisfied.append(s_s1)
+            if s_status1 == 2:
+                part_s1_str_dict[s_s1] = s_str
+                dissatisfied.append(s_s1)
+        A_count = sum(satisfied) + sum(dissatisfied)  # 计算合格与不合格对应指标权重之和（分母）
+        B_count = sum(dissatisfied)  # 计算不合格对应指标权重之和（分子）
+        risk_VAT_XZJX = float("%.2f" % (B_count / A_count))  # 计算风险异常比例
+        Risk_VAT_XZJX_score = str(int(risk_VAT_XZJX * 100)) + "%"  # 格式转化
+        part_s1_str_dict = sorted(part_s1_str_dict.items(), key=lambda x: x[0], reverse=True)  # 按照权重排序
+        p1_describe = []  # 获取风险权重前三提示
+        for pt in part_s1_str_dict:
+            p1_describe.append(pt[1])
+        p1_describe = getUniqueItems(p1_describe)
+        if len(p1_describe) > 3:
+            p1_describe = p1_describe[:3]
+        # 风险类别获取
+        risk_describe = RiskCompareRule(B_count)
+    else:
+        Risk_VAT_XZJX_score="0%"
+        p1_describe="贵公司提供的检测数据不能支撑此风险项检测，您可以尝试按季度或年度检测"
+        risk_describe="数据缺失"
+    Risk_VAT_XZJX={
+        "score":Risk_VAT_XZJX_score,
+        "describe":p1_describe,
+        "risk":risk_describe
+    }
+    return Risk_VAT_XZJX
+
+
+# 计算增值税隐藏销项风险异常
+def VAT_YCXX_Risk(dict_all):
+    VAT_YCXX_list = tax_desc.get("VAT_YCXX")  #获取增值税隐藏销项指标名称
+    VAT_YCXX_dict = {}  #建立增值税隐藏销项字典
+    for f1 in VAT_YCXX_list:
+        VAT_YCXX_dict[f1] = dict_all[f1]
+    #判断所有指标是否为缺失
+    Risk_not_null={}
+    for k1, v1 in VAT_YCXX_dict.items():
+        s_status1 = v1["status"]
+        if s_status1 >0:
+            Risk_not_null[k1]=VAT_YCXX_dict[k1]
+    if Risk_not_null:
+        satisfied = []
+        dissatisfied = []
+        part_s1_str_dict = {}
+        for k_part1, v_part1 in VAT_YCXX_dict.items():
+            s_s1 = v_part1["s_score"]
+            s_str = v_part1["factors_describe"]
+            s_status1 = v_part1["status"]
+            if s_status1 == 1:
+                satisfied.append(s_s1)
+            if s_status1 == 2:
+                part_s1_str_dict[s_s1] = s_str
+                dissatisfied.append(s_s1)
+        A_count = sum(satisfied) + sum(dissatisfied)  # 计算合格与不合格对应指标权重之和（分母）
+        B_count = sum(dissatisfied)  # 计算不合格对应指标权重之和（分子）
+        risk_VAT_YCXX = float("%.2f" % (B_count / A_count))  # 计算风险异常比例
+        Risk_VAT_YCXX_score = str(int(risk_VAT_YCXX * 100)) + "%"  # 格式转化
+        part_s1_str_dict = sorted(part_s1_str_dict.items(), key=lambda x: x[0], reverse=True)  # 按照权重排序
+        p1_describe = []  # 获取风险权重前三提示
+        for pt in part_s1_str_dict:
+            p1_describe.append(pt[1])
+        p1_describe = getUniqueItems(p1_describe)
+        if len(p1_describe) > 3:
+            p1_describe = p1_describe[:3]
+        # 风险类别获取
+        risk_describe = RiskCompareRule(B_count)
+    else:
+        Risk_VAT_YCXX_score="0%"
+        p1_describe="贵公司提供的检测数据不能支撑此风险项检测，您可以尝试按季度或年度检测"
+        risk_describe="数据缺失"
+    Risk_VAT_YCXX={
+        "score":Risk_VAT_YCXX_score,
+        "describe":p1_describe,
+        "risk":risk_describe
+    }
+    return Risk_VAT_YCXX
+
+# 计算消费税风险异常
+def CT_FX_Risk(dict_all):
+    CT_FX_list = tax_desc.get("CT_FX")  #获取消费税指标名称
+    CT_FX_dict = {}  #建立消费税字典
+    for f1 in CT_FX_list:
+        CT_FX_dict[f1] = dict_all[f1]
+    #判断所有指标是否为缺失
+    Risk_not_null={}
+    for k1, v1 in CT_FX_dict.items():
+        s_status1 = v1["status"]
+        if s_status1 >0:
+            Risk_not_null[k1]=CT_FX_dict[k1]
+    if Risk_not_null:
+        satisfied = []
+        dissatisfied = []
+        part_s1_str_dict = {}
+        for k_part1, v_part1 in CT_FX_dict.items():
+            s_s1 = v_part1["s_score"]
+            s_str = v_part1["factors_describe"]
+            s_status1 = v_part1["status"]
+            if s_status1 == 1:
+                satisfied.append(s_s1)
+            if s_status1 == 2:
+                part_s1_str_dict[s_s1] = s_str
+                dissatisfied.append(s_s1)
+        A_count = sum(satisfied) + sum(dissatisfied)  # 计算合格与不合格对应指标权重之和（分母）
+        B_count = sum(dissatisfied)  # 计算不合格对应指标权重之和（分子）
+        risk_CT_FX = float("%.2f" % (B_count / A_count))  # 计算风险异常比例
+        Risk_CT_FX_score = str(int(risk_CT_FX * 100)) + "%"  # 格式转化
+        part_s1_str_dict = sorted(part_s1_str_dict.items(), key=lambda x: x[0], reverse=True)  # 按照权重排序
+        p1_describe = []  # 获取风险权重前三提示
+        for pt in part_s1_str_dict:
+            p1_describe.append(pt[1])
+        p1_describe = getUniqueItems(p1_describe)
+        if len(p1_describe) > 3:
+            p1_describe = p1_describe[:3]
+        # 风险类别获取
+        risk_describe = RiskCompareRule(B_count)
+    else:
+        Risk_CT_FX_score="0%"
+        p1_describe="贵公司提供的检测数据不能支撑此风险项检测，您可以尝试按季度或年度检测"
+        risk_describe="数据缺失"
+    Risk_CT_FX={
+        "score":Risk_CT_FX_score,
+        "describe":p1_describe,
+        "risk":risk_describe
+    }
+    return Risk_CT_FX
+
+# 综合
+def ALLT_FX_Risk(dict_all):
+    ALLT_FX_list = tax_desc.get("ALLT_FX")  #获取综合风险指标名称
+    ALLT_FX_dict = {}  #建立综合风险字典
+    for f1 in ALLT_FX_list:
+        ALLT_FX_dict[f1] = dict_all[f1]
+    #判断所有指标是否为缺失
+    Risk_not_null={}
+    for k1, v1 in ALLT_FX_dict.items():
+        s_status1 = v1["status"]
+        if s_status1 >0:
+            Risk_not_null[k1]=ALLT_FX_dict[k1]
+    if Risk_not_null:
+        satisfied = []
+        dissatisfied = []
+        part_s1_str_dict = {}
+        for k_part1, v_part1 in ALLT_FX_dict.items():
+            s_s1 = v_part1["s_score"]
+            s_str = v_part1["factors_describe"]
+            s_status1 = v_part1["status"]
+            if s_status1 == 1:
+                satisfied.append(s_s1)
+            if s_status1 == 2:
+                part_s1_str_dict[s_s1] = s_str
+                dissatisfied.append(s_s1)
+        A_count = sum(satisfied) + sum(dissatisfied)  # 计算合格与不合格对应指标权重之和（分母）
+        B_count = sum(dissatisfied)  # 计算不合格对应指标权重之和（分子）
+        risk_ALLT_FX = float("%.2f" % (B_count / A_count))  # 计算风险异常比例
+        Risk_ALLT_FX_score = str(int(risk_ALLT_FX * 100)) + "%"  # 格式转化
+        part_s1_str_dict = sorted(part_s1_str_dict.items(), key=lambda x: x[0], reverse=True)  # 按照权重排序
+
+        p1_describe = []  # 获取风险权重前三提示
+        for pt in part_s1_str_dict:
+            p1_describe.append(pt[1])
+        p1_describe = getUniqueItems(p1_describe)
+        if len(p1_describe) > 3:
+            p1_describe = p1_describe[:3]
+        # 风险类别获取
+        risk_describe = RiskCompareRule(B_count)
+    else:
+        Risk_ALLT_FX_score="0%"
+        p1_describe="贵公司提供的检测数据不能支撑此风险项检测，您可以尝试按季度或年度检测"
+        risk_describe="数据缺失"
+    Risk_ALLT_FX={
+        "score":Risk_ALLT_FX_score,
+        "describe":p1_describe,
+        "risk":risk_describe
+    }
+    return Risk_ALLT_FX
+
+#整合调用前面构建函数，并结合传入数据建立风险监测结果函数
+def Riskinfo(GetAllinfo):
+    dict_all, ALLRisk = CreatBdictFromJson(GetAllinfo)
+    RiskA = RiskAbnormal(ALLRisk["t_r1"])  # 风险异常比例
+    MRisk = MonitoredRisk(ALLRisk["t_r2"])  # 被监控风险
+    ARisk = AuditedRisk(ALLRisk["t_r3"])  # 被稽查风险
+    info = information(ALLRisk["t_r1"])  # 提示语
+    Risk_IT_YCSR = IT_YCSR_Risk(dict_all)  # 企业所得税隐藏收入风险信息
+    Risk_IT_XZFY = IT_XZFY_Risk(dict_all)  # 企业所得税虚增费用风险信息
+    Risk_IT_XZCB = IT_XZCB_Risk(dict_all)  # 企业所得税虚增成本风险信息
+    Risk_VAT_XKFP = VAT_XKFP_Risk(dict_all)  # 企业增值税虚开发票风险信息
+    Risk_VAT_XZJX = VAT_XZJX_Risk(dict_all)  # 企业增值税虚增进项风险信息
+    Risk_VAT_YCXX = VAT_YCXX_Risk(dict_all)  # 企业增值税隐藏销项风险信息
+    Risk_CT_FX = CT_FX_Risk(dict_all)  # 消费税风险信息
+    Risk_ALLT_FX = ALLT_FX_Risk(dict_all)  # 综合风险信息
+    RI_result = {
+        "RiskA": RiskA,
+        "MRisk": MRisk,
+        "ARisk": ARisk,
+        "info": info,
+        "Risk_IT_YCSR": Risk_IT_YCSR,
+        "Risk_IT_XZFY": Risk_IT_XZFY,
+        "Risk_IT_XZCB": Risk_IT_XZCB,
+        "Risk_VAT_XKFP": Risk_VAT_XKFP,
+        "Risk_VAT_XZJX": Risk_VAT_XZJX,
+        "Risk_VAT_YCXX": Risk_VAT_YCXX,
+        "Risk_CT_FX": Risk_CT_FX,
+        "Risk_ALLT_FX": Risk_ALLT_FX
+    }
+    print(RI_result, "------------------------------")
+    return RI_result
+
+
+

rpt_result/getcompanyinfofromES.py

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Time    : 2020/2/11 19:25
+# @Author  : fanhui
+# @File    : getcompanyinfofromES.py
+# @Software: PyCharm
+
+import os, sys
+from flask_cors import *
+import json
+from flask import Flask
+import oss2,time,datetime
+from elasticsearch import Elasticsearch
+
+
+
+server = Flask(__name__)
+interface_path = os.path.dirname(__file__)
+sys.path.insert(0, interface_path)  # 将当前文件的父目录加入临时系统变量
+server.config['JSON_AS_ASCII'] = False
+CORS(server, supports_credentials=True)  # 跨域请求
+
+
+
+# 工商数据接口
+def GetComanyinfoFromES(companyname):
+    esip = "43.247.184.94"
+    esport = 9200
+    esuser = "admines"
+    espassword = "adminGSBes"
+    es = Elasticsearch([esip], http_auth=(esuser, espassword), port=esport, timeout=15000)
+    companyinfo = es.search(index="bigdata_ic_gsb_company_op",
+                            body={"query": {"bool": {"must": {"term": {"company_name.raw": companyname}}}}})
+    companylist = {}
+    if companyinfo["hits"]["hits"]:
+        for row in companyinfo["hits"]["hits"]:
+            companyname = row['_source']["company_name"]  # 公司名称
+            legal_person = row['_source']["legal_person"]  # 法定代表人
+            if legal_person is None:
+                legal_person = "_"
+            cred_code = row['_source']["credit_code"]  # 统一社会信用代码
+            if cred_code is None:
+                cred_code = "_"
+            gsb_province = row['_source']["base"]  # 所属省份
+            if gsb_province is None:
+                gsb_province = "_"
+            gsb_city = row['_source']["gsb_city"] #城市
+            if gsb_city is None:
+                gsb_city="_"
+            reg_location = row['_source']["reg_location"]  # 企业地址
+            if reg_location is None:
+                reg_location = "_"
+            establish_time = time.strftime("%Y-%m-%d", time.localtime(row["_source"]["estiblish_time"]))  # 成立日期
+            if establish_time is None:
+                establish_time = "_"
+            company_cate_1 = row['_source']["gsb_company_cate_1"]  # 所属行业
+            if company_cate_1 is None:
+                company_cate_1 = "_"
+            company_org_type = row['_source']["company_org_type"]  # 企业类型
+            if company_org_type is None:
+                company_org_type = "_"
+            reg_institute = row['_source']["reg_institute"]  # 登记机关
+            if reg_institute is None:
+                reg_institute = "_"
+            reg_status = row['_source']["reg_status"]  # 营业状态
+            if reg_status is None:
+                reg_status = "_"
+            reg_capital = row['_source']["reg_capital"]  # 注册资本
+            reg_unit=row['_source']["reg_unit"]  # 注册资本单位
+            if reg_capital is None:
+                reg_capital = "_"
+            else:
+                if reg_unit=="万人民币":
+                    reg_capital = str(reg_capital) + "万元"
+                else:
+                    reg_capital = str(reg_capital)
+            from_time = time.strftime("%Y-%m-%d", time.localtime(row["_source"]["from_time"]))  # 营业期限开始
+            if from_time is None:
+                from_time = "_"
+            to_time = time.strftime("%Y-%m-%d", time.localtime(row["_source"]["to_time"]))  # 营业期限结束
+            if to_time is None:
+                to_time = "_"
+            approved_time = time.strftime("%Y-%m-%d", time.localtime(row["_source"]["approved_time"]))  # 核准日期
+            if approved_time is None:
+                approved_time = "_"
+            business_scope = row['_source']["business_scope"]  # 经营范围
+            if business_scope is None:
+                business_scope = "_"
+            companylist["companyname"] = companyname
+            companylist["legal_person"] = legal_person
+            companylist["cred_code"] = cred_code
+            companylist["province"] = gsb_province
+            companylist["city"] = gsb_city
+            companylist["reg_location"] = reg_location
+            companylist["establish_time"] = establish_time
+            companylist["company_cate_1"] = company_cate_1
+            companylist["company_org_type"] = company_org_type
+            companylist["reg_institute"] = reg_institute
+            companylist["reg_capital"] = reg_capital
+            companylist["reg_status"] = reg_status
+            companylist["from_time"] = from_time
+            companylist["to_time"] = to_time
+            companylist["approved_time"] = approved_time
+            companylist["business_scope"] = business_scope
+        return companylist
+# a=GetComanyinfoFromES("用友网络科技股份有限公司")
+# print(a)
\ No newline at end of file

rpt_result/main.py

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
-# @Time    : 2019/12/26 17:33
+# @Time    : 2020/2/11 10:02
 # @Author  : fanhui
-# @File    : sql_tax_test.py
+# @File    : risk_to_word.py
 # @Software: PyCharm
 
-import os, sys
-from flask import request, Flask,jsonify
+from getcompanyinfofromES import GetComanyinfoFromES
+from GetFIinfoFromJson import GetFIinfoFromJson
+from GetRiskinfoFromJson import Riskinfo
+import time,requests,oss2,sys,os,json
+from flask import Flask ,request
+from docxtpl import DocxTemplate,InlineImage
+from docx.shared import Mm
+from pyecharts.charts import Gauge
+from pyecharts.render import make_snapshot
+from snapshot_selenium import snapshot
+from pyecharts import options as opts
 from flask_cors import *
-import json
 
 server = Flask(__name__)
 interface_path = os.path.dirname(__file__)
@@ -16,704 +24,198 @@ sys.path.insert(0, interface_path)  # 将当前文件的父目录加入临时系
 server.config['JSON_AS_ASCII'] = False
 CORS(server, supports_credentials=True)  # 跨域请求
 
-# 获取传入的参数，建立大字典
-def CreatBdictFromJson(new_status):
-    # 读取指标文件，获取权重及描述信息
-    file = open('tax_index.txt', 'r', encoding='utf-8')
-    lines = file.readlines()
-    dict_all = {}
-    for line in lines:
-        linesplit = line.strip().split('\t')
-        tax_index = {
-            "factors_code": linesplit[3].replace('"', ""),
-            "factors_name": linesplit[4].replace('"', ""),
-            "s_score": int(linesplit[5].replace('"', "")),
-            "factors_describe": linesplit[6].replace('"', ""),
-            "status": 0
-        }
-        dict_all[linesplit[4].replace('"', "")] = tax_index
-    # print(dict_all)
-    # 获取传入的参数
-    new_status = new_status
-    for factors_name, value in dict_all.items():
-        try:
-            dict_all[factors_name]["status"] = new_status[factors_name]
-        except:
-            pass
-    # print(dict_all)
 
-    # 计算总体风险异常比例、以及被监控风险比例和被稽查风险比例
-    qualified = []  # 合格
-    disqualified = []  # 不合格
-    for k, v in dict_all.items():
-        s_status = v['status']
-        s_score = v['s_score']
-        if s_status == 1:
-            qualified.append(s_score)
-        if s_status == 2:
-            disqualified.append(s_score)
-    A_count = sum(qualified) + sum(disqualified)  # 计算所有合格与不合格对应指标权重之和（分母）
-    B_count = sum(disqualified)  # 计算所有不合格对应指标权重之和（分子）
-    r1 = float("%.2f" % (B_count / A_count))  # 计算风险异常比例
-    print(r1, type(r1))
-    t_r1 = str(int(r1 * 100)) + "%"
-    t_r2 = str(int((r1 - 0.29) * 100)) + "%"
-    t_r3 = str(int((r1 - 0.49) * 100)) + "%"
-    standard1 = 0.3
-    standard2 = 0.5
-    # 建立第一层规则
-    rules = {r1 <= standard1: {"t_r1": t_r1, "t_r2": "1%", "t_r3": "1%"},
-             r1 > standard1 and r1 <= standard2: {"t_r1": t_r1, "t_r2": t_r2, "t_r3": "1%"},
-             r1 > standard2: {"t_r1": t_r1, "t_r2": t_r2, "t_r3": t_r3}}
-    ALLRisk = {}
-    for k1, v1 in rules.items():
-        if k1:
-            ALLRisk = v1
-    print(ALLRisk,"+++++++++++++")
-    return dict_all, ALLRisk
 
-
-# 输入计算出的风险异常比例，返回提示结果
-def RiskAbnormal(t_r1):
-    RiskA = {}
-    t_r1_s = str(int(t_r1.replace("%", "")) / 100)
-    rules_1 = {"t_r1_s<='0.3'": "企业风险异常同比其他企业比例较低",
-               "t_r1_s>'0.3'": "企业风险异常同比其他企业比例明显偏高"
-               }
-    for k1, v1 in rules_1.items():
-        if eval(str(k1)):
-            RiskA = {"score":t_r1,"describe":v1}
-    return RiskA
-
-# 输入计算出的被检测风险值，判断被监测风险结果
-def MonitoredRisk(t_r2):
-    MRisk = {}
-    t_r2_s = str(int(t_r2.replace("%", "")) / 100)
-    rules_2 = {"t_r2_s<='0.1'": "企业被监控的风险同比其他企业比例较低",
-               "t_r2_s>'0.1' and t_r2_s<='0.5'": "经检测，企业有一定可能性被选为税务监控对象",
-               "t_r2_s>'0.5'": "经检测，企业有很大可能性被选为税务监控对象"
-               }
-    for k2, v2 in rules_2.items():
-        if eval(str(k2)):
-            MRisk = {"score": t_r2, "describe": v2}
-    return MRisk
-
-# 输入计算出的被稽查风险值，判断被稽查风险结果
-def AuditedRisk(t_r3):
-    ARisk = {}
-    t_r3_s = str(int(t_r3.replace("%", "")) / 100)
-    rules_3 = {"t_r3_s<='0.1'": "企业被税务稽查的可能性较低",
-               "t_r3_s>'0.1' and t_r3_s<='0.3'": "企业有一定可能性被税务稽查",
-               "t_r3_s>'0.3'": "企业有很大可能性被税务稽查"
-               }
-    for k3, v3 in rules_3.items():
-        if eval(str(k3)):
-            ARisk = {"score":t_r3,"describe":v3}
-    return ARisk
-
-# 输入总体计算的风险异常比例，得到提示语（也可与RiskAbnormal函数合并）
-def information(t_r1):
-    info = {}
-    t_r1_s = str(int(t_r1.replace("%", "")) / 100)
-    rules_0 = {"t_r1_s<='0.3'": "贵企业的风险异常情况同比其他企业较少，如需更多更详细的诊断服务，请联系我们>>",
-               "t_r1_s>'0.3' and t_r1_s<='0.6'": "企业的风险异常情况同比其他企业明显偏多，有较大可能性成为税务系统监控的对象，请企业的财税人员参考以下详细检测情况进行自查，如需进行更详细的诊断服务请联系我们>>",
-               "t_r1_s>'0.6' and t_r1_s<='0.8'": "如果税务局选择企业所在行业进行行业稽查，该企业有一定可能性成为被选案例，请财税人员尽快参照以下比例进行自查，如需进行更详细的诊断服务请联系我们>>",
-               "t_r1_s>'0.8'": "如果税务局选择企业所在行业进行行业稽查，该企业有很大可能性成为被选案例，请财税人员尽快参照以下比例进行自查，如需进行更详细的诊断服务请联系我们>>"
-               }
-    for k0, v0 in rules_0.items():
-        if eval(str(k0)):
-            info = {"score":t_r1,"describe":v0}
-    return info
-
-
-# 建立不同风险监测项与指标的对应关系
-tax_desc = {
-    "IT_YCSR": ["所得税行业预警税负率",
-                "销售（营业）收入变动率",
-                "收入利润率",
-                "销售成本大于销售收入",
-                "当期新增其他应收款/销售收入>80%",
-                "期末存货大于实收资本差异幅度异常",
-                "成本费用利润率",
-                "利润总额变动率",
-                "投资利润率",
-                "预付账款为负数",
-                "应收账款为负数",
-                "应付账款为负数",
-                "总资产周转率",
-                "预收账款过大异常",
-                "资产利润率",
-                "纳税人营业外支出变动率与营业外收入变动率弹性系数异常",
-                "纳税人流动资产变动率与营业收入变动率弹性系数异常",
-                "纳税人期末存货与当期累计收入差异幅度异常",
-                "账面存货率过高将会导致系统预警",
-                "收入负担变动率",
-                "应税所得率变动率",
-                "应纳税所得额变动率",
-                "行业预警利润率",
-                "主营业务收入变动率",
-                "主营业务成本变动率",
-                "其他业务利润变动率",
-                "主营业务利润变动率",
-                "纳税人主营业务收入变动率与主营业务利润变动率弹性系数异常",
-                "纳税人主营业务收入变动率与主营业务成本变动率弹性系数异常"],
-    "IT_XZCB": ["所得税行业预警税负率",
-                "销售成本大于销售收入",
-                "成本费用利润率",
-                "期末存货大于实收资本差异幅度异常",
-                "收入成本率",
-                "产品销售（营业）成本变动率",
-                "纳税人期末存货与当期累计收入差异幅度异常",
-                "账面存货率过高将会导致系统预警",
-                "总资产周转率",
-                "资产负债率",
-                "主营业务成本变动率",
-                "纳税人固定资产综合折旧率变动异常将会导致系统预警",
-                "纳税人无形资产综合摊销率变动异常",
-                "高比例出口的亏损企业",
-                "收入负担变动率",
-                "应税所得率变动率",
-                "应纳税所得额变动率",
-                "行业预警利润率",
-                "其他业务利润变动率",
-                "主营业务利润变动率",
-                "纳税人主营业务收入变动率与主营业务利润变动率弹性系数异常"],
-    "IT_XZFY": ["所得税行业预警税负率",
-                "销售成本大于销售收入",
-                "成本费用利润率",
-                "期末存货大于实收资本差异幅度异常",
-                "收入费用率",
-                "期间费用变动率",
-                "纳税人期末存货与当期累计收入差异幅度异常",
-                "账面存货率过高将会导致系统预警",
-                "总资产周转率",
-                "资产负债率",
-                "成本费用率",
-                "费用率变动率",
-                "营业（管理、财务）费用变动率",
-                "主营业务成本变动率",
-                "高比例出口的亏损企业",
-                "收入负担变动率",
-                "应税所得率变动率",
-                "应纳税所得额变动率",
-                "行业预警利润率",
-                "其他业务利润变动率",
-                "主营业务利润变动率",
-                "纳税人主营业务收入变动率与主营业务利润变动率弹性系数异常",
-                "纳税人期间费用变动率与主营业务收入变动率弹性系数异常",
-                "广告费和业务宣传费异常",
-                "业务招待费异常"],
-    "VAT_XKFP": ["销售额变动率",
-                 "销售毛利（益）率",
-                 "税收负担变动率",
-                 "税收负担率",
-                 "增值税专用发票用量变动异常",
-                 "增值税一股纳税人税负变动异常",
-                 "增值税普通发票用量变动异常",
-                 "进项税额变动率∶应纳税额变动率"],
-    "VAT_YCXX": ["销售额变动率",
-                 "税收负担变动率",
-                 "税收负担率",
-                 "存货变动率∶销售成本变动率",
-                 "存货变动率∶销售收入变动率",
-                 "存货周转率∶资金周转率",
-                 "增值税一股纳税人税负变动异常",
-                 "纳税人销售额变动率与应纳税额变动率弹性系数异常",
-                 "进项税额变动率∶应纳税额变动率",
-                 "应收账款变动率∶销项税额变动率",
-                 "预收账款大于零且存货大于预收账款又无留抵",
-                 "进项税额变动率高于销售税额变动率",
-                 "在建工程增加额",
-                 "长期投资增加额分析",
-                 "企业期末预收账款变动率与销售收入变动率弹性系数异常",
-                 "纳税人销售毛利率变动率与税负率变动率弹性系统异常"],
-    "VAT_XZJX": ["销售毛利（益）率",
-                 "税收负担变动率",
-                 "税收负担率",
-                 "增值税一股纳税人税负变动异常",
-                 "纳税人销售额变动率与应纳税额变动率弹性系数异常",
-                 "应付账款变动率∶进项税额变动率",
-                 "进项税额变动率高于销售税额变动率",
-                 "在建工程增加额",
-                 "长期投资增加额分析",
-                 "企业期末预收账款变动率与销售收入变动率弹性系数异常",
-                 "应税销售额变动率",
-                 "纳税人销售毛利率变动率与税负率变动率弹性系统异常"],
-    "CT_FX": ["其他应付款变动额",
-              "应付帐款变动额",
-              "营业外支出变动额",
-              "长期投资变动额"],
-    "ALLT_FX": ["消费税申报收入与增值税申报收入差异",
-                "所得税申报收入与增值税申报收入差异",
-                "长期零申报"]
-}
-
-
-# 建立细分税种风险类别（根据传入的不合格指标之和进行比较）
-def RiskCompareRule(wx_part):
-    risk_describe = ''
-    if wx_part and wx_part != None:
-        risk_relus = {
-            wx_part >= 5 : "高危风险",
-            # wx_part >= 5 and wx_part <= 10: "高危风险",
-            wx_part >= 3 and wx_part <= 4: "重度风险",
-            wx_part == 2: "中度风险",
-            wx_part <= 1: "轻微风险"
-        }
-        for k, v in risk_relus.items():
-            if k:
-                risk_describe = v
-    else:
-        risk_describe = "数据缺失"
-    return risk_describe
-#风险描述去重
-def getUniqueItems(iterable):
-    seen = set()
-    result = []
-    for item in iterable:
-        if item not in seen:
-            seen.add(item)
-            result.append(item)
-    return result
-
-
-# 计算所得税隐藏收入风险异常(对所属指标不合格降序排列，展示前三的风险描述信息，计算不合格总和及风险比例）
-def IT_YCSR_Risk(dict_all):
-    IT_YCSR_list = tax_desc.get("IT_YCSR")  #获取所得税隐藏收入指标名称
-    IT_YCSR_dict = {}  #建立所得税隐藏收入字典
-    for f1 in IT_YCSR_list:
-        IT_YCSR_dict[f1] = dict_all[f1]
-    #判断所有指标是否为缺失
-    Risk_not_null={}
-    for k1, v1 in IT_YCSR_dict.items():
-        s_status1 = v1["status"]
-        if s_status1 >0:
-            Risk_not_null[k1]=IT_YCSR_dict[k1]
-    if Risk_not_null:
-        satisfied = []
-        dissatisfied = []
-        part_s1_str_dict = {}
-        for k_part1, v_part1 in IT_YCSR_dict.items():
-            s_s1 = v_part1["s_score"]
-            s_str = v_part1["factors_describe"]
-            s_status1 = v_part1["status"]
-            if s_status1 == 1:
-                satisfied.append(s_s1)
-            if s_status1 == 2:
-                part_s1_str_dict[s_s1] = s_str
-                dissatisfied.append(s_s1)
-        A_count = sum(satisfied) + sum(dissatisfied)  # 计算合格与不合格对应指标权重之和（分母）
-        B_count = sum(dissatisfied)  # 计算不合格对应指标权重之和（分子）
-        risk_IT_YCSR = float("%.2f" % (B_count / A_count))  # 计算风险异常比例
-        Risk_IT_YCSR_score = str(int(risk_IT_YCSR * 100)) + "%"  # 格式转化
-        part_s1_str_dict = sorted(part_s1_str_dict.items(), key=lambda x: x[0], reverse=True)  # 按照权重排序
-        p1_describe = []  # 获取风险权重前三提示
-        for pt in part_s1_str_dict:
-            p1_describe.append(pt[1])
-        p1_describe = getUniqueItems(p1_describe)  #风险描述去重
-        if len(p1_describe) > 3:
-            p1_describe = p1_describe[:3]
-        # 风险类别获取
-        risk_describe = RiskCompareRule(B_count)
-    else:
-        Risk_IT_YCSR_score="0%"
-        p1_describe=""
-        risk_describe="数据缺失"
-    Risk_IT_YCSR={
-        "score":Risk_IT_YCSR_score,
-        "describe":p1_describe,
-        "risk":risk_describe
-    }
-    return Risk_IT_YCSR
-
-
-# 计算所得税虚增费用风险异常
-def IT_XZFY_Risk(dict_all):
-    IT_XZFY_list = tax_desc.get("IT_XZFY")  #获取所得税虚增费用指标名称
-    IT_XZFY_dict = {}  #建立所得税虚增费用字典
-    for f2 in IT_XZFY_list:
-        IT_XZFY_dict[f2] = dict_all[f2]
-    #判断所有指标是否为缺失
-    Risk_not_null={}
-    for k2, v2 in IT_XZFY_dict.items():
-        s_status2 = v2["status"]
-        if s_status2 >0:
-            Risk_not_null[k2]=IT_XZFY_dict[k2]
-    if Risk_not_null:
-        satisfied = []
-        dissatisfied = []
-        part_s2_str_dict = {}
-        for k_part2, v_part2 in IT_XZFY_dict.items():
-            s_s2 = v_part2["s_score"]
-            s_str2 = v_part2["factors_describe"]
-            s_status2 = v_part2["status"]
-            if s_status2 == 1:
-                satisfied.append(s_s2)
-            if s_status2 == 2:
-                part_s2_str_dict[s_s2] = s_str2
-                dissatisfied.append(s_s2)
-        A_count = sum(satisfied) + sum(dissatisfied)  # 计算合格与不合格对应指标权重之和（分母）
-        B_count = sum(dissatisfied)  # 计算不合格对应指标权重之和（分子）
-        risk_IT_XZFY = float("%.2f" % (B_count / A_count))  # 计算风险异常比例
-        Risk_IT_XZFY_score = str(int(risk_IT_XZFY * 100)) + "%"  # 格式转化
-        part_s2_str_dict = sorted(part_s2_str_dict.items(), key=lambda x: x[0], reverse=True)  # 按照权重排序
-        p2_describe = []  # 获取风险权重前三提示
-        for pt in part_s2_str_dict:
-            p2_describe.append(pt[1])
-        p2_describe = getUniqueItems(p2_describe)
-        if len(p2_describe) > 3:
-            p2_describe = p2_describe[:3]
-        # 风险类别获取
-        risk_describe = RiskCompareRule(B_count)
-    else:
-        Risk_IT_XZFY_score="0%"
-        p2_describe=""
-        risk_describe="数据缺失"
-    Risk_IT_XZFY={
-        "score":Risk_IT_XZFY_score,
-        "describe":p2_describe,
-        "risk":risk_describe
-    }
-    return Risk_IT_XZFY
-
-# 计算所得税虚增成本风险异常
-def IT_XZCB_Risk(dict_all):
-    IT_XZCB_list = tax_desc.get("IT_XZCB")  #获取所得税虚增成本指标名称
-    IT_XZCB_dict = {}  #建立所得税虚增成本字典
-    for f1 in IT_XZCB_list:
-        IT_XZCB_dict[f1] = dict_all[f1]
-    #判断所有指标是否为缺失
-    Risk_not_null={}
-    for k1, v1 in IT_XZCB_dict.items():
-        s_status1 = v1["status"]
-        if s_status1 >0:
-            Risk_not_null[k1]=IT_XZCB_dict[k1]
-    if Risk_not_null:
-        satisfied = []
-        dissatisfied = []
-        part_s1_str_dict = {}
-        for k_part1, v_part1 in IT_XZCB_dict.items():
-            s_s1 = v_part1["s_score"]
-            s_str = v_part1["factors_describe"]
-            s_status1 = v_part1["status"]
-            if s_status1 == 1:
-                satisfied.append(s_s1)
-            if s_status1 == 2:
-                part_s1_str_dict[s_s1] = s_str
-                dissatisfied.append(s_s1)
-        A_count = sum(satisfied) + sum(dissatisfied)  # 计算合格与不合格对应指标权重之和（分母）
-        B_count = sum(dissatisfied)  # 计算不合格对应指标权重之和（分子）
-        risk_IT_XZCB = float("%.2f" % (B_count / A_count))  # 计算风险异常比例
-        Risk_IT_XZCB_score = str(int(risk_IT_XZCB * 100)) + "%"  # 格式转化
-        part_s1_str_dict = sorted(part_s1_str_dict.items(), key=lambda x: x[0], reverse=True)  # 按照权重排序
-        p1_describe = []  # 获取风险权重前三提示
-        for pt in part_s1_str_dict:
-            p1_describe.append(pt[1])
-        p1_describe = getUniqueItems(p1_describe)
-        if len(p1_describe) > 3:
-            p1_describe = p1_describe[:3]
-        # 风险类别获取
-        risk_describe = RiskCompareRule(B_count)
+# 生成图片
+def charts(datalist, charttype, pngpath):
+    try:
+        img = None
+        if charttype == 1:  # 仪表盘
+            img = Gauge(init_opts=opts.InitOpts(width="450px", height="450px"))
+            img.add("", [(datalist[0]["key"], datalist[0]["value"])],
+                axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(color=[(0.3, "#67e0e3"), (0.7, "#37a2da"), (1, "#fd666d")], width=35)),
+                label_opts=opts.LabelOpts(font_size=55,formatter="{value}%"))
+        if img:
+            make_snapshot(snapshot, img.render(), pngpath)
+            return True
+        else:
+            return False
+    except Exception as e:
+        print(e)
+        return False
+
+
+#生成报告(es工商、财务信息、风险监测（仪表盘图）)
+def WriteReport(Else_info,Companyinfo,Fi_dict,Risk_info):
+    tpl = DocxTemplate('tax_word_model.docx')
+    context={}
+    #其他传入参数
+    context['CreatTime']=Else_info['CreatTime']
+    context['Detection_zone'] = Else_info['Detection_zone']
+    #01-02工商
+    context['template'] =Companyinfo["companyname"]
+    context['legal_person'] =Companyinfo["legal_person"]
+    context['cred_code'] =Companyinfo["cred_code"]
+    context['province'] =Companyinfo["province"]
+    context['city'] =Companyinfo["city"]
+    context['reg_location'] =Companyinfo["reg_location"]
+    context['establish_time'] =Companyinfo["establish_time"]
+    context['company_cate_1'] =Companyinfo["company_cate_1"]
+    context['company_org_type'] =Companyinfo["company_org_type"]
+    context['reg_institute'] =Companyinfo["reg_institute"]
+    context['reg_capital'] =Companyinfo["reg_capital"]
+    context['reg_status'] =Companyinfo["reg_status"]
+    context['from_time'] =Companyinfo["from_time"]
+    context['to_time'] =Companyinfo["to_time"]
+    context['approved_time'] =Companyinfo["approved_time"]
+    context['business_scope'] =Companyinfo["business_scope"]
+    #03-05财务信息
+    context["Bq_time"] = Fi_dict["Bq_time"]
+    context["Sq_time"] = Fi_dict["Sq_time"]
+    context["Bq_Total_trade_income"] = Fi_dict["Bq_Total_trade_income"]
+    context["Sq_Total_trade_income"] = Fi_dict["Sq_Total_trade_income"]
+    context["change_rate_1"] = Fi_dict["change_rate_1"]
+    context["Bq_Total_profit"] = Fi_dict["Bq_Total_profit"]
+    context["Sq_Total_profit"] = Fi_dict["Sq_Total_profit"]
+    context["change_rate_2"] = Fi_dict["change_rate_2"]
+    context["Bq_Net_profit"] = Fi_dict["Bq_Net_profit"]
+    context["Sq_Net_profit"] = Fi_dict["Sq_Net_profit"]
+    context["change_rate_3"] = Fi_dict["change_rate_3"]
+    context["Bq_Total_Liabilities"] = Fi_dict["Bq_Total_Liabilities"]
+    context["Sq_Total_Liabilities"] = Fi_dict["Sq_Total_Liabilities"]
+    context["change_rate_4"] = Fi_dict["change_rate_4"]
+    context["Bq_Owner_rights"] = Fi_dict["Bq_Owner_rights"]
+    context["Sq_Owner_rights"] = Fi_dict["Sq_Owner_rights"]
+    context["change_rate_5"] = Fi_dict["change_rate_5"]
+    context["Bq_Total_sales"] = Fi_dict["Bq_Total_sales"]
+    context["Sq_Total_sales"] = Fi_dict["Sq_Total_sales"]
+    context["change_rate_6"] = Fi_dict["change_rate_6"]
+    context["Bq_Total_assets"] = Fi_dict["Bq_Total_assets"]
+    context["Sq_Total_assets"] = Fi_dict["Sq_Total_assets"]
+    context["change_rate_7"] = Fi_dict["change_rate_7"]
+    context["Bq_Total_liabilities_and_Owner_rights"] = Fi_dict["Bq_Total_liabilities_and_Owner_rights"]
+    context["Sq_Total_liabilities_and_Owner_rights"] = Fi_dict["Sq_Total_liabilities_and_Owner_rights"]
+    context["Bq_sales_expense"] = Fi_dict["Bq_sales_expense"]
+    context["Sq_sales_expense"] = Fi_dict["Sq_sales_expense"]
+    context["Bq_Net_interest_rate"] = Fi_dict["Bq_Net_interest_rate"]
+    context["Sq_Net_interest_rate"] = Fi_dict["Sq_Net_interest_rate"]
+    context["Bq_Equity_ratio"] = Fi_dict["Bq_Equity_ratio"]
+    context["Sq_Equity_ratio"] = Fi_dict["Sq_Equity_ratio"]
+    context["Bq_Gearing_ratio"] = Fi_dict["Bq_Gearing_ratio"]
+    context["Sq_Gearing_ratio"] = Fi_dict["Sq_Gearing_ratio"]
+    context["Bq_sales_growth_rate"] = Fi_dict["Bq_sales_growth_rate"]
+    context["Sq_sales_growth_rate"] = Fi_dict["Sq_sales_growth_rate"]
+    context["Bq_Net_profit_growth_rate"] = Fi_dict["Bq_Net_profit_growth_rate"]
+    context["Sq_Net_profit_growth_rate"] = Fi_dict["Sq_Net_profit_growth_rate"]
+    context["Bq_total_assets_growth_rate"] = Fi_dict["Bq_total_assets_growth_rate"]
+    context["Sq_total_assets_growth_rate"] = Fi_dict["Sq_total_assets_growth_rate"]
+
+
+    # 06仪表盘图（需获取评分grade）
+    grades1 = Risk_info["RiskA"]["score"].replace('%', "")
+    grades2 = Risk_info["MRisk"]["score"].replace('%', "")
+    grades3 = Risk_info["ARisk"]["score"].replace('%', "")
+    guage1 = ""
+    guage2 = ""
+    guage3 = ""
+    if charts([{"key": "风险异常比例", "value": grades1}], 1, "yibiaopan1.png"):
+        guage1 = "yibiaopan1.png"
+    if charts([{"key": "被监测风险", "value": grades2}], 1, "yibiaopan2.png"):
+        guage2 = "yibiaopan2.png"
+    if charts([{"key": "被稽查风险", "value": grades3}], 1, "yibiaopan3.png"):
+        guage3 = "yibiaopan3.png"
+    context['guage1'] = InlineImage(tpl, guage1, width=Mm(60))
+    context['guage2'] = InlineImage(tpl, guage2, width=Mm(60))
+    context['guage3'] = InlineImage(tpl, guage3, width=Mm(60))
+    context['RiskA_desc'] ="企业风险异常同比其他企业比例较低"
+    context['MRisk_desc'] = "企业风险异常同比其他企业比例较低"
+    context['ARisk_desc'] = "企业风险异常同比其他企业比例较低"
+    # 风险信息
+    context["RiskA_desc"] = Risk_info["RiskA"]["describe"]
+    context["MRisk_desc"] = Risk_info["MRisk"]["describe"]
+    context["ARisk_desc"] = Risk_info["ARisk"]["describe"]
+    context["describe_info"] = Risk_info["info"]["describe"]
+    #细分风险项
+    context["IT_YCSR_Risk_desc"] = str(Risk_info["Risk_IT_YCSR"]["describe"]).replace("['","").replace("']","").replace("'","")
+    context["IT_XZFY_Risk_desc"] = str(Risk_info["Risk_IT_XZFY"]["describe"]).replace("['","").replace("']","").replace("'","")
+    context["IT_XZCB_Risk_desc"] = str(Risk_info["Risk_IT_XZCB"]["describe"]).replace("['","").replace("']","").replace("'","")
+    context["VAT_XKFP_Risk_desc"] = str(Risk_info["Risk_VAT_XKFP"]["describe"]).replace("['","").replace("']","").replace("'","")
+    context["VAT_YCXX_Risk_desc"] = str(Risk_info["Risk_VAT_YCXX"]["describe"]).replace("['","").replace("']","").replace("'","")
+    context["VAT_XZJX_Risk_desc"] = str(Risk_info["Risk_VAT_XZJX"]["describe"]).replace("['","").replace("']","").replace("'","")
+    context["CT_FX_Risk_desc"] = str(Risk_info["Risk_CT_FX"]["describe"]).replace("['","").replace("']","").replace("'","")
+    context["ALLT_FX_Risk_desc"] = str(Risk_info["Risk_ALLT_FX"]["describe"]).replace("['","").replace("']","").replace("'","")
+
+    tpl.render(context)
+    addr = "/tmp/TaxRiskReport.docx"
+    # addr = "G://TaxRiskReport26.docx"
+    tpl.save(addr)
+    print(addr,"+++++++++")
+    return addr
+
+
+def put2oss(report):
+    # 阿里云接口地址
+    endpoint = 'http://oss-cn-beijing.aliyuncs.com'
+    auth = oss2.Auth('LTAIyAUK8AD04P5S', 'DHmRtFlw2Zr3KaRwUFeiu7FWATnmla')
+    bucket = oss2.Bucket(auth, endpoint, 'gsb-zc')
+    current_file_path = "TaxRiskReport.docx"
+    current_fold = time.strftime('%Y%m%d%H%M%S', time.localtime())
+    p = str(current_fold) + "_" + current_file_path
+    bucket.put_object_from_file(p, report)
+    addr="https://gsb-zc.oss-cn-beijing.aliyuncs.com/"+p
+    return addr
+
+#全半角转化函数
+def strQ2B(ustring):
+    rstring = ""
+    for uchar in ustring:
+        inside_code = ord(uchar)
+        if inside_code == 12288:  # 全角空格直接转换
+            inside_code = 32
+        elif (inside_code >= 65281 and inside_code <= 65374):  # 全角字符（除空格）根据关系转化
+            inside_code -= 65248
+        rstring += chr(inside_code)
+    return rstring
+
+
+# 调用更新报告接口
+def UpdateTaskRptUrl(batchid, rptUrl):
+    url = 'https://fkctlapi.gongsibao.com/api/rpt/rptApi/updateTaskRptUrl'  # 获取更新报告接口
+    report_addr = {"batchid": batchid, "rptUrl": rptUrl}  # 根据任务号进行接口访问参数
+    UpdateInfo = requests.post(url, report_addr)  #传参并访问接口
+    companyInfo_New = json.loads(UpdateInfo.text)# 访问接口返回的信息
+    print(companyInfo_New)
+    if companyInfo_New["msg"]=="操作成功":
+        return batchid+"报告已更新完成"
     else:
-        Risk_IT_XZCB_score="0%"
-        p1_describe=""
-        risk_describe="数据缺失"
-    Risk_IT_XZCB={
-        "score":Risk_IT_XZCB_score,
-        "describe":p1_describe,
-        "risk":risk_describe
-    }
-    return Risk_IT_XZCB
+        return batchid+"报告更新失败"
 
 
-# 计算增值税虚开发票风险异常
-def VAT_XKFP_Risk(dict_all):
-    VAT_XKFP_list = tax_desc.get("VAT_XKFP")  #获取增值税虚开发票指标名称
-    VAT_XKFP_dict = {}  #建立增值税虚开发票字典
-    for f1 in VAT_XKFP_list:
-        VAT_XKFP_dict[f1] = dict_all[f1]
-    #判断所有指标是否为缺失
-    Risk_not_null={}
-    for k1, v1 in VAT_XKFP_dict.items():
-        s_status1 = v1["status"]
-        if s_status1 >0:
-            Risk_not_null[k1]=VAT_XKFP_dict[k1]
-    if Risk_not_null:
-        satisfied = []
-        dissatisfied = []
-        part_s1_str_dict = {}
-        for k_part1, v_part1 in VAT_XKFP_dict.items():
-            s_s1 = v_part1["s_score"]
-            s_str = v_part1["factors_describe"]
-            s_status1 = v_part1["status"]
-            if s_status1 == 1:
-                satisfied.append(s_s1)
-            if s_status1 == 2:
-                part_s1_str_dict[s_s1] = s_str
-                dissatisfied.append(s_s1)
-        A_count = sum(satisfied) + sum(dissatisfied)  # 计算合格与不合格对应指标权重之和（分母）
-        B_count = sum(dissatisfied)  # 计算不合格对应指标权重之和（分子）
-        risk_VAT_XKFP = float("%.2f" % (B_count / A_count))  # 计算风险异常比例
-        Risk_VAT_XKFP_score = str(int(risk_VAT_XKFP * 100)) + "%"  # 格式转化
-        part_s1_str_dict = sorted(part_s1_str_dict.items(), key=lambda x: x[0], reverse=True)  # 按照权重排序
-        p1_describe = []  # 获取风险权重前三提示
-        for pt in part_s1_str_dict:
-            p1_describe.append(pt[1])
-        p1_describe = getUniqueItems(p1_describe)
-        if len(p1_describe) > 3:
-            p1_describe = p1_describe[:3]
-        # 风险类别获取
-        risk_describe = RiskCompareRule(B_count)
-    else:
-        Risk_VAT_XKFP_score="0%"
-        p1_describe=""
-        risk_describe="数据缺失"
-    Risk_VAT_XKFP={
-        "score":Risk_VAT_XKFP_score,
-        "describe":p1_describe,
-        "risk":risk_describe
-    }
-    return Risk_VAT_XKFP
-
-
-# 计算增值税虚增进项风险异常
-def VAT_XZJX_Risk(dict_all):
-    VAT_XZJX_list = tax_desc.get("VAT_XZJX")  #获取增值税虚增进项指标名称
-    VAT_XZJX_dict = {}  #建立增值税虚增进项字典
-    for f1 in VAT_XZJX_list:
-        VAT_XZJX_dict[f1] = dict_all[f1]
-    #判断所有指标是否为缺失
-    Risk_not_null={}
-    for k1, v1 in VAT_XZJX_dict.items():
-        s_status1 = v1["status"]
-        if s_status1 >0:
-            Risk_not_null[k1]=VAT_XZJX_dict[k1]
-    if Risk_not_null:
-        satisfied = []
-        dissatisfied = []
-        part_s1_str_dict = {}
-        for k_part1, v_part1 in VAT_XZJX_dict.items():
-            s_s1 = v_part1["s_score"]
-            s_str = v_part1["factors_describe"]
-            s_status1 = v_part1["status"]
-            if s_status1 == 1:
-                satisfied.append(s_s1)
-            if s_status1 == 2:
-                part_s1_str_dict[s_s1] = s_str
-                dissatisfied.append(s_s1)
-        A_count = sum(satisfied) + sum(dissatisfied)  # 计算合格与不合格对应指标权重之和（分母）
-        B_count = sum(dissatisfied)  # 计算不合格对应指标权重之和（分子）
-        risk_VAT_XZJX = float("%.2f" % (B_count / A_count))  # 计算风险异常比例
-        Risk_VAT_XZJX_score = str(int(risk_VAT_XZJX * 100)) + "%"  # 格式转化
-        part_s1_str_dict = sorted(part_s1_str_dict.items(), key=lambda x: x[0], reverse=True)  # 按照权重排序
-        p1_describe = []  # 获取风险权重前三提示
-        for pt in part_s1_str_dict:
-            p1_describe.append(pt[1])
-        p1_describe = getUniqueItems(p1_describe)
-        if len(p1_describe) > 3:
-            p1_describe = p1_describe[:3]
-        # 风险类别获取
-        risk_describe = RiskCompareRule(B_count)
-    else:
-        Risk_VAT_XZJX_score="0%"
-        p1_describe=""
-        risk_describe="数据缺失"
-    Risk_VAT_XZJX={
-        "score":Risk_VAT_XZJX_score,
-        "describe":p1_describe,
-        "risk":risk_describe
-    }
-    return Risk_VAT_XZJX
-
-
-# 计算增值税隐藏销项风险异常
-def VAT_YCXX_Risk(dict_all):
-    VAT_YCXX_list = tax_desc.get("VAT_YCXX")  #获取增值税隐藏销项指标名称
-    VAT_YCXX_dict = {}  #建立增值税隐藏销项字典
-    for f1 in VAT_YCXX_list:
-        VAT_YCXX_dict[f1] = dict_all[f1]
-    #判断所有指标是否为缺失
-    Risk_not_null={}
-    for k1, v1 in VAT_YCXX_dict.items():
-        s_status1 = v1["status"]
-        if s_status1 >0:
-            Risk_not_null[k1]=VAT_YCXX_dict[k1]
-    if Risk_not_null:
-        satisfied = []
-        dissatisfied = []
-        part_s1_str_dict = {}
-        for k_part1, v_part1 in VAT_YCXX_dict.items():
-            s_s1 = v_part1["s_score"]
-            s_str = v_part1["factors_describe"]
-            s_status1 = v_part1["status"]
-            if s_status1 == 1:
-                satisfied.append(s_s1)
-            if s_status1 == 2:
-                part_s1_str_dict[s_s1] = s_str
-                dissatisfied.append(s_s1)
-        A_count = sum(satisfied) + sum(dissatisfied)  # 计算合格与不合格对应指标权重之和（分母）
-        B_count = sum(dissatisfied)  # 计算不合格对应指标权重之和（分子）
-        risk_VAT_YCXX = float("%.2f" % (B_count / A_count))  # 计算风险异常比例
-        Risk_VAT_YCXX_score = str(int(risk_VAT_YCXX * 100)) + "%"  # 格式转化
-        part_s1_str_dict = sorted(part_s1_str_dict.items(), key=lambda x: x[0], reverse=True)  # 按照权重排序
-        p1_describe = []  # 获取风险权重前三提示
-        for pt in part_s1_str_dict:
-            p1_describe.append(pt[1])
-        p1_describe = getUniqueItems(p1_describe)
-        if len(p1_describe) > 3:
-            p1_describe = p1_describe[:3]
-        # 风险类别获取
-        risk_describe = RiskCompareRule(B_count)
-    else:
-        Risk_VAT_YCXX_score="0%"
-        p1_describe=""
-        risk_describe="数据缺失"
-    Risk_VAT_YCXX={
-        "score":Risk_VAT_YCXX_score,
-        "describe":p1_describe,
-        "risk":risk_describe
-    }
-    return Risk_VAT_YCXX
-
-# 计算消费税风险异常
-def CT_FX_Risk(dict_all):
-    CT_FX_list = tax_desc.get("CT_FX")  #获取消费税指标名称
-    CT_FX_dict = {}  #建立消费税字典
-    for f1 in CT_FX_list:
-        CT_FX_dict[f1] = dict_all[f1]
-    #判断所有指标是否为缺失
-    Risk_not_null={}
-    for k1, v1 in CT_FX_dict.items():
-        s_status1 = v1["status"]
-        if s_status1 >0:
-            Risk_not_null[k1]=CT_FX_dict[k1]
-    if Risk_not_null:
-        satisfied = []
-        dissatisfied = []
-        part_s1_str_dict = {}
-        for k_part1, v_part1 in CT_FX_dict.items():
-            s_s1 = v_part1["s_score"]
-            s_str = v_part1["factors_describe"]
-            s_status1 = v_part1["status"]
-            if s_status1 == 1:
-                satisfied.append(s_s1)
-            if s_status1 == 2:
-                part_s1_str_dict[s_s1] = s_str
-                dissatisfied.append(s_s1)
-        A_count = sum(satisfied) + sum(dissatisfied)  # 计算合格与不合格对应指标权重之和（分母）
-        B_count = sum(dissatisfied)  # 计算不合格对应指标权重之和（分子）
-        risk_CT_FX = float("%.2f" % (B_count / A_count))  # 计算风险异常比例
-        Risk_CT_FX_score = str(int(risk_CT_FX * 100)) + "%"  # 格式转化
-        part_s1_str_dict = sorted(part_s1_str_dict.items(), key=lambda x: x[0], reverse=True)  # 按照权重排序
-        p1_describe = []  # 获取风险权重前三提示
-        for pt in part_s1_str_dict:
-            p1_describe.append(pt[1])
-        p1_describe = getUniqueItems(p1_describe)
-        if len(p1_describe) > 3:
-            p1_describe = p1_describe[:3]
-        # 风险类别获取
-        risk_describe = RiskCompareRule(B_count)
-    else:
-        Risk_CT_FX_score="0%"
-        p1_describe=""
-        risk_describe="数据缺失"
-    Risk_CT_FX={
-        "score":Risk_CT_FX_score,
-        "describe":p1_describe,
-        "risk":risk_describe
-    }
-    return Risk_CT_FX
-
-# 综合
-def ALLT_FX_Risk(dict_all):
-    ALLT_FX_list = tax_desc.get("ALLT_FX")  #获取综合风险指标名称
-    ALLT_FX_dict = {}  #建立综合风险字典
-    for f1 in ALLT_FX_list:
-        ALLT_FX_dict[f1] = dict_all[f1]
-    #判断所有指标是否为缺失
-    Risk_not_null={}
-    for k1, v1 in ALLT_FX_dict.items():
-        s_status1 = v1["status"]
-        if s_status1 >0:
-            Risk_not_null[k1]=ALLT_FX_dict[k1]
-    if Risk_not_null:
-        satisfied = []
-        dissatisfied = []
-        part_s1_str_dict = {}
-        for k_part1, v_part1 in ALLT_FX_dict.items():
-            s_s1 = v_part1["s_score"]
-            s_str = v_part1["factors_describe"]
-            s_status1 = v_part1["status"]
-            if s_status1 == 1:
-                satisfied.append(s_s1)
-            if s_status1 == 2:
-                part_s1_str_dict[s_s1] = s_str
-                dissatisfied.append(s_s1)
-        A_count = sum(satisfied) + sum(dissatisfied)  # 计算合格与不合格对应指标权重之和（分母）
-        B_count = sum(dissatisfied)  # 计算不合格对应指标权重之和（分子）
-        risk_ALLT_FX = float("%.2f" % (B_count / A_count))  # 计算风险异常比例
-        Risk_ALLT_FX_score = str(int(risk_ALLT_FX * 100)) + "%"  # 格式转化
-        part_s1_str_dict = sorted(part_s1_str_dict.items(), key=lambda x: x[0], reverse=True)  # 按照权重排序
-
-        p1_describe = []  # 获取风险权重前三提示
-        for pt in part_s1_str_dict:
-            p1_describe.append(pt[1])
-        p1_describe = getUniqueItems(p1_describe)
-        if len(p1_describe) > 3:
-            p1_describe = p1_describe[:3]
-        # 风险类别获取
-        risk_describe = RiskCompareRule(B_count)
-    else:
-        Risk_ALLT_FX_score="0%"
-        p1_describe=""
-        risk_describe="数据缺失"
-    Risk_ALLT_FX={
-        "score":Risk_ALLT_FX_score,
-        "describe":p1_describe,
-        "risk":risk_describe
-    }
-    return Risk_ALLT_FX
 
 
-
-
-# 风险监测信息接口
-@server.route('/gsb/api/report', methods=['POST'])
+# 风险监测自动报告接口
+@server.route('/gsb/api/report2', methods=['POST'])
 def report():
     if request.method =='POST':
+        print("=======调用风险检测自动报告接口")
         data = request.json
         json_str = json.dumps(data)
-        new_status = json.loads(json_str)
-        dict_all, ALLRisk = CreatBdictFromJson(new_status)
-        RiskA=RiskAbnormal(ALLRisk["t_r1"])   #风险异常比例
-        MRisk=MonitoredRisk(ALLRisk["t_r2"])  #被监控风险
-        ARisk=AuditedRisk(ALLRisk["t_r3"])   #被稽查风险
-        info=information(ALLRisk["t_r1"])    #提示语
-        Risk_IT_YCSR=IT_YCSR_Risk(dict_all)  #企业所得税隐藏收入风险信息
-        Risk_IT_XZFY = IT_XZFY_Risk(dict_all) #企业所得税虚增费用风险信息
-        Risk_IT_XZCB = IT_XZCB_Risk(dict_all) #企业所得税虚增成本风险信息
-        Risk_VAT_XKFP=VAT_XKFP_Risk(dict_all) #企业增值税虚开发票风险信息
-        Risk_VAT_XZJX = VAT_XZJX_Risk(dict_all) #企业增值税虚增进项风险信息
-        Risk_VAT_YCXX = VAT_YCXX_Risk(dict_all) #企业增值税隐藏销项风险信息
-        Risk_CT_FX=CT_FX_Risk(dict_all)    #消费税风险信息
-        Risk_ALLT_FX=ALLT_FX_Risk(dict_all)  #综合风险信息
-        result={
-            "RiskA":RiskA,
-            "MRisk":MRisk,
-            "ARisk":ARisk,
-            "info":info,
-            "Risk_IT_YCSR":Risk_IT_YCSR,
-            "Risk_IT_XZFY":Risk_IT_XZFY, 
-            "Risk_IT_XZCB":Risk_IT_XZCB,
-            "Risk_VAT_XKFP": Risk_VAT_XKFP,
-            "Risk_VAT_XZJX": Risk_VAT_XZJX,
-            "Risk_VAT_YCXX": Risk_VAT_YCXX,
-            "Risk_CT_FX": Risk_CT_FX,
-            "Risk_ALLT_FX": Risk_ALLT_FX
-        }
-        print(result,"------------------------------")
-        return jsonify(result)
+        GetAllinfo = json.loads(json_str)
+        companyname=GetAllinfo["company_name"] #获取监测企业名称
+        batchid=GetAllinfo["Task_Num"] #获取任务号
+        companyname = strQ2B(companyname)
+        if companyname:
+            Companyinfo=GetComanyinfoFromES(companyname) #根据输入公司名获取工商信息
+            Else_info, Fi_dict=GetFIinfoFromJson(GetAllinfo)#获取输入其他信息和财务信息
+            Risk_info=Riskinfo(GetAllinfo)#计算风险项
+            report = WriteReport(Else_info,Companyinfo,Fi_dict,Risk_info) #将工商、财务、风险三部分内容写入word中
+            rptUrl = put2oss(report) # 上传文件到oss上
+            print("报告已生成",rptUrl)
+            result=UpdateTaskRptUrl(batchid, rptUrl)# 完成后调用接口，通知服务已完成
+            return result
+        else:
+            return "请输入检测公司名称"
 
 
-server.run(host='0.0.0.0', port=80, debug=True)
 
+server.run(host='0.0.0.0', port=80, debug=True)