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蒋勇
zhichan
Commits
cfffb64a
Commit
cfffb64a
authored
5 years ago
by
宋毅
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fanhui_third
parent
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rpt_result/main.py
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rpt_result/main.py
View file @
cfffb64a
...
@@ -32,12 +32,12 @@ def CreatBdictFromJson(new_status):
...
@@ -32,12 +32,12 @@ def CreatBdictFromJson(new_status):
"status"
:
""
"status"
:
""
}
}
dict_all
[
linesplit
[
4
]
.
replace
(
'"'
,
""
)]
=
tax_index
dict_all
[
linesplit
[
4
]
.
replace
(
'"'
,
""
)]
=
tax_index
print
(
dict_all
)
#
print(dict_all)
# 获取传入的参数
# 获取传入的参数
new_status
=
new_status
new_status
=
new_status
for
factors_name
,
value
in
dict_all
.
items
():
for
factors_name
,
value
in
dict_all
.
items
():
dict_all
[
factors_name
][
"status"
]
=
new_status
[
factors_name
]
dict_all
[
factors_name
][
"status"
]
=
new_status
[
factors_name
]
print
(
dict_all
)
#
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
# 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
qualified
=
[]
# 合格
...
@@ -66,16 +66,16 @@ def CreatBdictFromJson(new_status):
...
@@ -66,16 +66,16 @@ def CreatBdictFromJson(new_status):
for
k1
,
v1
in
rules
.
items
():
for
k1
,
v1
in
rules
.
items
():
if
k1
:
if
k1
:
ALLRisk
=
v1
ALLRisk
=
v1
print
(
ALLRisk
)
print
(
ALLRisk
,
"+++++++++++++"
)
return
dict_all
,
ALLRisk
return
dict_all
,
ALLRisk
# 输入计算出的风险异常比例,返回提示结果
# 输入计算出的风险异常比例,返回提示结果
def
RiskAbnormal
(
t_r1
):
def
RiskAbnormal
(
t_r1
):
RiskA
=
{}
RiskA
=
{}
t_r1
=
t_r1
t_r1
_s
=
str
(
int
(
t_r1
.
replace
(
"
%
"
,
""
))
/
100
)
rules_1
=
{
"t_r1
<='30
%
'"
:
"企业风险异常同比其他企业比例较低"
,
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
>'30
%
'"
:
"企业风险异常同比其他企业比例明显偏高"
"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
():
for
k1
,
v1
in
rules_1
.
items
():
if
eval
(
str
(
k1
)):
if
eval
(
str
(
k1
)):
...
@@ -85,23 +85,23 @@ def RiskAbnormal(t_r1):
...
@@ -85,23 +85,23 @@ def RiskAbnormal(t_r1):
# 输入计算出的被检测风险值,判断被监测风险结果
# 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
):
def
MonitoredRisk
(
t_r2
):
MRisk
=
{}
MRisk
=
{}
t_r2
=
t_r2
t_r2
_s
=
str
(
int
(
t_r2
.
replace
(
"
%
"
,
""
))
/
100
)
rules_2
=
{
"t_r2
<='10
%
'"
:
"企业被监控的风险同比其他企业比例较低"
,
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
>'10
%
' and t_r2<='50
%
'"
:
"经检测,企业有一定可能性被选为税务监控对象"
,
"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
>'50
%
'"
:
"经检测,企业有很大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
():
for
k2
,
v2
in
rules_2
.
items
():
if
eval
(
str
(
k2
)):
if
eval
(
str
(
k2
)):
MRisk
=
{
"score"
:
t_r2
,
"describe"
:
v2
}
MRisk
=
{
"score"
:
t_r2
,
"describe"
:
v2
}
return
MRisk
return
MRisk
# 输入计算出的被稽查风险值,判断被稽查风险结果
# 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
):
def
AuditedRisk
(
t_r3
):
ARisk
=
{}
ARisk
=
{}
t_r3
=
t_r3
t_r3
_s
=
str
(
int
(
t_r3
.
replace
(
"
%
"
,
""
))
/
100
)
rules_3
=
{
"t_r3
<='10
%
'"
:
"企业被税务稽查的可能性较低"
,
rules_3
=
{
"t_r3
_s<='0.1
'"
:
"企业被税务稽查的可能性较低"
,
"t_r3
>'10
%
' and t_r3<='30
%
'"
:
"企业有一定可能性被税务稽查"
,
"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
>'30
%
'"
:
"企业有很大可能性被税务稽查"
"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
():
for
k3
,
v3
in
rules_3
.
items
():
if
eval
(
str
(
k3
)):
if
eval
(
str
(
k3
)):
...
@@ -111,15 +111,15 @@ def AuditedRisk(t_r3):
...
@@ -111,15 +111,15 @@ def AuditedRisk(t_r3):
# 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)
# 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
):
def
information
(
t_r1
):
info
=
{}
info
=
{}
t_r1
=
t_r1
t_r1
_s
=
str
(
int
(
t_r1
.
replace
(
"
%
"
,
""
))
/
100
)
rules_0
=
{
"t_r1
<='30
%
'"
:
"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>>"
,
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
>'30
%
' and t_r1<='60
%
'"
:
"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>'0.3' 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
>'60
%
' and t_r1<='80
%
'"
:
"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>'0.6' 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
>'80
%
'"
:
"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>>"
"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
():
for
k0
,
v0
in
rules_0
.
items
():
if
eval
(
str
(
k0
)):
if
eval
(
str
(
k0
)):
info
=
{
{
"score"
:
t_r1
,
"describe"
:
v0
}
}
info
=
{
"score"
:
t_r1
,
"describe"
:
v0
}
return
info
return
info
...
@@ -263,6 +263,15 @@ def RiskCompareRule(wx_part):
...
@@ -263,6 +263,15 @@ def RiskCompareRule(wx_part):
else
:
else
:
risk_describe
=
"数据缺失"
risk_describe
=
"数据缺失"
return
risk_describe
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)
# 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)
...
@@ -290,20 +299,18 @@ def IT_YCSR_Risk(dict_all):
...
@@ -290,20 +299,18 @@ def IT_YCSR_Risk(dict_all):
if
s_status1
==
2
:
if
s_status1
==
2
:
part_s1_str_dict
[
s_s1
]
=
s_str
part_s1_str_dict
[
s_s1
]
=
s_str
dissatisfied
.
append
(
s_s1
)
dissatisfied
.
append
(
s_s1
)
print
(
part_s1_str_dict
,
type
(
part_s1_str_dict
))
A_count
=
sum
(
satisfied
)
+
sum
(
dissatisfied
)
# 计算合格与不合格对应指标权重之和(分母)
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)
B_count
=
sum
(
dissatisfied
)
# 计算不合格对应指标权重之和(分子)
risk_IT_YCSR
=
float
(
"
%.2
f"
%
(
B_count
/
A_count
))
# 计算风险异常比例
risk_IT_YCSR
=
float
(
"
%.2
f"
%
(
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
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
part_s1_str_dict
=
sorted
(
part_s1_str_dict
.
items
(),
key
=
lambda
x
:
x
[
0
],
reverse
=
True
)
# 按照权重排序
# print(part_s1_str_dict)
p1_describe
=
[]
# 获取风险权重前三提示
p1_describe
=
[]
# 获取风险权重前三提示
for
pt
in
part_s1_str_dict
:
for
pt
in
part_s1_str_dict
:
p1_describe
.
append
(
pt
[
1
])
p1_describe
.
append
(
pt
[
1
])
p1_describe
=
getUniqueItems
(
p1_describe
)
#风险描述去重
if
len
(
p1_describe
)
>
3
:
if
len
(
p1_describe
)
>
3
:
p1_describe
=
p1_describe
[:
3
]
p1_describe
=
p1_describe
[:
3
]
# 风险类别获取
# 风险类别获取
print
(
B_count
,
"--------------------------"
)
risk_describe
=
RiskCompareRule
(
B_count
)
risk_describe
=
RiskCompareRule
(
B_count
)
else
:
else
:
Risk_IT_YCSR_score
=
"0
%
"
Risk_IT_YCSR_score
=
"0
%
"
...
@@ -340,7 +347,7 @@ def IT_XZFY_Risk(dict_all):
...
@@ -340,7 +347,7 @@ def IT_XZFY_Risk(dict_all):
if
s_status2
==
1
:
if
s_status2
==
1
:
satisfied
.
append
(
s_s2
)
satisfied
.
append
(
s_s2
)
if
s_status2
==
2
:
if
s_status2
==
2
:
part_s2_str_dict
[
s_s2
]
=
[
s_str2
]
part_s2_str_dict
[
s_s2
]
=
s_str2
dissatisfied
.
append
(
s_s2
)
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)
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)
B_count
=
sum
(
dissatisfied
)
# 计算不合格对应指标权重之和(分子)
...
@@ -350,6 +357,7 @@ def IT_XZFY_Risk(dict_all):
...
@@ -350,6 +357,7 @@ def IT_XZFY_Risk(dict_all):
p2_describe
=
[]
# 获取风险权重前三提示
p2_describe
=
[]
# 获取风险权重前三提示
for
pt
in
part_s2_str_dict
:
for
pt
in
part_s2_str_dict
:
p2_describe
.
append
(
pt
[
1
])
p2_describe
.
append
(
pt
[
1
])
p2_describe
=
getUniqueItems
(
p2_describe
)
if
len
(
p2_describe
)
>
3
:
if
len
(
p2_describe
)
>
3
:
p2_describe
=
p2_describe
[:
3
]
p2_describe
=
p2_describe
[:
3
]
# 风险类别获取
# 风险类别获取
...
@@ -388,7 +396,7 @@ def IT_XZCB_Risk(dict_all):
...
@@ -388,7 +396,7 @@ def IT_XZCB_Risk(dict_all):
if
s_status1
==
1
:
if
s_status1
==
1
:
satisfied
.
append
(
s_s1
)
satisfied
.
append
(
s_s1
)
if
s_status1
==
2
:
if
s_status1
==
2
:
part_s1_str_dict
[
s_s1
]
=
[
s_str
]
part_s1_str_dict
[
s_s1
]
=
s_str
dissatisfied
.
append
(
s_s1
)
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)
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)
B_count
=
sum
(
dissatisfied
)
# 计算不合格对应指标权重之和(分子)
...
@@ -398,6 +406,7 @@ def IT_XZCB_Risk(dict_all):
...
@@ -398,6 +406,7 @@ def IT_XZCB_Risk(dict_all):
p1_describe
=
[]
# 获取风险权重前三提示
p1_describe
=
[]
# 获取风险权重前三提示
for
pt
in
part_s1_str_dict
:
for
pt
in
part_s1_str_dict
:
p1_describe
.
append
(
pt
[
1
])
p1_describe
.
append
(
pt
[
1
])
p1_describe
=
getUniqueItems
(
p1_describe
)
if
len
(
p1_describe
)
>
3
:
if
len
(
p1_describe
)
>
3
:
p1_describe
=
p1_describe
[:
3
]
p1_describe
=
p1_describe
[:
3
]
# 风险类别获取
# 风险类别获取
...
@@ -437,7 +446,7 @@ def VAT_XKFP_Risk(dict_all):
...
@@ -437,7 +446,7 @@ def VAT_XKFP_Risk(dict_all):
if
s_status1
==
1
:
if
s_status1
==
1
:
satisfied
.
append
(
s_s1
)
satisfied
.
append
(
s_s1
)
if
s_status1
==
2
:
if
s_status1
==
2
:
part_s1_str_dict
[
s_s1
]
=
[
s_str
]
part_s1_str_dict
[
s_s1
]
=
s_str
dissatisfied
.
append
(
s_s1
)
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)
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)
B_count
=
sum
(
dissatisfied
)
# 计算不合格对应指标权重之和(分子)
...
@@ -447,6 +456,7 @@ def VAT_XKFP_Risk(dict_all):
...
@@ -447,6 +456,7 @@ def VAT_XKFP_Risk(dict_all):
p1_describe
=
[]
# 获取风险权重前三提示
p1_describe
=
[]
# 获取风险权重前三提示
for
pt
in
part_s1_str_dict
:
for
pt
in
part_s1_str_dict
:
p1_describe
.
append
(
pt
[
1
])
p1_describe
.
append
(
pt
[
1
])
p1_describe
=
getUniqueItems
(
p1_describe
)
if
len
(
p1_describe
)
>
3
:
if
len
(
p1_describe
)
>
3
:
p1_describe
=
p1_describe
[:
3
]
p1_describe
=
p1_describe
[:
3
]
# 风险类别获取
# 风险类别获取
...
@@ -486,7 +496,7 @@ def VAT_XZJX_Risk(dict_all):
...
@@ -486,7 +496,7 @@ def VAT_XZJX_Risk(dict_all):
if
s_status1
==
1
:
if
s_status1
==
1
:
satisfied
.
append
(
s_s1
)
satisfied
.
append
(
s_s1
)
if
s_status1
==
2
:
if
s_status1
==
2
:
part_s1_str_dict
[
s_s1
]
=
[
s_str
]
part_s1_str_dict
[
s_s1
]
=
s_str
dissatisfied
.
append
(
s_s1
)
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)
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)
B_count
=
sum
(
dissatisfied
)
# 计算不合格对应指标权重之和(分子)
...
@@ -496,6 +506,7 @@ def VAT_XZJX_Risk(dict_all):
...
@@ -496,6 +506,7 @@ def VAT_XZJX_Risk(dict_all):
p1_describe
=
[]
# 获取风险权重前三提示
p1_describe
=
[]
# 获取风险权重前三提示
for
pt
in
part_s1_str_dict
:
for
pt
in
part_s1_str_dict
:
p1_describe
.
append
(
pt
[
1
])
p1_describe
.
append
(
pt
[
1
])
p1_describe
=
getUniqueItems
(
p1_describe
)
if
len
(
p1_describe
)
>
3
:
if
len
(
p1_describe
)
>
3
:
p1_describe
=
p1_describe
[:
3
]
p1_describe
=
p1_describe
[:
3
]
# 风险类别获取
# 风险类别获取
...
@@ -535,7 +546,7 @@ def VAT_YCXX_Risk(dict_all):
...
@@ -535,7 +546,7 @@ def VAT_YCXX_Risk(dict_all):
if
s_status1
==
1
:
if
s_status1
==
1
:
satisfied
.
append
(
s_s1
)
satisfied
.
append
(
s_s1
)
if
s_status1
==
2
:
if
s_status1
==
2
:
part_s1_str_dict
[
s_s1
]
=
[
s_str
]
part_s1_str_dict
[
s_s1
]
=
s_str
dissatisfied
.
append
(
s_s1
)
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)
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)
B_count
=
sum
(
dissatisfied
)
# 计算不合格对应指标权重之和(分子)
...
@@ -545,6 +556,7 @@ def VAT_YCXX_Risk(dict_all):
...
@@ -545,6 +556,7 @@ def VAT_YCXX_Risk(dict_all):
p1_describe
=
[]
# 获取风险权重前三提示
p1_describe
=
[]
# 获取风险权重前三提示
for
pt
in
part_s1_str_dict
:
for
pt
in
part_s1_str_dict
:
p1_describe
.
append
(
pt
[
1
])
p1_describe
.
append
(
pt
[
1
])
p1_describe
=
getUniqueItems
(
p1_describe
)
if
len
(
p1_describe
)
>
3
:
if
len
(
p1_describe
)
>
3
:
p1_describe
=
p1_describe
[:
3
]
p1_describe
=
p1_describe
[:
3
]
# 风险类别获取
# 风险类别获取
...
@@ -583,7 +595,7 @@ def CT_FX_Risk(dict_all):
...
@@ -583,7 +595,7 @@ def CT_FX_Risk(dict_all):
if
s_status1
==
1
:
if
s_status1
==
1
:
satisfied
.
append
(
s_s1
)
satisfied
.
append
(
s_s1
)
if
s_status1
==
2
:
if
s_status1
==
2
:
part_s1_str_dict
[
s_s1
]
=
[
s_str
]
part_s1_str_dict
[
s_s1
]
=
s_str
dissatisfied
.
append
(
s_s1
)
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)
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)
B_count
=
sum
(
dissatisfied
)
# 计算不合格对应指标权重之和(分子)
...
@@ -593,6 +605,7 @@ def CT_FX_Risk(dict_all):
...
@@ -593,6 +605,7 @@ def CT_FX_Risk(dict_all):
p1_describe
=
[]
# 获取风险权重前三提示
p1_describe
=
[]
# 获取风险权重前三提示
for
pt
in
part_s1_str_dict
:
for
pt
in
part_s1_str_dict
:
p1_describe
.
append
(
pt
[
1
])
p1_describe
.
append
(
pt
[
1
])
p1_describe
=
getUniqueItems
(
p1_describe
)
if
len
(
p1_describe
)
>
3
:
if
len
(
p1_describe
)
>
3
:
p1_describe
=
p1_describe
[:
3
]
p1_describe
=
p1_describe
[:
3
]
# 风险类别获取
# 风险类别获取
...
@@ -631,16 +644,18 @@ def ALLT_FX_Risk(dict_all):
...
@@ -631,16 +644,18 @@ def ALLT_FX_Risk(dict_all):
if
s_status1
==
1
:
if
s_status1
==
1
:
satisfied
.
append
(
s_s1
)
satisfied
.
append
(
s_s1
)
if
s_status1
==
2
:
if
s_status1
==
2
:
part_s1_str_dict
[
s_s1
]
=
[
s_str
]
part_s1_str_dict
[
s_s1
]
=
s_str
dissatisfied
.
append
(
s_s1
)
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)
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)
B_count
=
sum
(
dissatisfied
)
# 计算不合格对应指标权重之和(分子)
risk_ALLT_FX
=
float
(
"
%.2
f"
%
(
B_count
/
A_count
))
# 计算风险异常比例
risk_ALLT_FX
=
float
(
"
%.2
f"
%
(
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
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
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
p1_describe
=
[]
# 获取风险权重前三提示
for
pt
in
part_s1_str_dict
:
for
pt
in
part_s1_str_dict
:
p1_describe
.
append
(
pt
[
1
])
p1_describe
.
append
(
pt
[
1
])
p1_describe
=
getUniqueItems
(
p1_describe
)
if
len
(
p1_describe
)
>
3
:
if
len
(
p1_describe
)
>
3
:
p1_describe
=
p1_describe
[:
3
]
p1_describe
=
p1_describe
[:
3
]
# 风险类别获取
# 风险类别获取
...
@@ -696,6 +711,6 @@ def report():
...
@@ -696,6 +711,6 @@ def report():
print
(
result
,
"------------------------------"
)
print
(
result
,
"------------------------------"
)
return
jsonify
(
result
)
return
jsonify
(
result
)
server
.
run
(
host
=
'121.0.0.1'
,
port
=
80
,
debug
=
True
)
server
.
run
(
host
=
'121.0.0.1'
,
port
=
80
,
debug
=
True
)
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