Overview

Dataset statistics

Number of variables9
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory77.3 KiB
Average record size in memory79.1 B

Variable types

Text1
Numeric5
Categorical2
DateTime1

Dataset

Description한국주택금융공사 주택연금부 업무 관련 공개 공공데이터 (해당 부서의 업무와 관련된 데이터베이스에서 공개 가능한 원천 데이터)
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15073132/fileData.do

Alerts

JUDGE_DAY is highly overall correlated with INSP_DY and 1 other fieldsHigh correlation
INSP_DY is highly overall correlated with JUDGE_DAY and 1 other fieldsHigh correlation
PTTN_NO is highly overall correlated with JUDGE_DAY and 1 other fieldsHigh correlation
TRGT_PERS_DIV is highly imbalanced (92.2%)Imbalance
JUDGE_DAY is highly skewed (γ1 = -27.40310325)Skewed
PTTN_NO has unique valuesUnique

Reproduction

Analysis started2023-12-12 09:14:03.953735
Analysis finished2023-12-12 09:14:07.811552
Duration3.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

GUARNT_NO
Text

Distinct861
Distinct (%)86.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-12-12T18:14:08.090387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters14000
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique771 ?
Unique (%)77.1%

Sample

1st rowRTOA2019000215
2nd rowRTAD2018000231
3rd rowRTOA2020000221
4th rowRTQB2020000051
5th rowRTBA2012000239
ValueCountFrequency (%)
rtab2018000639 22
 
2.2%
rqad2007000193 6
 
0.6%
rtba2018001035 6
 
0.6%
rtaa2019000598 6
 
0.6%
rtad2018000268 5
 
0.5%
rtho2018000106 4
 
0.4%
rtab2018000837 3
 
0.3%
rtad2019000616 3
 
0.3%
rtab2015000525 3
 
0.3%
rtab2020000429 3
 
0.3%
Other values (851) 939
93.9%
2023-12-12T18:14:08.614992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4541
32.4%
2 1525
 
10.9%
1 1247
 
8.9%
R 1002
 
7.2%
A 927
 
6.6%
T 898
 
6.4%
9 464
 
3.3%
8 412
 
2.9%
7 386
 
2.8%
6 382
 
2.7%
Other values (14) 2216
15.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
71.4%
Uppercase Letter 4000
 
28.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 1002
25.1%
A 927
23.2%
T 898
22.4%
B 364
 
9.1%
H 234
 
5.9%
D 163
 
4.1%
Q 136
 
3.4%
O 97
 
2.4%
C 84
 
2.1%
P 35
 
0.9%
Other values (4) 60
 
1.5%
Decimal Number
ValueCountFrequency (%)
0 4541
45.4%
2 1525
 
15.2%
1 1247
 
12.5%
9 464
 
4.6%
8 412
 
4.1%
7 386
 
3.9%
6 382
 
3.8%
5 380
 
3.8%
3 361
 
3.6%
4 302
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
71.4%
Latin 4000
 
28.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 1002
25.1%
A 927
23.2%
T 898
22.4%
B 364
 
9.1%
H 234
 
5.9%
D 163
 
4.1%
Q 136
 
3.4%
O 97
 
2.4%
C 84
 
2.1%
P 35
 
0.9%
Other values (4) 60
 
1.5%
Common
ValueCountFrequency (%)
0 4541
45.4%
2 1525
 
15.2%
1 1247
 
12.5%
9 464
 
4.6%
8 412
 
4.1%
7 386
 
3.9%
6 382
 
3.8%
5 380
 
3.8%
3 361
 
3.6%
4 302
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4541
32.4%
2 1525
 
10.9%
1 1247
 
8.9%
R 1002
 
7.2%
A 927
 
6.6%
T 898
 
6.4%
9 464
 
3.3%
8 412
 
2.9%
7 386
 
2.8%
6 382
 
2.7%
Other values (14) 2216
15.8%

INDIV_SEQ
Real number (ℝ)

Distinct36
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.742
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T18:14:08.802881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum36
Range35
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.0407628
Coefficient of variation (CV)1.4736553
Kurtosis30.96178
Mean2.742
Median Absolute Deviation (MAD)1
Skewness5.1820056
Sum2742
Variance16.327764
MonotonicityNot monotonic
2023-12-12T18:14:08.998456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1 410
41.0%
2 335
33.5%
3 109
 
10.9%
4 42
 
4.2%
5 26
 
2.6%
6 19
 
1.9%
7 10
 
1.0%
9 7
 
0.7%
8 6
 
0.6%
12 4
 
0.4%
Other values (26) 32
 
3.2%
ValueCountFrequency (%)
1 410
41.0%
2 335
33.5%
3 109
 
10.9%
4 42
 
4.2%
5 26
 
2.6%
6 19
 
1.9%
7 10
 
1.0%
8 6
 
0.6%
9 7
 
0.7%
10 2
 
0.2%
ValueCountFrequency (%)
36 1
0.1%
35 1
0.1%
34 1
0.1%
33 1
0.1%
32 1
0.1%
31 1
0.1%
30 1
0.1%
29 1
0.1%
28 1
0.1%
27 1
0.1%

JUDGE_DAY
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct212
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20199975
Minimum20000310
Maximum20220522
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T18:14:09.176688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20000310
5-th percentile20200103
Q120200310
median20200519
Q320200810
95-th percentile20201014
Maximum20220522
Range220212
Interquartile range (IQR)500.25

Descriptive statistics

Standard deviation6634.8073
Coefficient of variation (CV)0.00032845622
Kurtosis823.12537
Mean20199975
Median Absolute Deviation (MAD)216
Skewness-27.403103
Sum2.0199975 × 1010
Variance44020669
MonotonicityNot monotonic
2023-12-12T18:14:09.375082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200421 12
 
1.2%
20191230 12
 
1.2%
20200928 11
 
1.1%
20200420 10
 
1.0%
20200113 9
 
0.9%
20200212 9
 
0.9%
20200519 9
 
0.9%
20200427 9
 
0.9%
20200604 9
 
0.9%
20200324 9
 
0.9%
Other values (202) 901
90.1%
ValueCountFrequency (%)
20000310 1
 
0.1%
20191223 3
 
0.3%
20191224 9
0.9%
20191226 6
0.6%
20191227 8
0.8%
20191230 12
1.2%
20191231 5
0.5%
20200102 5
0.5%
20200103 7
0.7%
20200106 4
 
0.4%
ValueCountFrequency (%)
20220522 1
 
0.1%
20201026 7
0.7%
20201023 5
0.5%
20201022 7
0.7%
20201021 2
 
0.2%
20201020 7
0.7%
20201019 3
 
0.3%
20201016 9
0.9%
20201015 5
0.5%
20201014 6
0.6%

INSP_DY
Real number (ℝ)

HIGH CORRELATION 

Distinct208
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20200134
Minimum20191220
Maximum20201026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T18:14:09.551877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20191220
5-th percentile20200102
Q120200310
median20200519
Q320200806
95-th percentile20201013
Maximum20201026
Range9806
Interquartile range (IQR)496.25

Descriptive statistics

Standard deviation1954.064
Coefficient of variation (CV)9.6735197 × 10-5
Kurtosis16.572003
Mean20200134
Median Absolute Deviation (MAD)216
Skewness-4.2538251
Sum2.0200134 × 1010
Variance3818365.9
MonotonicityNot monotonic
2023-12-12T18:14:09.706704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200420 14
 
1.4%
20200925 11
 
1.1%
20191227 11
 
1.1%
20200824 11
 
1.1%
20200316 10
 
1.0%
20200908 10
 
1.0%
20200102 9
 
0.9%
20200113 9
 
0.9%
20201023 9
 
0.9%
20200616 9
 
0.9%
Other values (198) 897
89.7%
ValueCountFrequency (%)
20191220 1
 
0.1%
20191223 9
0.9%
20191224 7
0.7%
20191226 8
0.8%
20191227 11
1.1%
20191230 7
0.7%
20191231 2
 
0.2%
20200102 9
0.9%
20200103 4
 
0.4%
20200106 9
0.9%
ValueCountFrequency (%)
20201026 1
 
0.1%
20201023 9
0.9%
20201022 6
0.6%
20201021 3
 
0.3%
20201020 3
 
0.3%
20201019 7
0.7%
20201016 3
 
0.3%
20201015 8
0.8%
20201014 7
0.7%
20201013 4
0.4%

REQ_DIV
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2
590 
1
410 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 590
59.0%
1 410
41.0%

Length

2023-12-12T18:14:09.854326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:14:09.978199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 590
59.0%
1 410
41.0%

TRGT_PERS_DIV
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
982 
2
 
8
3
 
7
99
 
3

Length

Max length2
Median length1
Mean length1.003
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 982
98.2%
2 8
 
0.8%
3 7
 
0.7%
99 3
 
0.3%

Length

2023-12-12T18:14:10.087714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:14:10.219288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 982
98.2%
2 8
 
0.8%
3 7
 
0.7%
99 3
 
0.3%

REG_ENO
Real number (ℝ)

Distinct100
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1767.908
Minimum1174
Maximum6018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T18:14:10.365734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1174
5-th percentile1385
Q11569
median1689
Q31866
95-th percentile2001
Maximum6018
Range4844
Interquartile range (IQR)297

Descriptive statistics

Standard deviation558.67741
Coefficient of variation (CV)0.31601045
Kurtosis47.640526
Mean1767.908
Median Absolute Deviation (MAD)132
Skewness6.5652843
Sum1767908
Variance312120.44
MonotonicityNot monotonic
2023-12-12T18:14:10.557804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1689 73
 
7.3%
1656 47
 
4.7%
2001 45
 
4.5%
1799 45
 
4.5%
1753 44
 
4.4%
1475 41
 
4.1%
1406 36
 
3.6%
1686 35
 
3.5%
1650 35
 
3.5%
1691 30
 
3.0%
Other values (90) 569
56.9%
ValueCountFrequency (%)
1174 22
2.2%
1304 6
 
0.6%
1331 1
 
0.1%
1371 9
 
0.9%
1375 1
 
0.1%
1385 27
2.7%
1406 36
3.6%
1410 3
 
0.3%
1446 1
 
0.1%
1469 1
 
0.1%
ValueCountFrequency (%)
6018 15
 
1.5%
2003 14
 
1.4%
2002 1
 
0.1%
2001 45
4.5%
2000 6
 
0.6%
1999 2
 
0.2%
1997 1
 
0.1%
1987 4
 
0.4%
1982 4
 
0.4%
1978 7
 
0.7%

REG_TS
Date

Distinct999
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minimum2019-12-20 19:59:32
Maximum2020-10-26 11:31:30
2023-12-12T18:14:10.749865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:10.929040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

PTTN_NO
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0205751 × 1010
Minimum2.0196201 × 1010
Maximum2.0206201 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T18:14:11.111083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0196201 × 1010
5-th percentile2.02062 × 1010
Q12.02062 × 1010
median2.02062 × 1010
Q32.0206201 × 1010
95-th percentile2.0206201 × 1010
Maximum2.0206201 × 1010
Range9999711
Interquartile range (IQR)532.5

Descriptive statistics

Standard deviation2073909.2
Coefficient of variation (CV)0.00010263955
Kurtosis17.362025
Mean2.0205751 × 1010
Median Absolute Deviation (MAD)266.5
Skewness-4.396283
Sum2.0205751 × 1013
Variance4.3010993 × 1012
MonotonicityNot monotonic
2023-12-12T18:14:11.301186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20206201020 1
 
0.1%
20206200307 1
 
0.1%
20206200321 1
 
0.1%
20206200320 1
 
0.1%
20206200319 1
 
0.1%
20206200318 1
 
0.1%
20206200316 1
 
0.1%
20206200315 1
 
0.1%
20206200317 1
 
0.1%
20206200313 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
20196201309 1
0.1%
20196201310 1
0.1%
20196201311 1
0.1%
20196201312 1
0.1%
20196201313 1
0.1%
20196201314 1
0.1%
20196201315 1
0.1%
20196201316 1
0.1%
20196201317 1
0.1%
20196201318 1
0.1%
ValueCountFrequency (%)
20206201020 1
0.1%
20206201019 1
0.1%
20206201018 1
0.1%
20206201017 1
0.1%
20206201016 1
0.1%
20206201015 1
0.1%
20206201014 1
0.1%
20206201013 1
0.1%
20206201012 1
0.1%
20206201011 1
0.1%

Interactions

2023-12-12T18:14:06.385832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:04.323081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:04.758316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:05.171024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:05.724190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:06.524935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:04.414626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:04.850042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:05.271300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:05.950487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:06.662526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:04.500530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:04.922730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:05.366725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:06.055575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:06.807661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:04.579745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:05.000752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:05.472809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:06.182441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:06.951042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:04.668190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:05.079739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:05.617916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:06.282581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:14:11.444497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
INDIV_SEQJUDGE_DAYINSP_DYREQ_DIVTRGT_PERS_DIVREG_ENOPTTN_NO
INDIV_SEQ1.0000.0730.0690.3500.0000.1210.069
JUDGE_DAY0.0731.0000.9980.0000.0000.0000.998
INSP_DY0.0690.9981.0000.0000.0000.0001.000
REQ_DIV0.3500.0000.0001.0000.0360.0380.000
TRGT_PERS_DIV0.0000.0000.0000.0361.0000.0000.000
REG_ENO0.1210.0000.0000.0380.0001.0000.000
PTTN_NO0.0690.9981.0000.0000.0000.0001.000
2023-12-12T18:14:11.558769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
TRGT_PERS_DIVREQ_DIV
TRGT_PERS_DIV1.0000.024
REQ_DIV0.0241.000
2023-12-12T18:14:11.648385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
INDIV_SEQJUDGE_DAYINSP_DYREG_ENOPTTN_NOREQ_DIVTRGT_PERS_DIV
INDIV_SEQ1.0000.0180.0200.0500.0200.2680.000
JUDGE_DAY0.0181.0000.9980.0340.9980.0000.000
INSP_DY0.0200.9981.0000.0321.0000.0000.000
REG_ENO0.0500.0340.0321.0000.0320.0660.000
PTTN_NO0.0200.9981.0000.0321.0000.0000.000
REQ_DIV0.2680.0000.0000.0660.0001.0000.024
TRGT_PERS_DIV0.0000.0000.0000.0000.0000.0241.000

Missing values

2023-12-12T18:14:07.142474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:14:07.728675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

GUARNT_NOINDIV_SEQJUDGE_DAYINSP_DYREQ_DIVTRGT_PERS_DIVREG_ENOREG_TSPTTN_NO
0RTOA2019000215220201026202010262120002020/10/26 11:31:3020206201020
1RTAD2018000231220201023202010232116562020/10/23 17:46:1420206201019
2RTOA2020000221120201023202010231120002020/10/23 17:03:0320206201018
3RTQB2020000051220201026202010232118172020/10/23 16:32:1720206201017
4RTBA2012000239120201023202010231117022020/10/23 13:54:3120206201016
5RTOB2016000053420201026202010232119652020/10/23 13:53:5120206201015
6RTAC2019000464120201026202010231117882020/10/23 13:52:0320206201014
7RTAB2019000564320201026202010232116892020/10/23 12:25:0320206201013
8RTBA2020000578120201026202010231117202020/10/23 11:05:2520206201012
9RTAC2016000763120201026202010231117532020/10/23 09:53:3920206201011
GUARNT_NOINDIV_SEQJUDGE_DAYINSP_DYREQ_DIVTRGT_PERS_DIVREG_ENOREG_TSPTTN_NO
990RTAD2018000479120191224201912231116502019/12/23 18:12:4420196201318
991RTLB2015000021220191224201912232114762019/12/23 17:38:1320196201317
992RTHB2017000329120191224201912231117732019/12/23 17:12:5620196201316
993RTMA2017000180320191224201912232119212019/12/23 17:05:3220196201315
994RTBA2011000032120191224201912231118592019/12/23 16:23:5520196201314
995RQAD2017000324120191224201912231116862019/12/23 16:10:2120196201313
996RTAB20160000821220191224201912232115322019/12/23 16:01:5120196201312
997RTQA2019000346320191223201912232118462019/12/23 15:12:1920196201311
998RTAB2015000611120191224201912231115132019/12/23 10:37:5320196201310
999RTAC2016001047320191223201912202119322019/12/20 19:59:3220196201309