Overview

Dataset statistics

Number of variables12
Number of observations393
Missing cells393
Missing cells (%)8.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory37.4 KiB
Average record size in memory97.3 B

Variable types

Categorical9
DateTime1
Text1
Unsupported1

Dataset

Description선박검역시 조치명령을 한 검역 정보 (검역구분, 검역일시, 검역소, 선박명, 선박국적, 선박종류, 오염구분, 출발국가, 쥐의서식흔적발견, 위생해충발견, 위생검사결과 등 )
Author질병관리청
URLhttps://www.data.go.kr/data/3074712/fileData.do

Alerts

위생검사결과 is highly overall correlated with 위생해충발견 and 1 other fieldsHigh correlation
선박종류 is highly overall correlated with 위생해충발견 and 1 other fieldsHigh correlation
출발국가 is highly overall correlated with 오염_비오염 and 2 other fieldsHigh correlation
증명서유효기간경과 is highly overall correlated with 검역구분 and 6 other fieldsHigh correlation
오염_비오염 is highly overall correlated with 출발국가 and 2 other fieldsHigh correlation
검역소 is highly overall correlated with 위생해충발견 and 1 other fieldsHigh correlation
검역구분 is highly overall correlated with 위생해충발견 and 1 other fieldsHigh correlation
위생해충발견 is highly overall correlated with 검역구분 and 6 other fieldsHigh correlation
선박국적 is highly overall correlated with 위생해충발견 and 1 other fieldsHigh correlation
검역구분 is highly imbalanced (85.7%)Imbalance
위생해충발견 is highly imbalanced (85.7%)Imbalance
위생검사결과 is highly imbalanced (50.4%)Imbalance
쥐의서식흔적발견 has 393 (100.0%) missing valuesMissing
쥐의서식흔적발견 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 06:51:40.960192
Analysis finished2023-12-12 06:51:41.835850
Duration0.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

검역구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
승선
385 
전자
 
8

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
승선 385
98.0%
전자 8
 
2.0%

Length

2023-12-12T15:51:41.901101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:51:42.002226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
승선 385
98.0%
전자 8
 
2.0%

검역일시
Date

Distinct391
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
Minimum2022-01-01 12:00:00
Maximum2022-12-31 08:15:00
2023-12-12T15:51:42.117890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:51:42.303474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

검역소
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
국립울산검역소
102 
국립여수검역소
72 
국립부산검역소
72 
국립평택검역소
55 
국립군산검역소
40 
Other values (5)
52 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row국립여수검역소
2nd row국립동해검역소
3rd row국립여수검역소
4th row국립평택검역소
5th row국립군산검역소

Common Values

ValueCountFrequency (%)
국립울산검역소 102
26.0%
국립여수검역소 72
18.3%
국립부산검역소 72
18.3%
국립평택검역소 55
14.0%
국립군산검역소 40
 
10.2%
국립포항검역소 23
 
5.9%
국립마산검역소 15
 
3.8%
국립인천검역소 11
 
2.8%
국립목포검역소 2
 
0.5%
국립동해검역소 1
 
0.3%

Length

2023-12-12T15:51:42.446172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:51:42.592066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국립울산검역소 102
26.0%
국립여수검역소 72
18.3%
국립부산검역소 72
18.3%
국립평택검역소 55
14.0%
국립군산검역소 40
 
10.2%
국립포항검역소 23
 
5.9%
국립마산검역소 15
 
3.8%
국립인천검역소 11
 
2.8%
국립목포검역소 2
 
0.5%
국립동해검역소 1
 
0.3%

선박명
Text

Distinct378
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
2023-12-12T15:51:43.058469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length16
Mean length11.094148
Min length3

Characters and Unicode

Total characters4360
Distinct characters45
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique363 ?
Unique (%)92.4%

Sample

1st rowSTAR CARIOCA
2nd rowNO.3 WATER VIS
3rd rowAQUALIBERTY
4th rowLIAN HE
5th rowMORNING CHAMPION
ValueCountFrequency (%)
stolt 15
 
1.9%
sun 8
 
1.0%
dong 7
 
0.9%
fu 6
 
0.8%
hai 6
 
0.8%
yuan 6
 
0.8%
da 6
 
0.8%
ocean 6
 
0.8%
feng 5
 
0.6%
sea 5
 
0.6%
Other values (548) 710
91.0%
2023-12-12T15:51:43.671988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 431
 
9.9%
N 398
 
9.1%
387
 
8.9%
E 337
 
7.7%
I 305
 
7.0%
O 288
 
6.6%
R 259
 
5.9%
S 240
 
5.5%
T 165
 
3.8%
L 163
 
3.7%
Other values (35) 1387
31.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3816
87.5%
Space Separator 387
 
8.9%
Decimal Number 115
 
2.6%
Other Punctuation 27
 
0.6%
Other Letter 15
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 431
11.3%
N 398
 
10.4%
E 337
 
8.8%
I 305
 
8.0%
O 288
 
7.5%
R 259
 
6.8%
S 240
 
6.3%
T 165
 
4.3%
L 163
 
4.3%
G 158
 
4.1%
Other values (16) 1072
28.1%
Decimal Number
ValueCountFrequency (%)
1 24
20.9%
2 18
15.7%
6 14
12.2%
7 14
12.2%
3 13
11.3%
8 12
10.4%
5 8
 
7.0%
0 7
 
6.1%
9 5
 
4.3%
Other Letter
ValueCountFrequency (%)
제 4
26.7%
호 3
20.0%
양 2
13.3%
경 2
13.3%
오 1
 
6.7%
룡 1
 
6.7%
한 1
 
6.7%
성 1
 
6.7%
Space Separator
ValueCountFrequency (%)
387
100.0%
Other Punctuation
ValueCountFrequency (%)
. 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3816
87.5%
Common 529
 
12.1%
Hangul 15
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 431
11.3%
N 398
 
10.4%
E 337
 
8.8%
I 305
 
8.0%
O 288
 
7.5%
R 259
 
6.8%
S 240
 
6.3%
T 165
 
4.3%
L 163
 
4.3%
G 158
 
4.1%
Other values (16) 1072
28.1%
Common
ValueCountFrequency (%)
387
73.2%
. 27
 
5.1%
1 24
 
4.5%
2 18
 
3.4%
6 14
 
2.6%
7 14
 
2.6%
3 13
 
2.5%
8 12
 
2.3%
5 8
 
1.5%
0 7
 
1.3%
Hangul
ValueCountFrequency (%)
제 4
26.7%
호 3
20.0%
양 2
13.3%
경 2
13.3%
오 1
 
6.7%
룡 1
 
6.7%
한 1
 
6.7%
성 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4345
99.7%
Hangul 15
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 431
 
9.9%
N 398
 
9.2%
387
 
8.9%
E 337
 
7.8%
I 305
 
7.0%
O 288
 
6.6%
R 259
 
6.0%
S 240
 
5.5%
T 165
 
3.8%
L 163
 
3.8%
Other values (27) 1372
31.6%
Hangul
ValueCountFrequency (%)
제 4
26.7%
호 3
20.0%
양 2
13.3%
경 2
13.3%
오 1
 
6.7%
룡 1
 
6.7%
한 1
 
6.7%
성 1
 
6.7%

선박국적
Categorical

HIGH CORRELATION 

Distinct41
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
파나마
79 
한국
50 
마샬군도
41 
라이베리아
25 
싱가폴
23 
Other values (36)
175 

Length

Max length7
Median length6
Mean length3.2569975
Min length2

Unique

Unique9 ?
Unique (%)2.3%

Sample

1st row마샬군도
2nd row몽골
3rd row라이베리아
4th row벨리즈
5th row바하마

Common Values

ValueCountFrequency (%)
파나마 79
20.1%
한국 50
12.7%
마샬군도 41
10.4%
라이베리아 25
 
6.4%
싱가폴 23
 
5.9%
벨리즈 22
 
5.6%
바하마 16
 
4.1%
홍콩 15
 
3.8%
케이만 제도 12
 
3.1%
러시아연방 11
 
2.8%
Other values (31) 99
25.2%

Length

2023-12-12T15:51:43.835498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
파나마 79
19.4%
한국 50
12.3%
마샬군도 41
 
10.1%
라이베리아 25
 
6.1%
싱가폴 23
 
5.7%
벨리즈 22
 
5.4%
바하마 16
 
3.9%
홍콩 15
 
3.7%
제도 14
 
3.4%
케이만 12
 
2.9%
Other values (32) 110
27.0%

선박종류
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
일반화물선
81 
석유제품운반선
66 
산물선(벌크선)
60 
케미칼운반선
42 
원유운반선
33 
Other values (13)
111 

Length

Max length11
Median length8
Mean length6.0229008
Min length4

Unique

Unique6 ?
Unique (%)1.5%

Sample

1st row산물선(벌크선)
2nd row일반화물선
3rd row원유운반선
4th row일반화물선
5th row자동차운반선

Common Values

ValueCountFrequency (%)
일반화물선 81
20.6%
석유제품운반선 66
16.8%
산물선(벌크선) 60
15.3%
케미칼운반선 42
10.7%
원유운반선 33
8.4%
원양어선 29
 
7.4%
풀컨테이너선 26
 
6.6%
LPG운반선 14
 
3.6%
LNG운반선 14
 
3.6%
냉동냉장선 12
 
3.1%
Other values (8) 16
 
4.1%

Length

2023-12-12T15:51:43.961924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반화물선 81
20.6%
석유제품운반선 66
16.8%
산물선(벌크선 60
15.3%
케미칼운반선 42
10.7%
원유운반선 33
8.4%
원양어선 29
 
7.4%
풀컨테이너선 26
 
6.6%
lpg운반선 14
 
3.6%
lng운반선 14
 
3.6%
냉동냉장선 12
 
3.1%
Other values (8) 16
 
4.1%

오염_비오염
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
오염
235 
비오염
158 

Length

Max length3
Median length2
Mean length2.4020356
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row비오염
2nd row비오염
3rd row비오염
4th row오염
5th row오염

Common Values

ValueCountFrequency (%)
오염 235
59.8%
비오염 158
40.2%

Length

2023-12-12T15:51:44.112582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:51:44.216901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
오염 235
59.8%
비오염 158
40.2%

출발국가
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
일본
81 
중국
67 
미국
40 
대양주
28 
러시아연방
25 
Other values (30)
152 

Length

Max length7
Median length2
Mean length2.6793893
Min length2

Unique

Unique6 ?
Unique (%)1.5%

Sample

1st row파나마
2nd row한국
3rd row한국
4th row중국
5th row중국

Common Values

ValueCountFrequency (%)
일본 81
20.6%
중국 67
17.0%
미국 40
10.2%
대양주 28
 
7.1%
러시아연방 25
 
6.4%
싱가폴 19
 
4.8%
파나마 18
 
4.6%
한국 17
 
4.3%
호주 15
 
3.8%
대만 10
 
2.5%
Other values (25) 73
18.6%

Length

2023-12-12T15:51:44.368662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일본 81
20.5%
중국 67
17.0%
미국 40
10.1%
대양주 28
 
7.1%
러시아연방 25
 
6.3%
싱가폴 19
 
4.8%
파나마 18
 
4.6%
한국 17
 
4.3%
호주 15
 
3.8%
대만 10
 
2.5%
Other values (26) 75
19.0%

쥐의서식흔적발견
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing393
Missing (%)100.0%
Memory size3.6 KiB

위생해충발견
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
<NA>
385 
벌레잡이소독명령
 
8

Length

Max length8
Median length4
Mean length4.0814249
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 385
98.0%
벌레잡이소독명령 8
 
2.0%

Length

2023-12-12T15:51:44.522932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:51:44.627477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 385
98.0%
벌레잡이소독명령 8
 
2.0%

위생검사결과
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
<NA>
296 
벌레잡이소독명령
79 
살균소독명령
 
17
벌레잡이(살균)소독명령
 
1

Length

Max length12
Median length4
Mean length4.9109415
Min length4

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row벌레잡이소독명령

Common Values

ValueCountFrequency (%)
<NA> 296
75.3%
벌레잡이소독명령 79
 
20.1%
살균소독명령 17
 
4.3%
벌레잡이(살균)소독명령 1
 
0.3%

Length

2023-12-12T15:51:44.757569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:51:44.885762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 296
75.3%
벌레잡이소독명령 79
 
20.1%
살균소독명령 17
 
4.3%
벌레잡이(살균)소독명령 1
 
0.3%

증명서유효기간경과
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
선박위생면제증명서발급
303 
<NA>
90 

Length

Max length11
Median length11
Mean length9.3969466
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row선박위생면제증명서발급
2nd row선박위생면제증명서발급
3rd row선박위생면제증명서발급
4th row선박위생면제증명서발급
5th row<NA>

Common Values

ValueCountFrequency (%)
선박위생면제증명서발급 303
77.1%
<NA> 90
 
22.9%

Length

2023-12-12T15:51:45.033324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:51:45.150070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
선박위생면제증명서발급 303
77.1%
na 90
 
22.9%

Correlations

2023-12-12T15:51:45.223692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
검역구분검역소선박국적선박종류오염_비오염출발국가위생검사결과
검역구분1.0000.3820.0000.4140.1050.4430.000
검역소0.3821.0000.6380.6860.3280.6340.551
선박국적0.0000.6381.0000.7960.3030.7880.000
선박종류0.4140.6860.7961.0000.5070.6600.000
오염_비오염0.1050.3280.3030.5071.0000.8010.000
출발국가0.4430.6340.7880.6600.8011.0000.391
위생검사결과0.0000.5510.0000.0000.0000.3911.000
2023-12-12T15:51:45.335723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위생검사결과선박종류출발국가증명서유효기간경과오염_비오염검역소검역구분위생해충발견선박국적
위생검사결과1.0000.0000.1781.0000.0000.2780.0001.0000.000
선박종류0.0001.0000.2271.0000.3930.3400.3201.0000.324
출발국가0.1780.2271.0001.0000.6740.2640.3591.0000.253
증명서유효기간경과1.0001.0001.0001.0001.0001.0001.000NaN1.000
오염_비오염0.0000.3930.6741.0001.0000.2490.0671.0000.240
검역소0.2780.3400.2641.0000.2491.0000.2911.0000.265
검역구분0.0000.3200.3591.0000.0670.2911.0001.0000.000
위생해충발견1.0001.0001.000NaN1.0001.0001.0001.0001.000
선박국적0.0000.3240.2531.0000.2400.2650.0001.0001.000
2023-12-12T15:51:45.482691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
검역구분검역소선박국적선박종류오염_비오염출발국가위생해충발견위생검사결과증명서유효기간경과
검역구분1.0000.2910.0000.3200.0670.3591.0000.0001.000
검역소0.2911.0000.2650.3400.2490.2641.0000.2781.000
선박국적0.0000.2651.0000.3240.2400.2531.0000.0001.000
선박종류0.3200.3400.3241.0000.3930.2271.0000.0001.000
오염_비오염0.0670.2490.2400.3931.0000.6741.0000.0001.000
출발국가0.3590.2640.2530.2270.6741.0001.0000.1781.000
위생해충발견1.0001.0001.0001.0001.0001.0001.0001.0000.000
위생검사결과0.0000.2780.0000.0000.0000.1781.0001.0001.000
증명서유효기간경과1.0001.0001.0001.0001.0001.0000.0001.0001.000

Missing values

2023-12-12T15:51:41.603121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:51:41.749002image/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

검역구분검역일시검역소선박명선박국적선박종류오염_비오염출발국가쥐의서식흔적발견위생해충발견위생검사결과증명서유효기간경과
0승선2022-01-07 11:30국립여수검역소STAR CARIOCA마샬군도산물선(벌크선)비오염파나마<NA><NA><NA>선박위생면제증명서발급
1승선2022-01-08 10:40국립동해검역소NO.3 WATER VIS몽골일반화물선비오염한국<NA><NA><NA>선박위생면제증명서발급
2승선2022-01-13 11:00국립여수검역소AQUALIBERTY라이베리아원유운반선비오염한국<NA><NA><NA>선박위생면제증명서발급
3승선2022-01-14 11:00국립평택검역소LIAN HE벨리즈일반화물선오염중국<NA><NA><NA>선박위생면제증명서발급
4승선2022-01-16 01:10국립군산검역소MORNING CHAMPION바하마자동차운반선오염중국<NA><NA>벌레잡이소독명령<NA>
5승선2022-01-21 16:40국립포항검역소UNISON LEADER대만일반화물선오염대만<NA><NA>벌레잡이소독명령<NA>
6승선2022-01-22 15:00국립여수검역소ASIA VENTURE바하마LNG운반선오염일본<NA><NA><NA>선박위생면제증명서발급
7승선2022-02-10 17:10국립평택검역소NAVE AQUILA파나마석유제품운반선오염필리핀<NA><NA>벌레잡이소독명령<NA>
8승선2022-02-11 15:00국립울산검역소WOOJIN CHEMI마샬군도석유제품운반선비오염대양주<NA><NA><NA>선박위생면제증명서발급
9승선2022-03-04 09:15국립마산검역소FENG HAI 68중국원양어선오염중국<NA><NA><NA>선박위생면제증명서발급
검역구분검역일시검역소선박명선박국적선박종류오염_비오염출발국가쥐의서식흔적발견위생해충발견위생검사결과증명서유효기간경과
383승선2022-11-15 10:45국립평택검역소SERI BALHAF말레이지아LNG운반선비오염남아프리카<NA><NA><NA>선박위생면제증명서발급
384승선2022-11-19 17:30국립부산검역소VASILYEVSKIY OSTROV러시아연방원양어선오염대양주<NA><NA>벌레잡이(살균)소독명령<NA>
385승선2022-11-20 08:55국립여수검역소NEW ACTIVITY라이베리아원유운반선오염싱가폴<NA><NA><NA>선박위생면제증명서발급
386승선2022-11-21 11:10국립여수검역소KITA LNG몰타LNG운반선비오염싱가폴<NA><NA><NA>선박위생면제증명서발급
387승선2022-12-05 10:30국립부산검역소QINGDAO TRADER마샬군도풀컨테이너선오염일본<NA><NA><NA>선박위생면제증명서발급
388승선2022-12-15 19:10국립여수검역소TM HAI HA 818베트남석유제품운반선오염베트남<NA><NA>벌레잡이소독명령<NA>
389승선2022-12-26 15:05국립부산검역소TAIHO MARU파나마냉동냉장선오염일본<NA><NA>벌레잡이소독명령<NA>
390승선2022-12-27 18:50국립평택검역소MING DA시에라리온일반화물선오염일본<NA><NA><NA>선박위생면제증명서발급
391승선2022-12-28 11:20국립여수검역소GPOSAPPHIRE마샬군도산물선(벌크선)비오염한국<NA><NA><NA>선박위생면제증명서발급
392승선2022-12-30 22:40국립부산검역소PACIFIC BEIJING싱가폴풀컨테이너선오염러시아연방<NA><NA>벌레잡이소독명령<NA>