|
1
|
|
|
import time |
|
2
|
|
|
from datetime import datetime, timedelta, timezone |
|
3
|
|
|
|
|
4
|
|
|
import mysql.connector |
|
5
|
|
|
|
|
6
|
|
|
import config |
|
7
|
|
|
|
|
8
|
|
|
|
|
9
|
|
|
######################################################################################################################## |
|
10
|
|
|
# This procedure will find and tag the bad energy values. |
|
11
|
|
|
# |
|
12
|
|
|
# Step 1: get the time slot to clean. |
|
13
|
|
|
# Step 2: check bad case class 1 with high limits and low limits. |
|
14
|
|
|
# Step 3: check bad case class 2 which is in concave shape model. |
|
15
|
|
|
# Step 4: tag the is_bad property of energy values. |
|
16
|
|
|
######################################################################################################################## |
|
17
|
|
|
|
|
18
|
|
|
def process(logger): |
|
19
|
|
|
|
|
20
|
|
|
while True: |
|
21
|
|
|
# the outermost loop to reconnect server if there is a connection error |
|
22
|
|
|
cnx_historical = None |
|
23
|
|
|
cursor_historical = None |
|
24
|
|
|
try: |
|
25
|
|
|
cnx_historical = mysql.connector.connect(**config.myems_historical_db) |
|
26
|
|
|
cursor_historical = cnx_historical.cursor() |
|
27
|
|
|
except Exception as e: |
|
28
|
|
|
logger.error("Error at the begin of clean_energy_value.process " + str(e)) |
|
29
|
|
|
if cursor_historical: |
|
30
|
|
|
cursor_historical.close() |
|
31
|
|
|
if cnx_historical: |
|
32
|
|
|
cnx_historical.close() |
|
33
|
|
|
time.sleep(60) |
|
34
|
|
|
continue |
|
35
|
|
|
|
|
36
|
|
|
# Note: |
|
37
|
|
|
# the default value of unchecked values' is_bad property is NULL |
|
38
|
|
|
# if a value is checked and the result is bad then is_bad would be set to 1 |
|
39
|
|
|
# else if a value is checked and the result is good then is_bad would be set to 0 |
|
40
|
|
|
|
|
41
|
|
|
################################################################################################################ |
|
42
|
|
|
# Step 1: get the time slot to clean. |
|
43
|
|
|
################################################################################################################ |
|
44
|
|
|
|
|
45
|
|
|
min_datetime = None |
|
46
|
|
|
max_datetime = None |
|
47
|
|
|
try: |
|
48
|
|
|
query = (" SELECT MAX(utc_date_time) " |
|
49
|
|
|
" FROM tbl_energy_value " |
|
50
|
|
|
" WHERE is_bad IS NOT NULL ") |
|
51
|
|
|
cursor_historical.execute(query, ()) |
|
52
|
|
|
row_datetime = cursor_historical.fetchone() |
|
53
|
|
|
if row_datetime is not None and len(row_datetime) == 1 and isinstance(row_datetime[0], datetime): |
|
54
|
|
|
# NOTE: To avoid omission mistakes, we start one hour early |
|
55
|
|
|
min_datetime = row_datetime[0] - timedelta(hours=1) |
|
56
|
|
|
else: |
|
57
|
|
|
# all is_bad properties are null |
|
58
|
|
|
min_datetime = datetime.strptime(config.start_datetime_utc, |
|
59
|
|
|
'%Y-%m-%d %H:%M:%S').replace(tzinfo=timezone.utc) |
|
60
|
|
|
|
|
61
|
|
|
query = (" SELECT MAX(utc_date_time) " |
|
62
|
|
|
" FROM tbl_energy_value " |
|
63
|
|
|
" WHERE is_bad IS NULL ") |
|
64
|
|
|
cursor_historical.execute(query, ()) |
|
65
|
|
|
row_datetime = cursor_historical.fetchone() |
|
66
|
|
|
if row_datetime is not None and len(row_datetime) == 1 and isinstance(row_datetime[0], datetime): |
|
67
|
|
|
max_datetime = row_datetime[0] |
|
68
|
|
|
|
|
69
|
|
|
except Exception as e: |
|
70
|
|
|
print("Error in Step 1 of clean_energy_value.process " + str(e)) |
|
71
|
|
|
logger.error("Error in Step 1 of clean_energy_value.process " + str(e)) |
|
72
|
|
|
if cursor_historical: |
|
73
|
|
|
cursor_historical.close() |
|
74
|
|
|
if cnx_historical: |
|
75
|
|
|
cnx_historical.close() |
|
76
|
|
|
time.sleep(60) |
|
77
|
|
|
continue |
|
78
|
|
|
|
|
79
|
|
|
if min_datetime is None or max_datetime is None: |
|
80
|
|
|
print("min_datetime or max_datetime is None") |
|
81
|
|
|
if cursor_historical: |
|
82
|
|
|
cursor_historical.close() |
|
83
|
|
|
if cnx_historical: |
|
84
|
|
|
cnx_historical.close() |
|
85
|
|
|
time.sleep(60) |
|
86
|
|
|
continue |
|
87
|
|
|
else: |
|
88
|
|
|
print("min_datetime: " + min_datetime.isoformat()[0:19]) |
|
89
|
|
|
print("max_datetime: " + max_datetime.isoformat()[0:19]) |
|
90
|
|
|
|
|
91
|
|
|
################################################################################################################ |
|
92
|
|
|
# Step 2: check bad case class 1 with high limits and low limits. |
|
93
|
|
|
################################################################################################################ |
|
94
|
|
|
|
|
95
|
|
|
################################################################################################################ |
|
96
|
|
|
# bad case 1.1 |
|
97
|
|
|
# id point_id utc_date_time actual_value is_bad (expected) |
|
98
|
|
|
# 104814811 3333 2018-01-31 16:45:04 115603.0078125 good |
|
99
|
|
|
# 104814588 3333 2018-01-31 16:44:00 115603.0078125 good |
|
100
|
|
|
# 104815007 3333 2018-01-31 16:46:09 1.832278249396618e21 bad |
|
101
|
|
|
# 104815226 3333 2018-01-31 16:47:13 1.832278249396618e21 bad |
|
102
|
|
|
# 104815423 3333 2018-01-31 16:48:17 1.832278249396618e21 bad |
|
103
|
|
|
# 104815643 3333 2018-01-31 16:49:22 1.832278249396618e21 bad |
|
104
|
|
|
# 104815820 3333 2018-01-31 16:50:26 1.832278249396618e21 bad |
|
105
|
|
|
# 104816012 3333 2018-01-31 16:51:30 1.832278249396618e21 bad |
|
106
|
|
|
# 104816252 3333 2018-01-31 16:52:34 1.832278249396618e21 bad |
|
107
|
|
|
# 104816446 3333 2018-01-31 16:53:38 1.832278249396618e21 bad |
|
108
|
|
|
# 104816667 3333 2018-01-31 16:54:43 1.832278249396618e21 bad |
|
109
|
|
|
# 104816860 3333 2018-01-31 16:55:47 1.832278249396618e21 bad |
|
110
|
|
|
# 104817065 3333 2018-01-31 16:56:51 1.832278249396618e21 bad |
|
111
|
|
|
# 104817284 3333 2018-01-31 16:57:55 1.832278249396618e21 bad |
|
112
|
|
|
# 104817482 3333 2018-01-31 16:58:59 1.832278249396618e21 bad |
|
113
|
|
|
# 104817723 3333 2018-01-31 17:00:04 1.832278249396618e21 bad |
|
114
|
|
|
# 104817940 3333 2018-01-31 17:01:08 115749.0078125 good |
|
115
|
|
|
# 104818142 3333 2018-01-31 17:02:11 115749.0078125 good |
|
116
|
|
|
# 104818380 3333 2018-01-31 17:03:16 115749.0078125 good |
|
117
|
|
|
# 104818596 3333 2018-01-31 17:04:20 115749.0078125 good |
|
118
|
|
|
################################################################################################################ |
|
119
|
|
|
|
|
120
|
|
|
################################################################################################################ |
|
121
|
|
|
# bad case 1.2: |
|
122
|
|
|
# id point_id utc_date_time actual_value is_bad (expected) |
|
123
|
|
|
# 3333 2018-01-31 17:27:53 115823.0078125 good |
|
124
|
|
|
# 3333 2018-01-31 17:28:57 115823.0078125 good |
|
125
|
|
|
# 3333 2018-01-31 17:30:02 115823.0078125 good |
|
126
|
|
|
# 3333 2018-01-31 17:31:06 115823.0078125 good |
|
127
|
|
|
# 3333 2018-01-31 17:32:11 0 bad |
|
128
|
|
|
# 3333 2018-01-31 17:33:15 0 bad |
|
129
|
|
|
# 3333 2018-01-31 17:34:19 0 bad |
|
130
|
|
|
# 3333 2018-01-31 17:35:24 0 bad |
|
131
|
|
|
# 3333 2018-01-31 17:36:28 0 bad |
|
132
|
|
|
# 3333 2018-01-31 17:37:32 0 bad |
|
133
|
|
|
# 3333 2018-01-31 17:38:36 0 bad |
|
134
|
|
|
# 3333 2018-01-31 17:39:41 0 bad |
|
135
|
|
|
# 3333 2018-01-31 17:40:44 0 bad |
|
136
|
|
|
# 3333 2018-01-31 17:41:49 0 bad |
|
137
|
|
|
# 3333 2018-01-31 17:43:57 0 bad |
|
138
|
|
|
# 3333 2018-01-31 17:42:53 0 bad |
|
139
|
|
|
# 3333 2018-01-31 17:45:01 0 bad |
|
140
|
|
|
# 3333 2018-01-31 17:46:06 0 bad |
|
141
|
|
|
# 3333 2018-01-31 17:47:10 0 bad |
|
142
|
|
|
# 3333 2018-01-31 17:48:14 115969.0078125 good |
|
143
|
|
|
# 3333 2018-01-31 17:49:18 115969.0078125 good |
|
144
|
|
|
# 3333 2018-01-31 17:50:22 115969.0078125 good |
|
145
|
|
|
################################################################################################################ |
|
146
|
|
|
|
|
147
|
|
|
################################################################################################################ |
|
148
|
|
|
# bad case 1.3: |
|
149
|
|
|
# id point_id utc_date_time actual_value is_bad (expected) |
|
150
|
|
|
# 3333 2018-02-04 07:00:38 139968 good |
|
151
|
|
|
# 3333 2018-02-04 07:01:42 139968 good |
|
152
|
|
|
# 3333 2018-02-04 07:03:54 -7.068193740872921e-3 bad |
|
153
|
|
|
# 3333 2018-02-04 07:04:58 -7.068193740872921e-3 bad |
|
154
|
|
|
# 3333 2018-02-04 07:06:03 -7.068193740872921e-3 bad |
|
155
|
|
|
# 3333 2018-02-04 07:07:06 -7.068193740872921e-3 bad |
|
156
|
|
|
# 3333 2018-02-04 07:08:10 -7.068193740872921e-3 bad |
|
157
|
|
|
# 3333 2018-02-04 07:09:13 -7.068193740872921e-3 bad |
|
158
|
|
|
# 3333 2018-02-04 07:10:17 -7.068193740872921e-3 bad |
|
159
|
|
|
# 3333 2018-02-04 07:11:21 -7.068193740872921e-3 bad |
|
160
|
|
|
# 3333 2018-02-04 07:12:25 -7.068193740872921e-3 bad |
|
161
|
|
|
# 3333 2018-02-04 07:13:29 -7.068193740872921e-3 bad |
|
162
|
|
|
# 3333 2018-02-04 07:14:33 -7.068193740872921e-3 bad |
|
163
|
|
|
# 3333 2018-02-04 07:15:37 -7.068193740872921e-3 bad |
|
164
|
|
|
# 3333 2018-02-04 07:16:41 -7.068193740872921e-3 bad |
|
165
|
|
|
# 3333 2018-02-04 07:17:45 140114 good |
|
166
|
|
|
# 3333 2018-02-04 07:18:49 140114 good |
|
167
|
|
|
# 3333 2018-02-04 07:19:53 140114 good |
|
168
|
|
|
################################################################################################################ |
|
169
|
|
|
|
|
170
|
|
|
################################################################################################################ |
|
171
|
|
|
# bad case 1.4: |
|
172
|
|
|
# id point_id utc_date_time actual_value is_bad (expected) |
|
173
|
|
|
# 3333 2018-02-08 01:16:38 165746.015625 good |
|
174
|
|
|
# 3333 2018-02-08 01:15:34 165746.015625 good |
|
175
|
|
|
# 3333 2018-02-08 01:14:30 165746.015625 good |
|
176
|
|
|
# 3333 2018-02-08 01:13:27 0.00303281145170331 bad |
|
177
|
|
|
# 3333 2018-02-08 01:12:22 0.00303281145170331 bad |
|
178
|
|
|
# 3333 2018-02-08 01:11:19 0.00303281145170331 bad |
|
179
|
|
|
# 3333 2018-02-08 01:10:15 0.00303281145170331 bad |
|
180
|
|
|
# 3333 2018-02-08 01:09:11 0.00303281145170331 bad |
|
181
|
|
|
# 3333 2018-02-08 01:08:06 0.00303281145170331 bad |
|
182
|
|
|
# 3333 2018-02-08 01:07:02 0.00303281145170331 bad |
|
183
|
|
|
# 3333 2018-02-08 01:05:58 0.00303281145170331 bad |
|
184
|
|
|
# 3333 2018-02-08 01:04:54 0.00303281145170331 bad |
|
185
|
|
|
# 3333 2018-02-08 01:03:50 0.00303281145170331 bad |
|
186
|
|
|
# 3333 2018-02-08 01:02:46 0.00303281145170331 bad |
|
187
|
|
|
# 3333 2018-02-08 01:01:42 0.00303281145170331 bad |
|
188
|
|
|
# 3333 2018-02-08 01:00:39 0.00303281145170331 bad |
|
189
|
|
|
# 3333 2018-02-08 00:59:34 0.00303281145170331 bad |
|
190
|
|
|
# 3333 2018-02-08 00:58:31 0.00303281145170331 bad |
|
191
|
|
|
# 3333 2018-02-08 00:57:27 165599.015625 good |
|
192
|
|
|
# 3333 2018-02-08 00:56:23 165599.015625 good |
|
193
|
|
|
# 3333 2018-02-08 00:55:20 165599.015625 good |
|
194
|
|
|
# 3333 2018-02-08 00:54:16 165599.015625 good |
|
195
|
|
|
################################################################################################################ |
|
196
|
|
|
print("Step 2: Processing bad case 1.x") |
|
197
|
|
|
cnx_system = None |
|
198
|
|
|
cursor_system = None |
|
199
|
|
|
point_dict = dict() |
|
200
|
|
|
try: |
|
201
|
|
|
cnx_system = mysql.connector.connect(**config.myems_system_db) |
|
202
|
|
|
cursor_system = cnx_system.cursor() |
|
203
|
|
|
|
|
204
|
|
|
query = (" SELECT id, high_limit, low_limit " |
|
205
|
|
|
" FROM tbl_points " |
|
206
|
|
|
" WHERE object_type='ENERGY_VALUE'") |
|
207
|
|
|
cursor_system.execute(query) |
|
208
|
|
|
rows_points = cursor_system.fetchall() |
|
209
|
|
|
|
|
210
|
|
|
if rows_points is not None and len(rows_points) > 0: |
|
211
|
|
|
for row in rows_points: |
|
212
|
|
|
point_dict[row[0]] = {"high_limit": row[1], |
|
213
|
|
|
"low_limit": row[2]} |
|
214
|
|
|
except Exception as e: |
|
215
|
|
|
logger.error("Error in step 2.1 of clean_energy_value.process " + str(e)) |
|
216
|
|
|
time.sleep(60) |
|
217
|
|
|
continue |
|
218
|
|
|
finally: |
|
219
|
|
|
if cursor_system: |
|
220
|
|
|
cursor_system.close() |
|
221
|
|
|
if cnx_system: |
|
222
|
|
|
cnx_system.close() |
|
223
|
|
|
|
|
224
|
|
|
try: |
|
225
|
|
|
query = (" SELECT id, point_id, actual_value " |
|
226
|
|
|
" FROM tbl_energy_value " |
|
227
|
|
|
" WHERE utc_date_time >= %s AND utc_date_time <= %s AND (is_bad = 0 OR is_bad IS NULL) ") |
|
228
|
|
|
cursor_historical.execute(query, (min_datetime, max_datetime,)) |
|
229
|
|
|
rows_energy_values = cursor_historical.fetchall() |
|
230
|
|
|
except Exception as e: |
|
231
|
|
|
logger.error("Error in step 2.2 of clean_energy_value.process " + str(e)) |
|
232
|
|
|
if cursor_historical: |
|
233
|
|
|
cursor_historical.close() |
|
234
|
|
|
if cnx_historical: |
|
235
|
|
|
cnx_historical.close() |
|
236
|
|
|
time.sleep(60) |
|
237
|
|
|
continue |
|
238
|
|
|
|
|
239
|
|
|
# initialize bad list |
|
240
|
|
|
bad_list = list() |
|
241
|
|
|
|
|
242
|
|
|
if rows_energy_values is not None and len(rows_energy_values) > 0: |
|
243
|
|
|
for row_energy_value in rows_energy_values: |
|
244
|
|
|
point_id = row_energy_value[1] |
|
245
|
|
|
actual_value = row_energy_value[2] |
|
246
|
|
|
point = point_dict.get(point_id, None) |
|
247
|
|
|
if point is None or actual_value > point['high_limit'] or actual_value < point['low_limit']: |
|
248
|
|
|
bad_list.append(row_energy_value[0]) |
|
249
|
|
|
|
|
250
|
|
|
print('bad list: ' + str(bad_list)) |
|
251
|
|
View Code Duplication |
while len(bad_list) > 0: |
|
|
|
|
|
|
252
|
|
|
update_100 = bad_list[:100] |
|
253
|
|
|
bad_list = bad_list[100:] |
|
254
|
|
|
try: |
|
255
|
|
|
update = (" UPDATE tbl_energy_value " |
|
256
|
|
|
" SET is_bad = 1 " |
|
257
|
|
|
" WHERE id IN (" + ', '.join(map(str, update_100)) + ")") |
|
258
|
|
|
cursor_historical.execute(update, ) |
|
259
|
|
|
cnx_historical.commit() |
|
260
|
|
|
except Exception as e: |
|
261
|
|
|
logger.error("Error in step 2.3 of clean_energy_value.process " + str(e)) |
|
262
|
|
|
if cursor_historical: |
|
263
|
|
|
cursor_historical.close() |
|
264
|
|
|
if cnx_historical: |
|
265
|
|
|
cnx_historical.close() |
|
266
|
|
|
time.sleep(60) |
|
267
|
|
|
continue |
|
268
|
|
|
|
|
269
|
|
|
################################################################################################################ |
|
270
|
|
|
# Step 3: check bad case class 2 which is in concave shape model. |
|
271
|
|
|
################################################################################################################ |
|
272
|
|
|
print("Step 3: Processing bad case 2.x") |
|
273
|
|
|
################################################################################################################ |
|
274
|
|
|
# bad case 2.1 |
|
275
|
|
|
# id point_id utc_date_time actual_value is_bad (expected) |
|
276
|
|
|
# 3333 2018-02-05 04:55:45 146129.015 good |
|
277
|
|
|
# 3333 2018-02-05 04:56:49 146129.015 good |
|
278
|
|
|
# 3333 2018-02-05 04:57:54 146129.015 good |
|
279
|
|
|
# 3333 2018-02-05 05:22:52 145693.015 bad |
|
280
|
|
|
# 3333 2018-02-05 05:25:01 146274 good |
|
281
|
|
|
# 3333 2018-02-05 05:26:03 146274 good |
|
282
|
|
|
# 3333 2018-02-05 05:27:05 146274 good |
|
283
|
|
|
# 3333 2018-02-05 05:29:30 146274 good |
|
284
|
|
|
################################################################################################################ |
|
285
|
|
|
|
|
286
|
|
|
################################################################################################################ |
|
287
|
|
|
# bad case 2.2 |
|
288
|
|
|
# id point_id utc_date_time actual_value is_bad (expected) |
|
289
|
|
|
# 3321 2018-05-15 15:09:54 33934040 good |
|
290
|
|
|
# 3321 2018-05-15 15:08:51 33934040 good |
|
291
|
|
|
# 3321 2018-05-15 15:07:47 33934040 good |
|
292
|
|
|
# 3321 2018-05-15 15:06:44 33934040 good |
|
293
|
|
|
# 3321 2018-05-15 15:05:40 33934040 good |
|
294
|
|
|
# 3321 2018-05-15 15:04:36 33934040 good |
|
295
|
|
|
# 3321 2018-05-15 09:09:00 33928880 bad |
|
296
|
|
|
# 3321 2018-05-15 09:05:23 33933568 good |
|
297
|
|
|
# 3321 2018-05-15 09:04:20 33933568 good |
|
298
|
|
|
# 3321 2018-05-15 09:03:16 33933568 good |
|
299
|
|
|
# 3321 2018-05-15 09:02:13 33933560 good |
|
300
|
|
|
# 3321 2018-05-15 09:01:09 33933560 good |
|
301
|
|
|
# 3321 2018-05-15 09:00:04 33933560 good |
|
302
|
|
|
################################################################################################################ |
|
303
|
|
|
|
|
304
|
|
|
################################################################################################################ |
|
305
|
|
|
# bad case 2.3 |
|
306
|
|
|
# id point_id utc_date_time actual_value is_bad (expected) |
|
307
|
|
|
# 554 2018-05-19 15:32:52 24001 good |
|
308
|
|
|
# 554 2018-05-19 15:30:45 24001 good |
|
309
|
|
|
# 554 2018-05-19 15:28:39 24001 good |
|
310
|
|
|
# 554 2018-05-19 15:26:32 24001 good |
|
311
|
|
|
# 554 2018-05-19 15:24:25 24001 good |
|
312
|
|
|
# 554 2018-05-19 15:22:18 24001 good |
|
313
|
|
|
# 554 2018-05-19 15:20:10 24001 good |
|
314
|
|
|
# 554 2018-05-19 15:18:04 24001 good |
|
315
|
|
|
# 554 2018-05-19 15:15:58 24001 good |
|
316
|
|
|
# 554 2018-05-19 15:13:51 24001 good |
|
317
|
|
|
# 554 2018-05-19 15:11:43 24001 good |
|
318
|
|
|
# 554 2018-05-19 15:09:37 24001 good |
|
319
|
|
|
# 554 2018-05-19 15:07:29 24000 good |
|
320
|
|
|
# 554 2018-05-19 15:05:22 23000 bad |
|
321
|
|
|
# 554 2018-05-19 15:03:14 23999 good |
|
322
|
|
|
# 554 2018-05-19 15:01:06 23999 good |
|
323
|
|
|
# 554 2018-05-19 14:58:59 23999 good |
|
324
|
|
|
# 554 2018-05-19 14:56:52 23998 good |
|
325
|
|
|
# 554 2018-05-19 14:54:45 23998 good |
|
326
|
|
|
# 554 2018-05-19 14:52:39 23998 good |
|
327
|
|
|
################################################################################################################ |
|
328
|
|
|
# todo bad case 2.3.1 |
|
329
|
|
|
# "id", "point_id", "utc_date_time", "actual_value", "is_bad" (actual) |
|
330
|
|
|
# 68504700, 2, "2021-01-09 03:40:12.0", 40454414.063, 0 |
|
331
|
|
|
# 68507243, 2, "2021-01-09 03:43:12.0", 40454476.563, 0 |
|
332
|
|
|
# 68510030, 2, "2021-01-09 03:47:17.0", 40428074.219, 0 ? |
|
333
|
|
|
# 68512573, 2, "2021-01-09 03:50:18.0", 40454621.094, 0 |
|
334
|
|
|
# 68515421, 2, "2021-01-09 03:54:23.0", 40454703.125, 0 |
|
335
|
|
|
# 68517964, 2, "2021-01-09 03:57:23.0", 40454761.719, 0 |
|
336
|
|
|
|
|
337
|
|
|
################################################################################################################ |
|
338
|
|
|
# bad case 2.4 |
|
339
|
|
|
# id point_id utc_date_time actual_value is_bad (expected) |
|
340
|
|
|
# 104373141 3336 2018-01-30 03:04:12 216463.015625 good |
|
341
|
|
|
# 104373337 3336 2018-01-30 03:05:15 216463.015625 good |
|
342
|
|
|
# 104373555 3336 2018-01-30 03:06:20 216463.015625 good |
|
343
|
|
|
# 104373750 3336 2018-01-30 03:07:25 192368.015625 bad |
|
344
|
|
|
# 104373957 3336 2018-01-30 03:08:29 192368.015625 bad |
|
345
|
|
|
# 104374175 3336 2018-01-30 03:09:33 192368.015625 bad |
|
346
|
|
|
# 104374382 3336 2018-01-30 03:10:38 192368.015625 bad |
|
347
|
|
|
# 104374604 3336 2018-01-30 03:11:42 192368.015625 bad |
|
348
|
|
|
# 104374792 3336 2018-01-30 03:12:47 192368.015625 bad |
|
349
|
|
|
# 104375010 3336 2018-01-30 03:13:51 192368.015625 bad |
|
350
|
|
|
# 104375200 3336 2018-01-30 03:14:55 192368.015625 bad |
|
351
|
|
|
# 104375418 3336 2018-01-30 03:16:00 192368.015625 bad |
|
352
|
|
|
# 104375617 3336 2018-01-30 03:17:04 192368.015625 bad |
|
353
|
|
|
# 104375837 3336 2018-01-30 03:18:08 192368.015625 bad |
|
354
|
|
|
# 104376023 3336 2018-01-30 03:19:12 192368.015625 bad |
|
355
|
|
|
# 104376216 3336 2018-01-30 03:20:16 192368.015625 bad |
|
356
|
|
|
# 104376435 3336 2018-01-30 03:21:21 192368.015625 bad |
|
357
|
|
|
# 104376634 3336 2018-01-30 03:22:25 192368.015625 bad |
|
358
|
|
|
# 104376853 3336 2018-01-30 03:23:30 192368.015625 bad |
|
359
|
|
|
# 104377071 3336 2018-01-30 03:24:34 192368.015625 bad |
|
360
|
|
|
# 104377274 3336 2018-01-30 03:25:38 192368.015625 bad |
|
361
|
|
|
# 104377501 3336 2018-01-30 03:26:42 216574.015625 good |
|
362
|
|
|
# 104377714 3336 2018-01-30 03:27:47 216574.015625 good |
|
363
|
|
|
################################################################################################################ |
|
364
|
|
|
|
|
365
|
|
|
################################################################################################################ |
|
366
|
|
|
# bad case 2.5 |
|
367
|
|
|
# id point_id utc_date_time actual_value is_bad (expected) |
|
368
|
|
|
# 104370839 3334 2018-01-30 02:52:23 844966.0625 good |
|
369
|
|
|
# 104371064 3334 2018-01-30 02:53:27 844966.0625 good |
|
370
|
|
|
# 104371261 3334 2018-01-30 02:54:32 844966.0625 good |
|
371
|
|
|
# 104371479 3334 2018-01-30 02:55:36 826142.0625 bad |
|
372
|
|
|
# 104371672 3334 2018-01-30 02:56:41 826142.0625 bad |
|
373
|
|
|
# 104371884 3334 2018-01-30 02:57:45 826142.0625 bad |
|
374
|
|
|
# 104372110 3334 2018-01-30 02:58:49 826142.0625 bad |
|
375
|
|
|
# 104372278 3334 2018-01-30 02:59:54 845019.0625 good |
|
376
|
|
|
# 104372512 3334 2018-01-30 03:00:58 845019.0625 good |
|
377
|
|
|
# 104372704 3334 2018-01-30 03:02:03 845019.0625 good |
|
378
|
|
|
################################################################################################################ |
|
379
|
|
|
|
|
380
|
|
|
################################################################################################################ |
|
381
|
|
|
# bad case 2.6 |
|
382
|
|
|
# 394084273 1001444 2019-08-22 03:39:44 38969028 good |
|
383
|
|
|
# 394083709 1001444 2019-08-22 03:38:43 38968876 good |
|
384
|
|
|
# 394083145 1001444 2019-08-22 03:37:43 28371884 bad |
|
385
|
|
|
# 394082019 1001444 2019-08-22 03:35:42 28371884 bad |
|
386
|
|
|
# 394081456 1001444 2019-08-22 03:34:42 28371884 bad |
|
387
|
|
|
# 394080892 1001444 2019-08-22 03:33:42 28371884 bad |
|
388
|
|
|
# 394079200 1001444 2019-08-22 03:30:38 28371884 bad |
|
389
|
|
|
# 394077511 1001444 2019-08-22 03:27:37 38968408 good |
|
390
|
|
|
# 394076947 1001444 2019-08-22 03:26:37 38968236 good |
|
391
|
|
|
# 394076384 1001444 2019-08-22 03:25:37 38968060 good |
|
392
|
|
|
################################################################################################################ |
|
393
|
|
|
|
|
394
|
|
|
################################################################################################################ |
|
395
|
|
|
# bad case 2.7 |
|
396
|
|
|
# id point_id utc_date_time actual_value is_bad (expected) |
|
397
|
|
|
# 17303260 11 2020-3-15 05:43:52 33600 good |
|
398
|
|
|
# 17303399 11 2020-3-15 05:44:58 33600 good |
|
399
|
|
|
# 17303538 11 2020-3-15 05:46:04 33600 good |
|
400
|
|
|
# 17303677 11 2020-3-15 05:47:10 33500 bad |
|
401
|
|
|
# 17303816 11 2020-3-15 05:48:15 33500 bad |
|
402
|
|
|
# 17303955 11 2020-3-15 05:49:21 33600 good |
|
403
|
|
|
# 17304094 11 2020-3-15 05:50:27 33600 good |
|
404
|
|
|
# 17304233 11 2020-3-15 05:51:33 33600 good |
|
405
|
|
|
################################################################################################################ |
|
406
|
|
|
|
|
407
|
|
|
try: |
|
408
|
|
|
query = (" SELECT point_id, id, utc_date_time, actual_value " |
|
409
|
|
|
" FROM tbl_energy_value " |
|
410
|
|
|
" WHERE utc_date_time >= %s AND utc_date_time <= %s AND (is_bad = 0 OR is_bad IS NULL) " |
|
411
|
|
|
" ORDER BY point_id, utc_date_time ") |
|
412
|
|
|
cursor_historical.execute(query, (min_datetime, max_datetime,)) |
|
413
|
|
|
rows_energy_values = cursor_historical.fetchall() |
|
414
|
|
|
except Exception as e: |
|
415
|
|
|
logger.error("Error in step 3.1 of clean_energy_value.process " + str(e)) |
|
416
|
|
|
if cursor_historical: |
|
417
|
|
|
cursor_historical.close() |
|
418
|
|
|
if cnx_historical: |
|
419
|
|
|
cnx_historical.close() |
|
420
|
|
|
time.sleep(60) |
|
421
|
|
|
continue |
|
422
|
|
|
|
|
423
|
|
|
point_value_dict = dict() |
|
424
|
|
|
current_point_value_list = list() |
|
425
|
|
|
current_point_id = 0 |
|
426
|
|
|
|
|
427
|
|
|
if rows_energy_values is not None and len(rows_energy_values) > 0: |
|
428
|
|
|
for row_energy_value in rows_energy_values: |
|
429
|
|
|
previous_point_id = current_point_id |
|
430
|
|
|
current_point_id = row_energy_value[0] |
|
431
|
|
|
if current_point_id not in point_value_dict.keys(): |
|
432
|
|
|
# new point id found |
|
433
|
|
|
# save previous point values |
|
434
|
|
|
if len(current_point_value_list) > 0: |
|
435
|
|
|
point_value_dict[previous_point_id] = current_point_value_list |
|
436
|
|
|
current_point_value_list = list() |
|
437
|
|
|
|
|
438
|
|
|
current_point_value_list.append({'id': row_energy_value[1], |
|
439
|
|
|
'actual_value': row_energy_value[3]}) |
|
440
|
|
|
else: |
|
441
|
|
|
current_point_value_list.append({'id': row_energy_value[1], |
|
442
|
|
|
'actual_value': row_energy_value[3]}) |
|
443
|
|
|
# end of for loop |
|
444
|
|
|
# save rest point values |
|
445
|
|
|
if len(current_point_value_list) > 0: |
|
446
|
|
|
point_value_dict[current_point_id] = current_point_value_list |
|
447
|
|
|
|
|
448
|
|
|
# reinitialize bad list |
|
449
|
|
|
bad_list = list() |
|
450
|
|
|
|
|
451
|
|
|
for point_id, point_value_list in point_value_dict.items(): |
|
452
|
|
|
if len(point_value_list) <= 1: |
|
453
|
|
|
continue |
|
454
|
|
|
elif len(point_value_list) == 2: |
|
455
|
|
|
if point_value_list[1]['actual_value'] < point_value_list[0]['actual_value']: |
|
456
|
|
|
bad_list.append(point_value_list[1]['id']) |
|
457
|
|
|
continue |
|
458
|
|
|
else: |
|
459
|
|
|
base_point_value = point_value_list[0]['actual_value'] |
|
460
|
|
|
concave_point_value_list = list() |
|
461
|
|
|
for i in range(len(point_value_list)): |
|
462
|
|
|
if point_value_list[i]['actual_value'] < base_point_value: |
|
463
|
|
|
# candidate concave value found |
|
464
|
|
|
concave_point_value_list.append(point_value_list[i]['id']) |
|
465
|
|
|
else: |
|
466
|
|
|
# normal value found |
|
467
|
|
|
if len(concave_point_value_list) > 0: |
|
468
|
|
|
# save confirmed concave value(s) to bad value(s) |
|
469
|
|
|
bad_list.extend(concave_point_value_list) |
|
470
|
|
|
|
|
471
|
|
|
# prepare for next candidate concave value list |
|
472
|
|
|
base_point_value = point_value_list[i]['actual_value'] |
|
473
|
|
|
concave_point_value_list.clear() |
|
474
|
|
|
continue |
|
475
|
|
|
|
|
476
|
|
|
print('bad list: ' + str(bad_list)) |
|
477
|
|
View Code Duplication |
while len(bad_list) > 0: |
|
|
|
|
|
|
478
|
|
|
update_100 = bad_list[:100] |
|
479
|
|
|
bad_list = bad_list[100:] |
|
480
|
|
|
try: |
|
481
|
|
|
update = (" UPDATE tbl_energy_value " |
|
482
|
|
|
" SET is_bad = 1 " |
|
483
|
|
|
" WHERE id IN (" + ', '.join(map(str, update_100)) + ")") |
|
484
|
|
|
cursor_historical.execute(update, ) |
|
485
|
|
|
cnx_historical.commit() |
|
486
|
|
|
except Exception as e: |
|
487
|
|
|
logger.error("Error in step 3.2 of clean_energy_value.process " + str(e)) |
|
488
|
|
|
if cursor_historical: |
|
489
|
|
|
cursor_historical.close() |
|
490
|
|
|
if cnx_historical: |
|
491
|
|
|
cnx_historical.close() |
|
492
|
|
|
time.sleep(60) |
|
493
|
|
|
continue |
|
494
|
|
|
|
|
495
|
|
|
################################################################################################################ |
|
496
|
|
|
# TODO: bad case 2.8 |
|
497
|
|
|
# id point_id utc_date_time actual_value is_bad (expected) |
|
498
|
|
|
# 105752070 3333 2018-02-04 00:27:15 138144 good |
|
499
|
|
|
# 105752305 3333 2018-02-04 00:28:19 138144 good |
|
500
|
|
|
# 105752523 3333 2018-02-04 00:29:22 138144 good |
|
501
|
|
|
# 105752704 3333 2018-02-04 00:30:26 138144 good |
|
502
|
|
|
# 105752924 3333 2018-02-04 00:31:30 138144 good |
|
503
|
|
|
# 105753138 3333 2018-02-04 00:32:34 138144 good |
|
504
|
|
|
# 105753351 3333 2018-02-04 00:33:38 138144 good |
|
505
|
|
|
# 105753577 3333 2018-02-04 00:34:42 52776558592 bad? |
|
506
|
|
|
# 105753794 3333 2018-02-04 00:35:46 52776558592 bad? |
|
507
|
|
|
# 105753999 3333 2018-02-04 00:36:50 52776558592 bad? |
|
508
|
|
|
# 105754231 3333 2018-02-04 00:37:54 52776558592 bad? |
|
509
|
|
|
# 105754443 3333 2018-02-04 00:38:58 52776558592 bad? |
|
510
|
|
|
# 105754655 3333 2018-02-04 00:40:01 52776558592 bad? |
|
511
|
|
|
# 105754878 3333 2018-02-04 00:41:06 52776558592 bad? |
|
512
|
|
|
# 105755092 3333 2018-02-04 00:42:09 52776558592 bad? |
|
513
|
|
|
# 105755273 3333 2018-02-04 00:43:14 52776558592 bad? |
|
514
|
|
|
# 105755495 3333 2018-02-04 00:44:17 52776558592 bad? |
|
515
|
|
|
# 105755655 3333 2018-02-04 00:45:21 52776558592 bad? |
|
516
|
|
|
# 105755854 3333 2018-02-04 00:46:25 52776558592 bad? |
|
517
|
|
|
# 105756073 3333 2018-02-04 00:47:29 52776558592 bad? |
|
518
|
|
|
# 105756272 3333 2018-02-04 00:48:34 52776558592 bad? |
|
519
|
|
|
# 105756489 3333 2018-02-04 00:49:38 52776558592 bad? |
|
520
|
|
|
################################################################################################################ |
|
521
|
|
|
|
|
522
|
|
|
################################################################################################################ |
|
523
|
|
|
# TODO: bad case 2.10 |
|
524
|
|
|
# id point_id utc_date_time actual_value is_bad (expected) |
|
525
|
|
|
# 106363135 3336 2018-02-06 04:45:57 253079.015625 good |
|
526
|
|
|
# 106363776 3336 2018-02-06 04:49:09 253079.015625 good |
|
527
|
|
|
# 106364381 3336 2018-02-06 04:52:21 253079.015625 good |
|
528
|
|
|
# 106364603 3336 2018-02-06 04:53:25 253079.015625 good |
|
529
|
|
|
# 106365213 3336 2018-02-06 04:56:37 253079.015625 good |
|
530
|
|
|
# 106365634 3336 2018-02-06 04:58:45 253079.015625 good |
|
531
|
|
|
# 106366055 3336 2018-02-06 05:00:53 253079.015625 good |
|
532
|
|
|
# 106367097 3336 2018-02-06 05:06:12 259783.015625 bad? |
|
533
|
|
|
# 106367507 3336 2018-02-06 05:08:21 259783.015625 bad? |
|
534
|
|
|
# 106368318 3336 2018-02-06 05:12:37 259783.015625 bad? |
|
535
|
|
|
# 106368732 3336 2018-02-06 05:14:44 259783.015625 bad? |
|
536
|
|
|
# 106368952 3336 2018-02-06 05:15:48 259783.015625 bad? |
|
537
|
|
|
# 106369145 3336 2018-02-06 05:16:52 259783.015625 bad? |
|
538
|
|
|
# 106369353 3336 2018-02-06 05:17:56 259783.015625 bad? |
|
539
|
|
|
################################################################################################################ |
|
540
|
|
|
|
|
541
|
|
|
################################################################################################################ |
|
542
|
|
|
# TODO: bad case 2.11 |
|
543
|
|
|
# id point_id utc_date_time actual_value is_bad (expected) |
|
544
|
|
|
# 14784589 21 2020-03-05 07:22:22 17990 good |
|
545
|
|
|
# 14784450 21 2020-03-05 07:21:17 17990 good |
|
546
|
|
|
# 14784311 21 2020-03-05 07:20:10 17990 good |
|
547
|
|
|
# 14784172 21 2020-03-05 07:19:04 17990 good |
|
548
|
|
|
# 14784033 21 2020-03-05 07:17:58 18990 bad |
|
549
|
|
|
# 14783894 21 2020-03-05 07:16:52 17990 good |
|
550
|
|
|
# 14783755 21 2020-03-05 07:15:46 17990 good |
|
551
|
|
|
# 14783616 21 2020-03-05 07:14:40 17990 good |
|
552
|
|
|
# 14783477 21 2020-03-05 07:13:34 17990 good |
|
553
|
|
|
# 14783338 21 2020-03-05 07:12:28 17990 good |
|
554
|
|
|
# 14783199 21 2020-03-05 07:11:22 17990 good |
|
555
|
|
|
################################################################################################################ |
|
556
|
|
|
|
|
557
|
|
|
################################################################################################################ |
|
558
|
|
|
# TODO: bad case 2.12 |
|
559
|
|
|
# id point_id utc_date_time actual_value is_bad (expected) |
|
560
|
|
|
# 3337308 21 2020-01-07 09:02:18 7990 good |
|
561
|
|
|
# 3337174 21 2020-01-07 09:01:13 7990 good |
|
562
|
|
|
# 3337040 21 2020-01-07 09:00:08 7990 good |
|
563
|
|
|
# 3336906 21 2020-01-07 08:59:04 7990 good |
|
564
|
|
|
# 3336772 21 2020-01-07 08:57:59 7990 good |
|
565
|
|
|
# 3336638 21 2020-01-07 08:56:54 8990 bad |
|
566
|
|
|
# 3336504 21 2020-01-07 08:55:49 7990 good |
|
567
|
|
|
# 3336370 21 2020-01-07 08:54:44 7990 good |
|
568
|
|
|
# 3336236 21 2020-01-07 08:53:39 7990 good |
|
569
|
|
|
# 3336102 21 2020-01-07 08:52:34 7990 good |
|
570
|
|
|
# 3335968 21 2020-01-07 08:51:30 7990 good |
|
571
|
|
|
################################################################################################################ |
|
572
|
|
|
# Step 4: tag the is_bad property of energy values. |
|
573
|
|
|
################################################################################################################ |
|
574
|
|
|
try: |
|
575
|
|
|
update = (" UPDATE tbl_energy_value " |
|
576
|
|
|
" SET is_bad = 0 " |
|
577
|
|
|
" WHERE utc_date_time >= %s AND utc_date_time < %s AND is_bad IS NULL ") |
|
578
|
|
|
# NOTE: use '<' instead of '<=' in WHERE statement because there may be some new inserted values |
|
579
|
|
|
cursor_historical.execute(update, (min_datetime, max_datetime,)) |
|
580
|
|
|
cnx_historical.commit() |
|
581
|
|
|
except Exception as e: |
|
582
|
|
|
logger.error("Error in step 4 of clean_energy_value.process " + str(e)) |
|
583
|
|
|
time.sleep(60) |
|
584
|
|
|
continue |
|
585
|
|
|
finally: |
|
586
|
|
|
if cursor_historical: |
|
587
|
|
|
cursor_historical.close() |
|
588
|
|
|
if cnx_historical: |
|
589
|
|
|
cnx_historical.close() |
|
590
|
|
|
|
|
591
|
|
|
time.sleep(900) |
|
592
|
|
|
|
MyEMS /
myems
Loading history...