我正在开发一个MySQL数据库系统，该系统将允许我的公司在目前所用时间的一小部分时间内处理我们的旅程时间调查数据。我有以下MySQL命令进行计算：
select anpr_1_unique.date as "Date",
anpr_1_unique.NETBIOSNAME as "ID for site A",
anpr_1_unique.time as "Timestamp at site A",
anpr_3_unique.NETBIOSNAME as "ID for site B",
anpr_3_unique.time as "Timestamp at site B",
anpr_1_unique.plate as "Plate",
if (timediff(anpr_3_unique.time,anpr_1_unique.time) like "%-%", null,timediff(anpr_3_unique.time,anpr_1_unique.time)) as "Journey time in direction 1",
if (timediff(anpr_1_unique.time,anpr_3_unique.time) like "%-%", null,timediff(anpr_1_unique.time,anpr_3_unique.time)) as "Journey time in direction 1",
if (timediff(anpr_1_unique.time,anpr_3_unique.time) like "%-%", "A->B","B->A") as "Direction of travel"
from anpr_1_unique inner join anpr_3_unique on anpr_1_unique.plate=anpr_3_unique.plate
where anpr_1_unique.date = "2013-02-26" and anpr_3_unique.date="2013-02-26" and anpr_1_unique.time like "%06:%%:%%%" order by anpr_1_unique.time
以下列格式生成表格：
+------------+---------------+---------------------+---------------+---------------------+---------+-----------------------------+-----------------------------+---------------------+
| Date | ID for site A | Timestamp at site A | ID for site B | Timestamp at site B | Plate | Journey time in direction 1 | Journey time in direction 1 | Direction of travel |
+------------+---------------+---------------------+---------------+---------------------+---------+-----------------------------+-----------------------------+---------------------+
| 2013-02-26 | Farnham N 1 | 06:50:52 | Farnham S | 06:54:32 | LS60JAO | 00:03:40 | NULL | A->B |
| 2013-02-26 | Farnham N 1 | 06:51:33 | Farnham S | 06:53:32 | FH53WGW | 00:01:59 | NULL | A->B |
| 2013-02-26 | Farnham N 1 | 06:51:51 | Farnham S | 06:54:23 | V987USD | 00:02:32 | NULL | A->B |
| 2013-02-26 | Farnham N 1 | 06:52:17 | Farnham S | 06:54:28 | BK61RNY | 00:02:11 | NULL | A->B |
| 2013-02-26 | Farnham N 1 | 06:52:30 | Farnham S | 06:54:35 | WU59SXP | 00:02:05 | NULL | A->B |
| 2013-02-26 | Farnham N 1 | 06:53:02 | Farnham S | 12:23:33 | NA08UKV | 05:30:31 | NULL | A->B |
| 2013-02-26 | Farnham N 1 | 06:53:44 | Farnham S | 06:56:09 | KC04CTF | 00:02:25 | NULL | A->B |
| 2013-02-26 | Farnham N 1 | 06:53:46 | Farnham S | 06:56:11 | LL58YAJ | 00:02:25 | NULL | A->B |
| 2013-02-26 | Farnham N 1 | 06:54:41 | Farnham S | 06:57:44 | AP57CWE | 00:03:03 | NULL | A->B |
| 2013-02-26 | Farnham N 1 | 06:54:46 | Farnham S | 14:55:29 | EU55LRF | 08:00:43 | NULL | A->B |
| 2013-02-26 | Farnham N 1 | 06:55:13 | Farnham S | 06:58:08 | AJ60KVK | 00:02:55 | NULL | A->B |
| 2013-02-26 | Farnham N 1 | 06:55:19 | Farnham S | 06:58:44 | T96ALO | 00:03:25 | NULL | A->B |
| 2013-02-26 | Farnham N 1 | 06:55:57 | Farnham S | 06:58:46 | F604WNV | 00:02:49 | NULL | A->B |
| 2013-02-26 | Farnham N 1 | 06:56:22 | Farnham S | 06:58:50 | S905AAP | 00:02:28 | NULL | A->B |
| 2013-02-26 | Farnham N 1 | 06:56:52 | Farnham S | 06:59:35 | LO08SKV | 00:02:43 | NULL | A->B |
| 2013-02-26 | Farnham N 1 | 06:56:55 | Farnham S | 06:59:37 | KT53TNK | 00:02:42 | NULL | A->B |
| 2013-02-26 | Farnham N 1 | 06:57:45 | Farnham S | 07:00:33 | LC60EOR | 00:02:48 | NULL | A->B |
| 2013-02-26 | Farnham N 1 | 06:58:44 | Farnham S | 07:06:15 | S278VNA | 00:07:31 | NULL | A->B |
| 2013-02-26 | Farnham N 1 | 06:58:49 | Farnham S | 07:00:46 | X906GVT | 00:01:57 | NULL | A->B |
| 2013-02-26 | Farnham N 1 | 06:59:15 | Farnham S | 07:01:35 | YS11AWP | 00:02:20 | NULL | A->B |
| 2013-02-26 | Farnham N 1 | 06:59:44 | Farnham S | 07:01:46 | LP12NWA | 00:02:02 | NULL | A->B |
| 2013-02-26 | Farnham N 1 | 06:59:59 | Farnham S | 07:02:29 | RJ11BUA | 00:02:30 | NULL | A->B |
+------------+---------------+---------------------+---------------+---------------------+---------+-----------------------------+-----------------------------+---------------------+
22 rows in set (0.09 sec)
这只是一个10分钟的结果样本。我对此感到满意，结果如何，但我希望能够将这些结果分组为5,10或15分钟的聚合时间
我在本网站和其他网站上寻找其他可能的解决方案，最值得注意的是建议使用GROUP BY UNIX_TIMESTAMP(<time_stamp>) DIV <time>来生成聚合。我试过这个，但它不适用于这个查询。
如何有效汇总这些数据？可以用我生成的查询来完成，还是需要重写？
我设法通过以下SQL代码大致完成了我想要的计算：
select
anpr_1_unique.date as "Date",
str_to_date(concat(date_format(anpr_1_unique.time, '%H'),':',(floor(date_format(anpr_1_unique.time, '%i')/30)*30), ':00'), '%H:%i:%s') as starttime,
anpr_1_unique.NETBIOSNAME as "ID for site A",
anpr_3_unique.NETBIOSNAME as "ID for site B",
if (avg(timediff(anpr_3_unique.time,anpr_1_unique.time) like "%-%"), null,time(avg(timediff(anpr_3_unique.time,anpr_1_unique.time)))) as "Journey time in direction A->B"
from anpr_1_unique
inner join anpr_3_unique on anpr_1_unique.plate=anpr_3_unique.plate
where anpr_1_unique.date = "2013-02-26" and anpr_3_unique.date="2013-02-26"
and timediff(anpr_3_unique.time,anpr_1_unique.time) between "00:00:00" and "00:30:00"
GROUP BY starttime;
show warnings;
生成如下数据：
+------------+-----------+---------------+---------------+---------------------------------+
| Date | starttime | ID for site A | ID for site B | Journey time in direction A->B |
+------------+-----------+---------------+---------------+---------------------------------+
| 2013-02-26 | 06:30:00 | Farnham N 1 | Farnham S | NULL |
| 2013-02-26 | 07:00:00 | Farnham N 1 | Farnham S | 00:03:46.3276 |
| 2013-02-26 | 07:30:00 | Farnham N 1 | Farnham S | 00:04:51.5588 |
| 2013-02-26 | 08:00:00 | Farnham N 1 | Farnham S | 00:11:10.8462 |
| 2013-02-26 | 08:30:00 | Farnham N 1 | Farnham S | 00:11:36.6410 |
| 2013-02-26 | 09:00:00 | Farnham N 1 | Farnham S | NULL |
| 2013-02-26 | 09:30:00 | Farnham N 1 | Farnham S | 00:05:39.0714 |
| 2013-02-26 | 10:00:00 | Farnham N 1 | Farnham S | NULL |
| 2013-02-26 | 10:30:00 | Farnham N 1 | Farnham S | NULL |
| 2013-02-26 | 11:00:00 | Farnham N 1 | Farnham S | NULL |
| 2013-02-26 | 11:30:00 | Farnham N 1 | Farnham S | NULL |
| 2013-02-26 | 12:00:00 | Farnham N 1 | Farnham S | NULL |
| 2013-02-26 | 12:30:00 | Farnham N 1 | Farnham S | NULL |
| 2013-02-26 | 13:00:00 | Farnham N 1 | Farnham S | 00:05:17.1250 |
| 2013-02-26 | 13:30:00 | Farnham N 1 | Farnham S | NULL |
| 2013-02-26 | 14:00:00 | Farnham N 1 | Farnham S | NULL |
| 2013-02-26 | 14:30:00 | Farnham N 1 | Farnham S | 00:05:06.8864 |
| 2013-02-26 | 15:00:00 | Farnham N 1 | Farnham S | 00:09:07.2308 |
| 2013-02-26 | 15:30:00 | Farnham N 1 | Farnham S | 00:13:02.2558 |
| 2013-02-26 | 16:00:00 | Farnham N 1 | Farnham S | NULL |
| 2013-02-26 | 16:30:00 | Farnham N 1 | Farnham S | 00:26:45.7143 |
| 2013-02-26 | 17:30:00 | Farnham N 1 | Farnham S | 00:28:04.0000 |
| 2013-02-26 | 18:00:00 | Farnham N 1 | Farnham S | 00:22:56.0667 |
| 2013-02-26 | 18:30:00 | Farnham N 1 | Farnham S | NULL |
+------------+-----------+---------------+---------------+---------------------------------+
但是，如图所示，这会产生许多NULL结果，其中应该有数据。警告显示相同数量的“| Warning | 1292 | Truncated incorrect time value: '(number)' |”消息。在计算时我遇到了类似的问题，我使用timediff()命令而不是手动计算解决了这个问题。我不知道我现在可以做些什么来“去除”这些结果。
关于我现在可以做什么的任何建议，现在？
答案 0 :(得分：1)
你需要围绕你的分区结果。用地板，天花板或圆形
进行此操作 GROUP BY floor(UNIX_TIMESTAMP(<time_stamp>)/(seconds*minutes))
GROUP BY floor( UNIX_TIMESTAMP(<time_stamp>)/(60*15) ) - group into 15 minute intervals
特别;
GROUP BY floor( UNIX_TIMESTAMP(CONCAT(anpr_1_unique.date, ' ', npr_1_unique.time))/(60*15) )