我正在开发一个 MySQL 数据库系统，该系统将使我的公司能够在目前所需时间的一小部分内处理我们的旅程时间调查数据。我有以下执行计算的 MySQL 命令：
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
生成以下格式的表格：
+------------+---------------+---------------------+---------------+---------------------+---------+-----------------------------+-----------------------------+---------------------+
| 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 10 分钟的结果样本。我对此感到满意，以及结果如何，但是我希望能够将这些结果分组为 5,10 或 15 分钟的聚合期
我在这个站点和其他站点上寻找其他潜在的解决方案，最值得注意的是建议使用GROUP BY UNIX_TIMESTAMP(<time_stamp>) DIV <time>来生成聚合。我已经尝试过了，但它不适用于此查询。
我该怎么做才能有效地汇总这些数据？可以用我生成的查询来完成，还是需要重写？
更新
我设法让计算大致完成了我想要使用以下 SQL 代码：
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;
产生这样的数据：
+------------+-----------+---------------+---------------+---------------------------------+
| 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，这会在应该有数据的地方生成许多 NULL 结果。警告显示相同数量的“ | Warning | 1292 | Truncated incorrect time value: '(number)' |”消息。我在进行计算时遇到了类似的问题，我通过使用timediff()命令而不是手动计算解决了这个问题。我不确定我现在可以用它做什么来“取消”这些结果。
关于我现在可以在这里做什么的任何建议？