在python中有一个时间数据库如下： {'date': '2023-05-9 21:00:00', 'Orbit': '371'} {'date': '2023-05-9 21:00:01', 'Orbit': '371'} {'date': '2023-05-9 21:00:02', 'Orbit': '371'} {'date': '2023-05-9 21:00:03', 'Orbit': '371'} {'date': '2023-05-9 21:00:04', 'Orbit': '371'} {'date': '2023-05-9 21:00:05', 'Orbit': '371'} {'date': '2023-05-9 21:00:06', 'Orbit': '371'} {'date': '2023-05-9 21:00:07', 'Orbit': '371'} {'date': '2023-05-9 21:00:08', 'Orbit': '371'} {'date': '2023-05-10 08:16:43', 'Orbit': '378'} {'date': '2023-05-10 08:16:44', 'Orbit': '378'} {'date': '2023-05-10 08:16:45', 'Orbit': '378'} {'date': '2023-05-10 08:16:46', 'Orbit': '378'} {'date': '2023-05-10 08:16:47', 'Orbit': '378'} {'date': '2023-05-10 08:16:48', 'Orbit': '378'} {'date': '2023-05-10 08:16:49', 'Orbit': '379'} {'date': '2023-05-10 08:16:50', 'Orbit': '379'} {'date': '2023-05-10 08:16:51', 'Orbit': '379'} {'date': '2023-05-10 08:16:52', 'Orbit': '379'} {'date': '2023-05-10 08:16:53', 'Orbit': '379'} {'date': '2023-05-10 08:16:54', 'Orbit': '379'} {'date': '2023-05-10 08:16:55', 'Orbit': '379'} {'date': '2023-05-10 08:16:56', 'Orbit': '379'} {'date': '2023-05-10 08:16:57', 'Orbit': '379'} {'date': '2023-05-10 08:16:58', 'Orbit': '379'} 我现在需要按照以下格式 20230509180413 20230509234724 GZ 78 20230510065339 20230510110050 JM 80 将数据库里面的时间放在相应的时间段里面，该怎么做

首先，需要将时间字符串转换为datetime对象，并提取出日期和时间。然后，将日期和时间组合成一个整数，表示为YYYYMMDDHHMMSS的格式。接下来，我们可以按照指定的时间段将这些整数放入相应的时间段中，最后根据不同时间段的整数的数量计算出每个时间段的Orbit平均值。

下面是一个示例代码，可以实现上述功能：

from datetime import datetime

# 时间段的起始和结束时间
time_ranges = [
    ('20230509000000', '20230509230000'),
    ('20230510000000', '20230510120000')
]

# 初始化每个时间段的Orbit总和和计数器
orbit_sum = [0] * len(time_ranges)
count = [0] * len(time_ranges)

# 时间数据库
time_db = [
    {'date': '2023-05-9 21:00:00', 'Orbit': '371'},
    {'date': '2023-05-9 21:00:01', 'Orbit': '371'},
    {'date': '2023-05-9 21:00:02', 'Orbit': '371'},
    {'date': '2023-05-9 21:00:03', 'Orbit': '371'},
    {'date': '2023-05-9 21:00:04', 'Orbit': '371'},
    {'date': '2023-05-9 21:00:05', 'Orbit': '371'},
    {'date': '2023-05-9 21:00:06', 'Orbit': '371'},
    {'date': '2023-05-9 21:00:07', 'Orbit': '371'},
    {'date': '2023-05-9 21:00:08', 'Orbit': '371'},
    {'date': '2023-05-10 08:16:43', 'Orbit': '378'},
    {'date': '2023-05-10 08:16:44', 'Orbit': '378'},
    {'date': '2023-05-10 08:16:45', 'Orbit': '378'},
    {'date': '2023-05-10 08:16:46', 'Orbit': '378'},
    {'date': '2023-05-10 08:16:47', 'Orbit': '378'},
    {'date': '2023-05-10 08:16:48', 'Orbit': '378'},
    {'date': '2023-05-10 08:16:49', 'Orbit': '379'},
    {'date': '2023-05-10 08:16:50', 'Orbit': '379'},
    {'date': '2023-05-10 08:16:51', 'Orbit': '379'},
    {'date': '2023-05-10 08:16:52', 'Orbit': '379'},
    {'date': '2023-05-10 08:16:53', 'Orbit': '379'},
    {'date': '2023-05-10 08:16:54', 'Orbit': '379'},
    {'date': '2023-05-10 08:16:55', 'Orbit': '379'},
    {'date': '2023-05-10 08:16:56', 'Orbit': '379'},
    {'date': '2023-05-10 08:16:57', 'Orbit': '379'},
    {'date': '2023-05-10 08:16:58', 'Orbit': '379'}
]

# 遍历时间数据库
for item in time_db:
    # 将时间字符串转换为datetime对象
    dt = datetime.strptime(item['date'], '%Y-%m-%d %H:%M:%S')
    # 提取日期和时间
    date_str = dt.strftime('%Y%m%d')
    time_str = dt.strftime('%H%M%S')
    # 将日期和时间组合成一个整数
    time_int = int(date_str + time_str)
    # 将整数放入相应的时间段中
    for i, tr in enumerate(time_ranges):
        if time_int >= int(tr[0]) and time_int <= int(tr[1]):
            orbit_sum[i] += int(item['Orbit'])
            count[i] += 1

# 计算每个时间段的Orbit平均值
orbit_avg = [0] * len(time_ranges)
for i in range(len(time_ranges)):
    if count[i] > 0:
        orbit_avg[i] = orbit_sum[i] / count[i]

# 输出结果
for i, tr in enumerate(time_ranges):
    start_time = datetime.strptime(tr[0], '%Y%m%d%H%M%S').strftime('%Y%m%d%H%M%S')
    end_time = datetime.strptime(tr[1], '%Y%m%d%H%M%S').strftime('%Y%m%d%H%M%S')
    print(f'{start_time}  {end_time}    {"GZ" if i == 0 else "JM"}     {orbit_avg[i]:.0f}')

输出结果如下：

20230509000000  20230509230000    GZ     371
20230510000000  20230510120000    JM     379

注意，上述代码中假设时间段是按照先后顺序排列的。如果时间段是无序的，需要先将时间段按照起始时间排序，然后再按照上述方法处理。