Welcome to this exciting journey into data processing! 🚀 Let's dive into the world of log analysis and learn how to extract meaningful information from complex data!

Imagine a social networking application that allows users to form groups. Each group has a unique ID ranging from 1 up to n, the total number of groups. Interestingly, the app keeps track of when a group is created and deleted, logging all these actions in a string.

The task before us is to create a Python function named analyze_logs(). This function will take as input a string of logs and output a list of tuples representing the groups with the longest lifetime. Each tuple contains two items: the group ID and the group's lifetime. By 'lifetime,' we mean the duration from when the group was created until its deletion. If a group has been created and deleted multiple times, the lifetime is the total sum of those durations. If multiple groups have the same longest lifetime, the function should return all such groups in ascending order of their IDs.

For example, if we have a log string as follows:
"1 create 09:00, 2 create 10:00, 1 delete 12:00, 3 create 13:00, 2 delete 15:00, 3 delete 16:00",
the function will return: [(2, '05:00')].

Firstly, we import the datetime module from Python's standard library. This module provides functions and classes for working with dates and times. Once we separate the input string into individual operations, we use the datetime function to parse the timestamps contained in these operations.