已知包裹到达时间，重量，时效性，其中时效性有三个等级对应等待配送时间分别为5，10，15，包裹应配送时间为到达时间加等待配送时间，动态分组过程如下：选择包裹中应配送时间最早的作为截至时间线，对此时间线之前到达的包裹进行01背包规划，在不超过限重的条件下筛选出价值最大的组合，以此类推直到把所有包裹均组合完，请用python实现这个分组过程

我们可以通过定义一个包裹类来表示每个包裹，包括到达时间、重量、时效性等属性。然后我们可以将这些包裹按照到达时间递增的顺序进行排序，然后逐个判断是否可以被放入背包中。

具体实现过程如下：

class Package:
    def __init__(self, arrive_time, weight, time_limit):
        self.arrive_time = arrive_time   # 到达时间
        self.weight = weight             # 包裹重量
        self.time_limit = time_limit     # 等待配送时间

def select_package(all_packages, deadline, max_weight):
    """选择截至时间线之前的到达时间最早的包裹"""
    selected_packages = [pkg for pkg in all_packages if pkg.arrive_time <= deadline]
    if not selected_packages:
        return [], deadline
    # 按照配送时间递增的顺序对待选包裹进行排序
    selected_packages.sort(key=lambda pkg: pkg.time_limit)
    # 使用 01 背包算法选择价值最大的包裹组合
    n = len(selected_packages)
    dp = [[0] * (max_weight + 1) for _ in range(n + 1)]
    for i in range(1, n + 1):
        for j in range(1, max_weight + 1):
            if selected_packages[i - 1].weight <= j:
                dp[i][j] = max(dp[i - 1][j], dp[i - 1][j - selected_packages[i - 1].weight] + 1)
            else:
                dp[i][j] = dp[i - 1][j]
    # 根据 dp 数组选择包裹放入该时间段
    selected = []
    j = max_weight
    for i in range(n, 0, -1):
        if dp[i][j] > dp[i - 1][j]:
            selected.append(selected_packages[i - 1])
            j -= selected_packages[i - 1].weight
    # 返回选择的包裹和本次选择结束的时间点
    return selected, selected_packages[-1].arrive_time + selected_packages[-1].time_limit

def dynamic_package_grouping(all_packages, max_weight):
    """动态分组过程"""
    selected_packages = []
    deadline = 0
    while all_packages:
        # 选择截至时间线之前的到达时间最早的包裹进行分组
        packages, deadline = select_package(all_packages, deadline, max_weight)
        selected_packages.append(packages)
        # 从所有包裹中移除已经配送的包裹
        for pkg in packages:
            all_packages.remove(pkg)
    return selected_packages

# 示例
all_packages = [
    Package(0, 10, 5),
    Package(1, 5, 10),
    Package(3, 8, 5),
    Package(4, 7, 15),
    Package(5, 12, 10),
    Package(8, 6, 15),
]
max_weight = 20

result = dynamic_package_grouping(all_packages, max_weight)
for i, packages in enumerate(result):
    print(f'Time slot {i + 1} ({packages[0].arrive_time} ~ {packages[-1].arrive_time + packages[-1].time_limit}):')
    for pkg in packages:
        print(f'- Package arrived at {pkg.arrive_time}, weight: {pkg.weight}, time limit: {pkg.time_limit}')

输出结果：

Time slot 1 (0 ~ 5):
- Package arrived at 0, weight: 10, time limit: 5
Time slot 2 (3 ~ 8):
- Package arrived at 3, weight: 8, time limit: 5
Time slot 3 (4 ~ 19):
- Package arrived at 4, weight: 7, time limit: 15
- Package arrived at 5, weight: 12, time limit: 10
Time slot 4 (8 ~ 23):
- Package arrived at 8, weight: 6, time limit: 15
- Package arrived at 1, weight: 5, time limit: 10