解决该问题的一种方法是在Airflow配置中修改kubernetesExecutor配置，并使用Kubernetes的lifecycle钩子来处理Worker pod的创建和终止过程。

首先，打开Airflow的配置文件（通常是airflow.cfg），找到kubernetesExecutor部分。确保已经启用了Kubernetes Executor，并设置了正确的Kubernetes集群配置。

[kubernetes]
...

# Enable Kubernetes Executor
executor = KubernetesExecutor

...

然后，添加以下配置来定义Worker pod的lifecycle钩子：

[kubernetes]
...

# Define lifecycle hooks for worker pods
worker_pod_lifecycle_hooks = {"postCreate": "post_create_hook", "preDelete": "pre_delete_hook"}

...

接下来，创建一个名为dags/hooks.py的新文件，并添加以下代码：

from airflow.contrib.kubernetes.pod import Pod, Resources
from airflow.contrib.kubernetes.kube_client import get_kube_client

def post_create_hook():
    # This function will be called after the worker pod is created
    # Add your post-create logic here
    pass

def pre_delete_hook():
    # This function will be called before the worker pod is deleted
    # Add your pre-delete logic here
    pass

def apply_hooks():
    # Get the Kubernetes client
    kube_client = get_kube_client()

    # Create a Pod object for the worker pod
    pod = Pod(
        pod_id="worker",
        namespace="default",
        image="airflow-worker",
        cmds=["bash", "-c", "sleep 3600"],
        resources=Resources(),
        pod_lifecycle_hooks={
            "postStart": {"exec": {"command": ["/bin/sh", "-c", "python /usr/local/airflow/hooks.py post_create_hook"]}},
            "preStop": {"exec": {"command": ["/bin/sh", "-c", "python /usr/local/airflow/hooks.py pre_delete_hook"]}}
        }
    )

    # Apply the Pod object to the Kubernetes cluster
    kube_client.create_namespaced_pod(
        body=pod,
        namespace="default"
    )

if __name__ == "__main__":
    # Apply the hooks
    apply_hooks()

此代码示例中的post_create_hook和pre_delete_hook函数分别定义了Worker pod创建后和终止前的处理逻辑。可以在这些函数中添加或修改代码来满足你的需求。

最后，将这个hooks.py文件添加到你的DAG目录中，并在需要使用Worker pod的DAG文件中导入并调用apply_hooks函数。

from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from hooks import apply_hooks

default_args = {
    ...
}

dag = DAG(
    "my_dag",
    default_args=default_args,
    schedule_interval="0 0 * * *",
)

apply_hooks()

task = PythonOperator(
    task_id="my_task",
    python_callable=my_function,
    dag=dag,
)

这样，当Airflow启动DAG时，将会创建一个Worker pod，并在创建后调用post_create_hook函数。当DAG停止时，将会调用pre_delete_hook函数，然后删除Worker pod。

注意：上述示例只是一种解决方法，并不能保证适用于所有情况。根据你的具体需求，你可能需要修改或扩展这些示例代码。