The Deadline policy \D" indicates the minimum rate at which a Data Writer will send data. It also indicates how long a Data Reader is willing to wait for new data. This policy is useful for cases where a Topic is expected to have each instance updated periodically. The default is a value of in¯nity, it means there is no deadline. For example, the braking electronic unit requires a new value of \the vehicle speed" to be received every 100 ms. If that deadline passes without a new command, the application will be noti¯ed and can apply a default safe command until a new appropriate command comes in. Bene¯ts34:

截止时间策略"D"表示Data Writer发送数据的最小速率。它还表示Data Reader愿意等待新数据的时间长度。该策略对于预期每个实例定期更新的主题非常有用。默认值为无限大，表示没有截止时间。例如，制动电子单元要求每100毫秒接收到"车辆速度"的新值。如果超过该截止时间而没有新的命令，则应用程序将收到通知，并可以应用默认的安全命令，直到接收到新的适当命令为止。好处包括：

相关问题

Deadline 提示 这个错误 该如何解决 Error: FailRenderException : Error: file: C:/Users/rfarm/AppData/Local/Temp/tmp15FD.tmp line 51: Cannot load scene "T:/dpcq2_02/sequences/E15/dp2-15-14-0240/work/Lighting/maya/dp2-15-14-0240_lighting_zhangxinyu_v002.mb". Please check the scene path, then try opening the scene on the machine which ran this job to troubleshoot the problem.

这个错误提示是无法加载指定的场景文件，请检查场景文件路径是否正确，然后尝试在运行作业的机器上打开场景文件以排除问题。可能的原因包括：文件路径错误、文件被移动或删除、文件无法访问或文件损坏等。您可以尝试以下解决方法：

1. 确认文件路径是否正确，特别是网络文件路径是否正确访问。

2. 检查文件是否存在于指定的路径中。

3. 确认文件是否可以被访问，尝试访问文件并确保您有访问权限。

4. 尝试使用其他软件打开该文件，以确认文件是否已损坏。

5. 如果以上方法都不能解决问题，您可以尝试重新保存该文件，然后再次尝试渲染。

按照以下要求写出python代码，Suppose there are n assignments A= [a1, a2 …ai …an]that you need to complete before the deadline. An assignment ai= [durationi, deadlinei] need durationi days to complete and must be completed before or on deadlinei. You can only do one assignment at a time and start next assignment once the current assignment is completed. Assuming you start on day 1, implement an efficient algorithm to find the maximum number of assignments you can complete.

以下是Python代码：

def max_assignments(A):
# 按照deadlinei从小到大排序
A.sort(key=lambda x: x[1])
# 初始化当前时间和已完成的任务数
current_time = 0
completed = 0
for i in range(len(A)):
# 如果当前时间加上任务需要的时间小于等于deadlinei，则可以完成该任务
if current_time + A[i][0] <= A[i][1]:
current_time += A[i][0]
completed += 1
return completed

# 测试代码
A = [[2, 4], [3, 5], [1, 2], [4, 7], [5, 9]]
print(max_assignments(A)) # 输出3