服务器硬盘变missing了,RAID一块硬盘显示红色PDMissing解决办法

问题

a4c26d1e5885305701be709a3d33442f.png

进入WebBIOS主页,发现一块硬盘显示红色PD

Missing状态,同时阵列降级变成了蓝色Degraded状态,此时数据还是可用的,点击【Physical

View】进入物理视图

解决方案

1:点击【Physical View】进入物理视图

2:发现一块黑色硬盘显示Foreign Unconfigured Bad状态,选中该硬盘进入属性页面

3:在属性列表中找到“Media Error”和“Pred Fail

Count”两项(如果找不到请点击【Next】翻页),两项都是零,说明硬盘无故障,可以放心使用(如果有错误建议直接更换硬盘)

a4c26d1e5885305701be709a3d33442f.png

4:至此,通过Clear Foreign Configuration,可以解决此问题。选择【Make Unconf

Good】,点击【GO】执行

5:返回home可以看到一块蓝色硬盘显示Foreign Unconfigured Good状态,点击【Scan

Devices】

6:提示发现外来配置信息,选择【Clear】清除,然后再回到home,可以看到硬盘开始重建(Rebuild)了。

### Predictive RNN Usage and Implementation #### Overview of Predictive RNNs Predictive recurrent neural networks (RNNs), such as PredRNN, are designed to handle spatiotemporal data by integrating both spatial and temporal dependencies into a unified memory structure. Traditional multi-layered RNN architectures fail to account for the influence from higher-level cells at time \(t\) on lower-level cells at time \(t+1\)[^2]. This oversight can lead to suboptimal performance when dealing with complex sequences that require understanding long-term dependencies. #### Architecture Design To address this limitation, researchers introduced PredRNN which incorporates Spatial-Temporal Long Short Term Memory units (ST-LSTM). These ST-LSTM layers allow each cell state not only to pass information forward through time but also across different levels within the network hierarchy. By doing so, it ensures better propagation of relevant features between consecutive frames or steps in sequence prediction tasks [^3]. #### Practical Applications In practical applications like video frame forecasting or human activity recognition systems where capturing detailed motion patterns over multiple timesteps is crucial; predictive RNN models have shown significant improvements compared to conventional approaches due to their enhanced ability to model intricate dynamics present in real-world datasets [^4]. ```python import torch from torch import nn class PredRNNSpatialTemporalLSTMCell(nn.Module): def __init__(self, input_size, hidden_size): super().__init__() self.input_size = input_size self.hidden_size = hidden_size # Define parameters here based on specific requirements def forward(self, x_t, h_prev, c_prev): """Forward computation step.""" # Implement LSTM operations considering spatial-temporal aspects. ... return new_h, new_c def create_predrnn_model(input_dim=..., num_layers=...): layers = [] for i in range(num_layers): layer_input_dim = input_dim if i == 0 else output_dim_of_previous_layer rnn_cell = PredRNNSpatialTemporalLSTMCell(layer_input_dim, ...) layers.append(rnn_cell) return nn.Sequential(*layers) model = create_predrnn_model() ```
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