# Construction of Power Fault Knowledge Graph Based on Deep Learning
## 4 Experiments and Results
### Experiment 1-BiSGRU-GA
#### 4.1.5 Experimental Results  
We selected three widely used anomaly detection models to compare with the proposed model: PCA, DeepLog, and a GRU-based Deep Learning Anomaly Detection Model. PCA is a model for log anomaly detection based on offline mode, which was implemented by [PERSON] et al. [29]. DeepLog constructs an anomaly detection model based on LSTM, which is reproduced in this paper based on [22]. The GRU-based Deep Learning anomaly detection model is an improvement on the basic DeepLog model and can complete the online detection of log anomalies. Firstly, the experiment compares the detection effects of the PCA, DeepLog and GRU models, and then compares the detection speed of the PCA, DeepLog, and GRU models with the BiSGRU-GA model proposed in this paper, so as to comprehensively evaluate this model from many aspects. The comparative experiment of this paper uses the operation log data set of the whole business unified data center of a provincial power company. We divided the 5647 log event samples (i.e., normal or daily logs) into a training set and test set according to the ratio of 3:1. Table 2 provides the specific information of the training set and test set used in this experiment. Table 3 and Figure 11 shows the performance of the four models. It can be seen that the PCA model performs well in false positive indicators, indicating that the PCA model \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Parameter Value** \\ \hline
**Word vector dimension** & 200 \\
**Batch\_size** & 64 \\
**Number of hidden nodes** & 100 \\
**Optimizer** & Adam \\
**Dropout** & 0.5 \\
**Number of iterations** & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: BiSGRU-GA Model parameter setting. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Normal Logs** & **Number of Abnormal Logs** & **Number of Log Keys** \\ \hline Training set 4235 & 0 & 17 \\ test set 1411 & 45 & 17 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Training set and test set data information. controls the abnormal false positive rate well, but obtains more false negatives, indicating that the PCA model tends to judge the abnormal logs as normal. In contrast, the DeepLog and GRU models achieved less false positives and false negatives. The BiSGRU-GA model is slightly better than the DeepLog model and the GRU model in terms of false positives and false negatives. The performance is improved to a certain extent. Figure 11 compares the above four models in terms of accuracy, recall, and F-measure. As can be seen from the figure, except for the F-measure index of the proposed model being slightly lower than that of the DeepLog model, the proposed model achieved the highest results in the three indexes of accuracy, precision, and recall. Compared with the PCA model, this model increased by 1.1%, 7.4%, 22.1%, and 10.9% in the four indexes of accuracy, precision, recall, and F-measure, respectively. Compared with the DeepLog model, the accuracy rate, precision rate, and recall rate increased by 0.5%, 0.9%, and 2.5%, respectively. The F-measure is 0.3% lower, and compared with the GRU model, the accuracy, precision, recall, and F-measure increased by 4.4%, 2.0%, 1.6%, and 0.6%, respectively. The overall performance is slightly better than the DeepLog model and the GRU model, and significantly better than PCA model. In order to verify the improvement of the running speed of this model, we compared this model with the PCA, DeepLog, and GRU-based deep learning anomaly detection models. Two indicators are used to measure the running speed of the model: the total time spent detecting all logs and the average time spent detecting each log. At the same time, we  
Figure 11: Experimental results of anomaly detection model performance comparison. \begin{table}
\begin{tabular}{c c} \hline \hline
**FP** & **FN** \\ \hline PCA 18 & 64 \\ DeepLog 11 & 35 \\ GRU 9 & 32 \\ BiSGRU-GA 7 & 21 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of PCA, DeepLog, GRU.