# Communication Fault Maintenance Decision of Information System Based on Inverse Symmetry Algorithm
## 3 Experimental Analysis
### Effectiveness of Fault Diagnosis  
The validity of the fault diagnosis is verified by comparing with the actual operation data. Take five groups of information systems in Table 1 as an example. Where \"1\" indicates abnormal test and \"0\" indicates normal test. See Table 1 for statistical results of test data.  
In the first group of fault test data, abnormal alarms occur in load balancing, SDH (Synchronous Digital Hierarchy), application program, database, and interface program. According to the method in this paper, the possibility of platform software failure is the highest. In the second group of fault test data, abnormal alarms are generated in the host, switch, pigtail, PDH (Plesiochronous Digital Hierarchy), and disaster recovery program. According to the method in this paper, abnormal alarms can be calculated. In the third group of test data, the storage, switch, optical cable, pigtail, middleware, and interface program all send out abnormal alarms. According to the traditional manual judgment method, all system attributes with alarm information need to be searched. In the actual operation process of the power information system, information equipment, communication equipment, and platform software are relatively independent, so it is difficult to determine which group of problems it is according to the manual judgment method. It takes time and effort to find out the cause of the accident one by one. According to the method in this paper, the communication equipment in the third group of fault state is the most likely to fail, and the equipment that fails can be found quickly. In the fourth group of test data that fails, the load balance, optical cable, SDH, application degree, and middleware all send abnormal alarms, and according to the method in this paper, the communication equipment is the most likely to fail; in the same way, in the fifth group of failure test data, many attributes have alarm information. According to this method, we can quickly determine that the communication equipment has the highest failure probability, followed by the platform software. At the same time, according to the result of the attribute reduction of the fuzzy set, load balancing, pigtail, interface program, and disaster recovery program cannot be considered or arranged in the final troubleshooting, which saves time when troubleshooting.  
After confirming the specific location of the communication fault, corresponding maintenance decision are made according to different communication fault types, as shown in Table 2.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Attribute Item** & **Group 1** & **Group 2** & **Group 3** & **Group 4** & **Group 5** \\ \hline Load balancing & 1 & 0 & 0 & 1 & 1 \\ Host & 0 & 1 & 0 & 0 & 1 \\ Storage & 0 & 0 & 1 & 0 & 0 \\ Virtual machine & 0 & 0 & 0 & 0 & 0 \\ Firewall & 0 & 0 & 0 & 0 & 0 \\ Switch & 0 & 1 & 1 & 0 & 0 \\ Optical cable & 0 & 0 & 1 & 1 & 1 \\ Pigtail & 1 & 1 & 1 & 0 & 0 \\ SDH (Synchronous Digital & 1 & 0 & 0 & 1 & 0 \\ Hierarchy) & 0 & 1 & 0 & 0 & 1 \\ Application & 1 & 0 & 0 & 1 & 1 \\ Middleware & 0 & 0 & 1 & 1 & 0 \\ Database & 1 & 0 & 0 & 0 & 0 \\ Interface program & 1 & 0 & 1 & 0 & 1 \\ Disaster recovery procedure & 0 & 1 & 1 & 0 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test data statistics.  
From the experimental results in Table 2, it can be seen that this method can effectively diagnose the communication fault of the information system based on the inverse symmetry algorithm, and make the maintenance decision in the shortest time, which verifies the effectiveness of this method in improving the communication performance of the information system.