# Communication Fault Maintenance Decision of Information System Based on Inverse Symmetry Algorithm
## 2 Definition of Algorithm
### Information System Communication Fault Diagnosis  
There are many system attributes and fault types in the information system based on the inverse symmetry algorithm. Therefore, the advantages of Bayesian network and rough set are integrated. Removing redundant attributes in the system through rough sets. On this basis, the Bayesian network model with the minimum diagnosis set is obtained by combining the prior knowledge in the operation of the information system [15; 16]. The input of the network is the condition attribute of the decision table, and the output is the decision attribute of decision table. Through the above operations, the optimal fault diagnosis rules are established, so as to improve the efficiency and accuracy of diagnosis. The detailed algorithm steps of realizing communication fault diagnosis of information system are shown in Figure 3.  
The specific steps of the algorithm are as follows:  
1. Sort out and analyze the original data of fault records of the system operation, extract characteristic attribute items, combine synonyms and synonyms, and make word frequency statistics.
2. According to the prior knowledge, a fault decision table including all system attribute values is formed.
3. Rough set theory is used to reduce the characteristic attribute of the decision table, to form the minimum decision rule set, and to establish the fault diagnosis knowledge base.
4. According to the word frequency statistics and the number of faults of the characteristic attributes retained in the diagnosis knowledge base, the prior probability information of the characteristic attributes is obtained.
5. A systematic Bayesian network fault information diagnosis model is established, including the conditional probability or joint conditional probability of all nodes in the network structure with associated relationships, the connection between nodes, the reason node to the intermediate node, and the fault node [17].  
Using the communication fault diagnosis model established above, when the information system fault diagnosis is carried out, the probability calculation method of all fault cause nodes is as follows:  
1. Input the result node vector into the Bayesian network.
2. For each node n that has not been processed in the network, if it has the fact (evidence) of occurrence, mark it as processed, otherwise continue to execute downward.  
Figure 3: Trouble shooting algorithm flow chart.  
If one of its child nodes has not been processed, the node will not be processed, otherwise continue to execute downward.
4. According to the probability of all the sub nodes of node n and the conditional probability or joint conditional probability, the probability distribution of node n is calculated according to the conditional probability formula, and node n is marked as processed.
5. Repeat (2) to (4) and handle all nodes.  
At this time, the probability distribution of each cause node is its probability of occurrence/non-occurrence. According to the probability distribution of the cause node, the failure causes are sorted according to the probability [18]. If the probability is similar, the failure causes are processed according to the parallel events, and then the system attributes corresponding to the failure causes are detected in the information system in order to make the communication failure maintenance decision quickly.