Computer Simulation Method for Data Trust Analysis Based on Average Deviation Algorithm

The computer simulation trust evaluation enhances the performance and recognition of the simulation system, but this technology still faces the problem of low calculation accuracy. This research proposes a data trust analysis method based on the improved average deviation degree algorithm. First, the average deviation degree and data trust degree are analyzed, and then the average deviation degree calculation model based on the second order oscillation and particle swarm optimization with repulsion factor (SecRPSO) is constructed. Finally, the performance verification and application analysis of the model are carried out. The research shows that the prediction model established based on SecRPSO optimization algorithm has better fitting degree with the real load curve, and has better prediction accuracy and is relatively stable. Comparing the mean error (ME) and absolute mean relative error (MAPE) of three different algorithms, the ME value of the model proposed in this study is 1.5673, while the ME value of Grid search and Basic PSO are 35.0983 and 14.4811, respectively. The ME value of the model proposed in this paper is significantly lower than that of other models. Moreover, its MAPE is generally small, the predicted value can track the change of the actual value, and the error fluctuates within the allowable range of engineering application. The data trust analysis method based on the average deviation algorithm can effectively calculate the expected threshold information of the data, and find out the tolerance threshold. Therefore, this method can be extended to various management data such as finance, engineering, and environmental protection.