earticle

영어

Dangerous signal mining method of dangerous goods transport vehicles and how to improve the accuracy of the mining are studied in the paper. There are many dangerous conditions such as high temperature, high pressure and other dangerous condition caused by the influence of the external factors such as friction in the transportation process of dangerous goods. If people can discover these dangerous condition signals as soon as possible, they can deal with them as soon as possible, which can effectively reduce the transport accidents and loss. The dangerous condition signals change as the interference of different nonlinear random mutation is present. The traditional signal mining methods have not a good solution to properly capture abnormal signal nonlinear changes, which causing flammable signal detection in the transport of dangerous goods inaccurate. The paper proposes a dangerous signal mining model based on the characteristics association mining algorithm for dangerous goods transport vehicles. First, the paper studies Cluster analysis theory and present the model for dangerous signal mining for dangerous goods transport vehicles. Second, the paper present wavelet transform model for the data extraction from transport vehicle status signal feature, and present a new mining model based the characteristics association data mining algorithm for dangerous signal mining, which can improve the accuracy of the mining. Finally, the experiment was done and shown that the new mining model for dangerous goods transport vehicles dangerous signal mining, can greatly improve the accuracy of the excavation.