Aiming at the problems of security risk in vehicle communication and complex identity re-authentication when vehicles enter new infrastructure coverage in Vehicular Ad hoc NETwork （VANET）， a blockchain-based V2I （Vehicle-to-Infrastructure） fast handover authentication scheme in VANET was proposed. The decentralized， distributed and tamper-proof characteristics of blockchain were utilized to realize the storage and query of vehicle authentication information. Token mechanism was used to reduce the number of queries of blockchain， and simplify handover authentication process between Road Side Units （RSUs）. Because only the validity of token needed to be checked in subsequent authentication， rapid handover authentication of RSU was realized. Batch authentication was adopted to reduce the computation overhead and improve the efficiency of message authentication. In addition， the traceability and revocation of malicious vehicles was realized， and the anonymous identities of vehicles were updated in time to ensure the anonymity of vehicles. Compared with anonymous batch authentication scheme， authentication scheme with full aggregation， certificateless aggregate signature scheme， blockchain-based authentication scheme， the proposed scheme reduced the time consumption for message authentication by 51.1%， 77.45%， 77.56% and 76.01%. The experimental results show that proposed scheme can effectively reduce the computation overhead and communication overhead in VANET.

With massive features and long unit delays, it is very difficult to effectively select the most appropriate features and corresponding delays during the modeling of the main steam temperature of thermal power unit. Therefore, a modeling method of the fusion model jointly considering feature selection and delay selection was proposed. Aiming at the high dimensionality of the features of thermal power units, the features highly associated with the main steam temperature were selected through the correlation coefficients and the feature selection of gradient boosting machine. For the delay identification, the Temporal Correlation Coefficient-based Time Delay(TD-CORT) calculation algorithm was designed to estimate the time delay between each parameter and the predicted target main steam temperature. And the automatic matching of the sliding window size was realized for the prediction target and the calculation complexity. Finally, the fusion model of Deep Neural Network (DNN) and Long Short-Term Memory (LSTM) was used to predict the main steam temperature of the thermal power unit. The deployment results on a 1 000 MW ultra-supercritical coal-fired unit in China show that the proposed method has the prediction Mean Absolute Error (MAE) value of 0.101 6, and the prediction accuracy 57.42% higher than the neural network without considering the delay.