In order to solve the problem of low accuracy in application load prediction, this paper proposes an application load prediction model based on SA-IPSO-BiLSTM. Firstly, the model puts the data into the BiLSTM neural network for training and uses the adaptive algorithms to automatically adjust the parameters of the BiLSTM neural network. Then, an improved particle swarm optimization algorithm is used to optimize the parameters of the BiLSTM neural network. Finally, the optimized BiLSTM is used for the application load prediction. Comparison with the existed prediction models, the result demonstrates that the SA-IPSO-BiLSTM model has a higher accuracy and strong applicability in application load prediction.
At present, blockchain users lack effective means of supervision and authorization for intermediaries at different levels, making user data privacy at risk of leakage. Aiming at the above problems, this paper studies the trusted supervision technology scheme suitable for blockchain data privacy protection, enriches and expands on the classic BIP32 protocol, and designs and implements a dynamic authorization strategy in the new protocol. Authorization policies make private data truly in the hands of users by changing and setting regulatory permissions. The experimental results and performance comparison show that the blockchain data privacy protection method proposed by this scheme is efficient, stable, and has strong practicability, which is suitable for data privacy protection scenarios of trusted supervision. At the same time, experiments show that with the increase of the encryption key depth, the supervision time increases linearly.
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