KEYWORDS: Data modeling, Blockchain, Computer security, Fuzzy logic, Systems modeling, Data transmission, Performance modeling, Detection and tracking algorithms, Data storage, Network security
This paper proposes a novel data security sharing system that integrates attribute-based encryption, keyword fuzzy search, and blockchain technology to address the poor performance and data vulnerability in existing data openness and sharing systems. The proposed system employs the attribute-based encryption algorithm to encrypt data, supplemented by blockchain technology to facilitate system search, thereby establishing the keyword search mechanism and repository for fragile items. The research results demonstrated that the average delay time for data sharing search in the new system ranged from 1.0 to 4.0 seconds, with a reduction of approximately 1100 milliseconds in encryption time compared with other methods. The average initialization time was between 15 and 20 milliseconds. Moreover, at an equivalent transaction volume, the proposed method exhibited a maximum processing time of 11.2 seconds, which was significantly improved by 4.2 seconds compared with other methods. These results underscore the enhanced performance of the proposed method in terms of encryption and keyword search efficiency, indicating its potential to advance data security sharing research and open up a promising path for future investigations.
KEYWORDS: Data modeling, Systems modeling, Machine learning, Performance modeling, Matrices, Power grids, Solar energy, Neural networks, Tunable filters, Power consumption
Accurate power time series prediction is crucial for stable operation and optimized scheduling of power grids rapidly developing and integrating increasing renewable energy sources. Traditional prediction models often neglect the spatiotemporal characteristics of power grid data, resulting in inadequate accuracy. To address this, we propose an enhanced dynamic and static graph attention network model for power time series prediction. Experimental results on the Solar Energy and Electricity datasets demonstrate superior fitness values of 99.01 and 99.88 after 30 and 16 iterations, respectively. The model achieves an RMSE value of 2.14% within 100 seconds on the Solar Energy dataset and a CORR value of 0.982 after 30 cycles. In practical application, the method consistently exhibits a low RSE value (within a fluctuation range from 0.021 to 0.035) as the Layer parameter increases. The proposed method offers high prediction accuracy, providing valuable insights for power system management, operation, and scheduling, thereby enhancing the safety, stability, and economic operation of power systems.
The current conventional illegal access behavior security control strategy for heterogeneous cloud resources mainly realizes the access control of resources by conceiving the digital identity of access units, which leads to the low security of access control due to the low encryption of heterogeneous cloud resources. In this regard, the heterogeneous cloud resource illegal access behavior security control strategy based on life cycle characteristics is proposed. By analyzing the user access history behavior data, the trust value of user access operation is calculated. And the access control policy is constructed based on the encryption of user's operation behavior as well as resources by using a two-layer encryption algorithm. In the experiment, the designed illegal access behavior security control policy is tested for security type. The final result can prove that when using the proposed method for access control of heterogeneous cloud resources, the speed of intercepting illegal users is faster, and when facing various types of attacks, this method has a low success rate and high access control security.
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