KEYWORDS: Photovoltaics, Data modeling, Data acquisition, Computing systems, Data communications, Windows, Solar energy, Data transmission, Systems modeling, Instrument modeling
The existing sensor network trust evaluation model cannot be directly applied to the new power system distributed home photovoltaic collection scenario, which is difficult to meet the requirements of strong computing power and high defense power of the new power system. Therefore, a distributed dynamic trust evaluation model based on multi-index detection is proposed. Firstly, the communication trust evaluation based on Bayes is carried out according to the historical interaction of terminal acquisition nodes. Then, the current collected data is evaluated by perceptual trust based on its historical data support degree and regional trust based on probability density. Finally, the entropy weight method is used to dynamically decentralize the values of each trust module. The node activeness and double reward and punishment mechanism are introduced to calculate the comprehensive trust value and realize the dynamic update. The experimental results show that the four levels of trust evaluation model suitable for the new power system environment and can be used to detect the distributed in a short period of time household photovoltaic power generation collection in view of the signal in the scene, such as equipment aging and malicious tampering with cases of abnormal nodes, dynamic, accuracy of node trust evaluation, and according to the specific details provide a reference for analysis of exception type.
With the in-depth development of informatization, the Internet has become an important position for data security protection, an important network terminal of key infrastructure, and an important target for the infiltration and deep latent of hostile forces. In view of the risk of disclosure of important national and enterprise information caused by illegal transmission of important files by internal personnel, this paper studies the key technologies of content security based on NLP, and proposes a text classification method based on label tree, which effectively improves the accurate management of terminal data.
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