Aiming at the problem that the current power OPGW optical cable early warning model adopts a single technical index, this article proposes an early-warning model of power OPGW cable operating status based on joint judgment. Firstly, a multi-element joint judgment power OPGW optical cable prediction model is constructed based on the Long Short-Term Memory (LSTM) model, and the predicted power OPGW optical cable core strain sequence provides a data basis for the early warning of the operation status of the optical cable. Then, based on the identification and classification results of the Multilayer Perceptron (MLP) model, the early warning level is divided, and the core strain data of the power OPGW optical cable obtained by the prediction module is identified and output the early warning level, so as to realize the early warning of the operation status of the power OPGW optical cable. Finally, model experimental analysis is carried out. The experimental results show that the proposed model can provide early warning for power OPGW optical cable, and further improve the risk management and control strategy of OPGW optical cable, so as to complete the operation and maintenance of power OPGW optical cable more efficiently
To fully understand the energy consumption characteristics of 5G base-station, a DBSCAN-based energy consumption pattern clustering identification method is proposed for 5G base-station. Firstly, this paper analyzes the daily-curve characteristics of power consumption behavior in typical application scenarios of 5G base-station for further pattern clustering identification. Then, the proposed pattern clustering identification method is depicted based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering decision, which is composed of the feature extraction for power consumption daily-curve of 5G base-station. Finally, the experiment is implemented using actual operation data of 5G base-station as data source. The experiment results illustrate that the proposed method can effectively identify the clustering characteristics of the energy consumption behavior for 5G base-station.
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