Abstract: This application discloses an information processing method, apparatus, and device, and relates to the field of artificial intelligence (AI). The method includes displaying a user interface, the user interface comprising a received virtual item packet, and the virtual item packet comprising topic information; displaying the topic information of the virtual item packet; receiving an interaction message corresponding to a second user account, the interaction message being matched with target information to request to receive a virtual item in the virtual item packet, and the target information being the topic information or information associated with the topic information; and receiving the virtual item in the virtual item packet in response to matching the interaction message and the target information.

Abstract: A 1D-CNN-based ((one-dimensional convolutional neural network)-based) distributed optical fiber sensing signal feature learning and classification method is provided, which solves a problem that an existing distributed optical fiber sensing system has poor adaptive ability to a complex and changing environment and consumes time and effort due to adoption of manually extracted distinguishable event features.

Abstract: A 1D-CNN-based ((one-dimensional convolutional neural network)-based) distributed optical fiber sensing signal feature learning and classification method is provided, which solves a problem that an existing distributed optical fiber sensing system has poor adaptive ability to a complex and changing environment and consumes time and effort due to adoption of manually extracted distinguishable event features, The method includes steps of: segmenting time sequences of distributed optical fiber sensing acoustic and vibration signals acquired at all spatial points, and building a typical event signal dataset; constructing a 1D-CNN model, conducting iterative update training of the network through typical event signals in a training dataset to obtain optimal network parameters, and learning and extracting 1D-CNN distinguishable features of different types of events through an optimal network to obtain typical event signal feature sets; and after training different types of classifiers through the typical event sign