Patent ID: 11882299
Assignee: NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY
Field: Audio-visual technology (Electrical engineering)
Classification: CPC H  Y | IPC G  H

Claim 0:
1. A predictive contrastive representation method for multivariate time-series data processing, comprising:
obtaining multivariate situational information historical data, wherein the multivariate situational information historical data comprises coding situational data and prediction situational label data, and the prediction situational label data comprises a positive sample and a negative sample; the multivariate situational information historical data is historical data generated by unmanned aerial vehicles UAVs in a mission-level confrontation platform; the coding situational data is used to encode situational information at a current moment; the prediction situational label data represents a predictive feature correlation autoregressive loss used to calculate the multivariate situational information encoded in a temporal encoder; the situational information comprises statistics of a number of UAVs and spots to be reconnoitered, or locations and status information of the UAVs and the spots to be reconnoitered;
encoding the coding situational data by using a fully connected network and a convolutional neural network (CNN) to obtain single-frame coding information at the current moment;
encoding the single-frame coding information based on a long short-term memory (LSTM) network and an attention mechanism network to obtain temporal coding information at the current moment:
mapping the temporal coding information at the current moment and future situational information by using a logarithmic bilinear model to obtain a similarity between the temporal coding information at the current moment and the future situational information;
training the similarity according to a noise contrastive estimation method and the prediction situational label data, and constructing, based on a training result, a predictive contrastive loss function for the temporal coding information at the current moment and the future situational information;
sampling the prediction situational label data according to a corresponding optimal loss in the predictive contrastive loss function to obtain a sampling probability of the positive sample;
optimizing the predictive contrastive loss function by using a direct proportion property between the sampling probability and the similarity to obtain an optimized predictive contrastive loss function; and
constructing mutual information between the temporal coding information at the current moment and the future situational information based on the optimized predictive contrastive loss function, and performing predictive contrastive representation by maximizing the mutual information; wherein performing predictive contrastive representation by maximizing the mutual information comprises:
predicting future information by maximizing the mutual information between the future situational information and the temporal coding information at the current moment.