A major event feature time sequence analysis method based on a DTW algorithm
By using the DTW algorithm and multimodal feature extraction, the problem of time misalignment of features in major events is solved, enabling efficient and accurate feature sorting and logical judgment in event analysis, thereby improving the accuracy and reliability of event analysis.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- THE 54TH RESEARCH INSTITUTE OF CHINA ELECTRONICS TECHNOLOGY GROUP CORPORATION
- Filing Date
- 2026-03-31
- Publication Date
- 2026-07-17
AI Technical Summary
Traditional time series analysis methods struggle to accurately characterize the asynchronous or delayed time features of multidimensional major events, making it difficult to accurately depict the evolutionary patterns of events.
The Dynamic Time Warping (DTW) algorithm is combined with multimodal feature extraction. By constructing feature time series and calculating DTW distance, and combining feature logical relationships and dependencies, the order of feature occurrence is determined, and feature importance weights are calculated using entropy weighting or analytic hierarchy process.
It improves the accuracy and reliability of major event analysis, achieves time alignment of multi-source features, and outputs an ordered event evolution logic.
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