Deep unsupervised learning approach, device and storage medium for airspace complexity evaluation
US12045702B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 24, 2020 |
| Grant date | Jul 23, 2024 |
| Priority date | — |
| Expiry date | May 25, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG08G5/727
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
This application provides an airspace complexity evaluation method based on deep unsupervised learning for air traffic management, which includes the following parts. A stacked autoencoder is used to establish an airspace complexity evaluation model. Input the airspace complexity factors into the stacked autoencoder to obtain the low-dimensional embedded representations of the airspace complexity factors. Cluster the low-dimensional embedded points to capture the centroids of the airspace complexity data. The application utilizes the soft assignment distribution and real assignment distribution of the embedded representations to construct a training loss function which optimizes the airspace complexity evaluation model by gradient descent algorithm. The trained airspace complexity evaluation model and the three obtained cluster centroids describing the airspace complexity level are used to evaluate the current airspace complexity.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.