ATC Sector Complexity Detection Using Energy-Concentrated Flight Paths

The patent segments the complexity evaluation into two distinct phases: an offline training phase where the neural network is trained using comprehensive analytical functions to learn complex patterns, and an online evaluation phase where the trained network provides rapid predictions. This segmentation allows the system to benefit from both the precision of complex analytical methods and the speed of simplified calculations at runtime.

The system performs preliminary action by training the neural network offline using extensive analytical complexity evaluations before actual sector management operations. This pre-computation stores the learned patterns in the network weights, enabling fast online evaluations without repeating the computationally intensive analytical calculations during time-critical operations.

The patent implements dynamic sector management where sector definitions and controller allocations are continuously adjusted based on real-time complexity evaluations. The system can dynamically create, merge, or split sectors and reassign controllers automatically according to current traffic conditions, replacing static manual assignments with adaptive automated decisions that respond to changing operational requirements.

The system incorporates feedback loops where complexity evaluation results are continuously fed back to the sector management module, which then adjusts sector configurations and controller assignments accordingly. This closed-loop control enables automatic adaptation to changing traffic patterns without manual intervention, improving both productivity and adaptability simultaneously.

The patent replaces the mechanical execution of complex analytical functions during online operations with a neural network-based computational model. The neural network, once trained, performs evaluations through simple forward propagation calculations rather than executing complex analytical algorithms, significantly reducing computational complexity while preserving the reliability gained from training on comprehensive data.

The system creates a simplified copy of the complex analytical evaluation capability through the neural network model. The network learns and replicates the decision-making patterns of the analytical functions during training, then uses this learned representation to perform evaluations efficiently during operation, maintaining reliability while reducing computational burden.