Associative Memory for Flight Plan Duration Adjustment
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Solution Overview
Problem
Flight plans often fail to account for real-time conditions that deviate from historical data, leading to inaccuracies and potential disruptions in flight duration, impacting scheduling and passenger expectations.
Innovation Solution
An associative memory system that modifies flight plans by comparing attribute values of incoming flight plans with prior plans, incorporating intangible data such as weather, seasonal trends, and passenger characteristics to adjust flight durations based on relatedness scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If flight plans are based on historical experience and limited information, then the preparation and filing process is simple and organized, but the accuracy of flight duration estimates deteriorates when real-time conditions deviate from historical data
Solution Approach 1:
The system performs preliminary actions by proactively identifying and retrieving relevant historical flight plans before finalizing the current flight plan. The associative memory system pre-processes and stores flight plan data with weighted attributes, enabling quick comparison and adjustment when real-time conditions are input, thus improving accuracy without adding operational complexity.
Solution Approach 2:
The associative memory system acts as an intermediary between historical flight data and current flight planning. It mediates by retrieving relevant historical plans based on weighted attribute matching and using them to adjust predicted flight duration, thereby bridging the gap between limited historical information and real-time conditions without requiring complex real-time data processing.
2Loss of information
If flight plans use only structured data from historical flights, then data processing is efficient, but the system cannot capture intangible factors like weather conditions, turbulence, and landing difficulties that affect actual flight duration
Solution Approach 1:
The system applies parameter changes by assigning different weights to various attributes of historical flight plans based on their relevance to predicting flight duration. This allows the system to prioritize certain intangible factors (like weather conditions or landing difficulties) over others, capturing essential information without requiring equally complex processing of all possible data parameters.
Solution Approach 2:
The associative memory system applies local quality by selectively retrieving and weighting specific attributes of historical flight plans rather than processing all data uniformly. It focuses on locally relevant intangible factors (such as weather en route, turbulence, or landing conditions) that are most impactful on flight duration, thereby capturing critical information without proportionally increasing overall system complexity.
3Productivity
If flight duration is underestimated based on historical data, then scheduling appears efficient, but actual delays occur causing adverse impacts on scheduling, routing, and passenger experience
Solution Approach 1:
The system implements feedback by using actual flight outcomes from historical data to continuously refine and adjust the weighted attributes of flight plan characteristics. This feedback loop allows the system to learn from past deviations between predicted and actual flight durations, improving the reliability of future predictions while maintaining scheduling efficiency through accurate time estimates.
Solution Approach 2:
The system performs preliminary action by proactively adjusting flight duration predictions based on patterns identified in historical data before flights are scheduled. By pre-calculating adjustments for intangible factors like weather conditions and operational difficulties, the system ensures more reliable time estimates are built into schedules in advance, preventing delays rather than reacting to them afterward.
Data Source
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AI summary
An associative memory system, method and computer-readable storage medium are provided to modify a flight plan. In regards to an associative memory system, a receiver module is provided that is configured to receive a flight plan of an aircraft. The flight plan includes a plurality of attribute categories and their associated attribute values. The associative memory system also includes an associative memory configured to compare attribute values of the flight plan with corresponding attribute values of prior flight plans to identify one or more related flight plans. The one or more related flight plans include intangible data related to a respective flight. The associative memory system also includes a modification module configured to modify the flight plan of the aircraft based upon information from one or more of the related flight plans, including the intangible data from one or more of the related flight plans.