System and methods for risk and risk precursor identification in commercial aviation operations

A method of identifying aviation risk precursors from a data source of historical operational flight track data, comprising:

• providing a risk detection modeling system comprising a set of selectable risk models stored within a memory device on a computing platform having one or more processors configured to execute machine learning models, wherein each selectable risk model:
• receives a set of data from the data source comprising flight data for individual aircraft flown within a geographic region;
• and
• is trained to identify potential risk precursors to a predetermined type of risk condition;
• accessing the risk detection modeling system with a risk determination application programming interface (API) in response to a command from a user interface to select a risk model from the set of the selectable risk models;
• removing a portion of the set of data not required by the selected risk model prior to providing the set of data from the data source to the selected risk model to improve processing speed and data transfer times;
• downloading the set of data from the data source via the risk detection modeling system and processing according to the selected risk model;
• interpreting the processed set of data via the risk determination API to determine if any potential risk precursors were identified in the set of processed data for the selected risk model prior to conditions for any predetermined type of risk within the selected risk model being met by the flight track data;
• converting the interpreted processed set of data into a visual format for display on the user interface according to the selected risk model to distinguish any potential risk precursors from nominal data;
• and
• overlaying the visual format onto a map displayed on the user interface, wherein the map corresponds to the selected risk model.

A method for identifying aviation risk precursors from a source of real-time flight track data, comprising:

• storing a set of selectable risk models within a risk detection modeling system having a memory device on a computing platform having one or more processors configured to execute machine learning models in communication with the source of real-time flight track data, wherein each selectable risk model receives and analyzes a set of data from the source of real-time flight track data and is trained to identify potential risk precursors to a predetermined type of risk condition;
• accessing the risk detection modeling system with a risk determination application programming interface (API) configured to:
• select a risk model from the set of the selectable risk models;
• improve processing speed and data transfer times between the risk detection modeling system and the source of real-time flight track data by performing at least one of:
• a removal of a portion of the set of data not required by the selected risk model prior to providing the set of data from the source of real-time flight track data to the selected risk model;
• and
• a conversion of the received set of data into a usable format by the selected risk model, wherein a smoothing function is applied to eliminate values that could signal risk but are merely the result of the conversion prior to providing to the selected risk model;
• cause the selected risk model to process the received set of data based on the selected risk model;
• interpret the processed set of data to determine if any potential risk precursors were identified in the received set of data for the selected risk model prior to conditions for any predetermined type of risk within the selected risk model being met by the flight track data;
• and
• generate instructions to convert the interpreted processed set of data into a visual format according to the selected risk model to distinguish any potential risk precursors in the processed set of data from nominal data from the processed set of data;
• and
• accessing the risk determination API with a user interface configured to:
• send a request causing the risk determination API to select the risk model;
• receive the processed set of data according to the generated instructions;
• and
• overlay the processed set of data according to the visual format onto a map corresponding to the selected risk model.