Systems and methods for assessing fatigue of pilot of aircraft

1 . A system comprising:

a schedule database storing schedule data including a schedule for a pilot of an aircraft, wherein the schedule includes one or more previous flights flown by the pilot, and one or more future flights to be flown by the pilot; and

an artificial intelligence (AI) control unit in communication with the schedule database, the AI control unit configured to:

receive the schedule for the pilot of the aircraft,

receive survey answer data from the pilot, wherein the survey answer data includes answers to questions of a survey conducted before the pilot operates the aircraft, wherein the questions relate to pilot readiness to operate the aircraft, wherein the survey includes (a) one or more topics, (b) the questions regarding the one or more topics, and (c) answer areas for the questions,

use a plurality of machine learning models to analyze the schedule and the survey answer data,

select results of one of the plurality of machine learning models based on an assessed reliability, and

assess a readiness level of the pilot based on the schedule and the survey answer data,

wherein the aircraft is operated in accordance with the readiness level as assessed by the AI control unit.

2 . The system of claim 1 , further comprising a user interface in communication with the AI control unit, the user interface including a display in communication with an input device, wherein the AI control unit shows the survey on the display, and wherein the answers to the questions within the survey are input by the pilot via the input device.

3 . The system of claim 2 , wherein the user interface is onboard the aircraft.

4 . The system of claim 2 , further comprising a survey database in communication with the AI control unit, wherein the survey database stores survey data including the survey.

5 . The system of claim 1 , further comprising a model database in communication with the AI control unit, wherein the model database stores the one or more machine learning models.

6 . The system of claim 1 , wherein the one or more machine learning models comprise a voting classifier model.

7 . The system of claim 1 , wherein the AI control unit is further configured to automatically operate one or more aspects the aircraft based on the readiness level as assessed by the AI control unit.

8 . A method comprising:

storing, in a schedule database, schedule data including a schedule for a pilot of an aircraft, wherein the schedule includes one or more previous flights flown by the pilot, and one or more future flights to be flown by the pilot; and

receiving, by an artificial intelligence (AI) control unit in communication the schedule database, the schedule for the pilot of the aircraft;

receiving, by the AI control unit, survey answer data from the pilot, wherein the survey answer data includes answers to questions of a survey conducted before the pilot operates the aircraft, wherein the questions relate to pilot readiness to operate the aircraft, wherein the survey includes (a) one or more topics, (b) the questions regarding the one or more topics, and (c) answer areas for the questions;

using, by the AI control unit, a plurality of machine learning models to analyze the schedule and the survey answer data;

selecting, by the AI control unit, results of one of the plurality of machine learning models based on an assessed reliability; and

assessing, by the AI control unit, a readiness level of the pilot based on said using,

wherein the aircraft is operated in accordance with the readiness level as assessed by the AI control unit.

9 . The method of claim 8 , further comprising showing, by AI control unit, the survey on a display of a user interface, and wherein answers to questions within the survey are input by the pilot via the input device.

10 . The method of claim 9 , further comprising storing, within a survey database in communication with the AI control unit, survey data including the survey.

11 . The method of claim 8 , further comprising storing, within a model database in communication with the AI control unit, the machine learning models.

12 . The method of claim 8 , wherein the machine learning models comprise a voting classifier model.

13 . The method of claim 8 , further comprising automatically operating one or more aspects the aircraft based on the readiness level as assessed by the AI control unit.

14 . A non-transitory computer-readable storage medium comprising executable instructions that, in response to execution, cause one or more control units comprising a processor, to perform operations comprising:

receiving a schedule for a pilot of an aircraft, wherein the schedule includes one or more previous flights flown by the pilot, and one or more future flights to be flown by the pilot, wherein the schedule data is stored in a schedule database;

receiving survey answer data from the pilot, wherein the survey answer data includes answers to questions of a survey conducted before the pilot operates the aircraft, wherein the questions relate to pilot readiness to operate the aircraft, wherein the survey includes (a) one or more topics, (b) the questions regarding the one or more topics, and (c) answer areas for the questions;

using a plurality of machine learning models to analyze the schedule and the survey answer data;

selecting results of one of the plurality of machine learning models based on an assessed reliability; and

assessing a readiness level of the pilot based said using,

wherein the aircraft is operated in accordance with the readiness level as assessed.

15 . The system of claim 1 , wherein the one or more topics comprise:

a crew, wherein the questions include crew questions regarding a pilot in charge, flight recency, proficiency, and crew mix, and

mission, wherein the questions include mission questions regarding mission complexity and mission changes.

16 . The non-transitory computer-readable storage medium of claim 14 , further comprising showing the survey on a display of a user interface.

17 . The non-transitory computer-readable storage medium of claim 16 , wherein answers to questions within the survey are input by the pilot via the input device.

18 . The non-transitory computer-readable storage medium of claim 14 , wherein survey data including the survey is stored within a survey database.

19 . The non-transitory computer-readable storage medium of claim 14 , wherein the machine learning models are stored within a model database.

20 . The non-transitory computer-readable storage medium of claim 14 , wherein the machine learning models comprise a voting classifier model.

21 . The non-transitory computer-readable storage medium of claim 14 , further comprising automatically operating one or more aspects the aircraft based on the readiness level.