Unmanned aerial vehicle maintenance troubleshooting decision tree

A method comprising:

• receiving, by at least one server and from a vehicle, a maintenance notification associated with the vehicle, wherein the maintenance notification includes an identifier of the vehicle;
• determining, by the at least one server, a profile associated with the vehicle based at least in part on the identifier, the profile enabling access to at least one of an operational history or a maintenance history associated with the vehicle;
• determining, by the at least one server and based at least in part on the profile, a configuration comprised of one or more vehicle components assembled to form the vehicle;
• determining, by the at least one server and based at least in part on the configuration and the maintenance notification, one or more additional profiles associated with one or more additional vehicles, the one or more additional vehicles including a vehicle component of the one or more vehicle components and the one or more additional profiles indicating that the one or more additional vehicles are associated with the maintenance notification;
• accessing, by the at least one server, fleet data associated with the one or more additional profiles;
• determining, by the at least one server, a decision tree that prioritizes one or more maintenance operations to be performed on the vehicle, the decision tree being based at least in part on the fleet data associated with the one or more additional profiles;
• determining, by the at least one server, weights in the decision tree associated with the fleet data, the weights being assigned based at least in part on a data source associated with the fleet data, a statistical likelihood that a maintenance action isolates a cause of the maintenance notification, and an estimate of time to complete the maintenance action, the weights including a first weight associated with a first portion of the fleet data of a first type including operational history data, the weights including a second weight associated with a second portion of the fleet data of a second type different than the first type;
• determining, by the at least one server and based in part on at least one selection enabling traversal of the decision tree, a maintenance action to resolve the maintenance notification, the at least one selection being received via user input to a user interface;
• and
• causing, via execution of a software update, automatic performance of the maintenance action.

A non-transitory computer-readable storage medium having computer executable instructions stored thereupon which, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• receiving, from a vehicle, a maintenance notification associated with the vehicle, wherein the maintenance notification includes an identifier of the vehicle;
• determining a profile associated with the vehicle based at least in part on the identifier, the profile enabling access to at least one of an operational history or a maintenance history associated with the vehicle;
• determining, based at least in part on the profile, a configuration comprised of one or more vehicle components assembled to form the vehicle;
• determining, based at least in part on the configuration and the maintenance notification, one or more additional profiles associated with one or more additional vehicles, the one or more additional vehicles including a vehicle component of the one or more vehicle components and the one or more additional profiles indicating that the one or more additional vehicles are associated with the maintenance notification;
• accessing fleet data associated with the one or more additional profiles;
• determining a decision tree that prioritizes one or more maintenance operations to be performed on the vehicle, the decision tree being based at least in part on the fleet data associated with the one or more additional profiles;
• determining weights in the decision tree associated with the fleet data, the weights being assigned based at least in part on a data source associated with the fleet data, a statistical likelihood that a maintenance action isolates a cause of the maintenance notification, and an estimate of time to complete the maintenance action, the weights including a first weight associated with a first portion of the fleet data of a first type including operational history data, the weights including a second weight associated with a second portion of the fleet data of a second type different than the first type;
• determining, based in part on at least one selection enabling traversal of the decision tree, a maintenance action to resolve the maintenance notification, the at least one selection being received via user input to a user interface;
• and
• causing, via execution of a software update, automatic performance of the maintenance action.