US 2026/0289448 A1 · SYSTEMS AND METHODS OF DEPLOYING NEURAL NETWORKS FOR CREW ALLOCATIONS

Abstract

In some embodiments, systems and methods for preplacing crews in anticipation of at least one outage ticket based on an objective can aid in reducing downtime from the outage. In some embodiments, such preplacement strategies leverage historical outage data, weather forecasting, and machine learning-based predictions to position crews at optimal locations before outages occur. In some embodiments, this proactive approach can minimize response times and improve overall restoration efficiency. In some embodiments, systems and methods for determining real-time crew dispatch for at least one crew in response to at least one outage ticket can also aid in reducing downtime from the outage. In some embodiments, real-time dispatch systems dynamically assign crews based on outage prioritization, crew availability, and predicted outage types, enabling adaptive resource allocation as conditions evolve during an outage event.

Claims20

Dependent claims

A system for determining a crew preplacement for a crew in response to an outage ticket based on an objective, the system comprising:

a computing system comprising:
a non-transitory memory storing a set of executable instructions;
and
a processor coupled to said non-transitory memory, wherein said processor executes said set of executable instructions to determine said crew preplacement by
acquiring and analyzing historical outage data associated with a geographical region and an outage type;
computing a machine learning-based metric and an outage prediction associated with said geographical region;
generating an initial crew preplacement suggestion based on said machine learning-based metric and said outage prediction associated with said geographical region;
acquiring a first user input accepting said initial crew preplacement suggestion;
and
terminating the crew preplacement if acceptance of said initial crew preplacement suggestion is acquired.

7 more elements

A system for determining a real-time crew dispatch for a crew in response to an outage ticket, the system comprising:

a portable tracking device carried by said crew,
wherein said portable tracking device comprises a wireless communication interface configured to transmit outage data;
a computing system comprising:
a non-transitory memory storing a set of executable instructions;
a transceiving module connecting with said wireless communication interface in said portable tracking device;
and
a processor coupled to said transceiving module and said non-transitory memory, wherein said processor, executes said set of executable instructions determining said real-time crew dispatch by:
acquiring and analyzing an outage data associated said outage ticket, wherein said outage data includes an outage location and a customer count;
sorting and prioritizing said outage ticket based on said customer count;
acquiring a historical outage data to calculate a probability of an outage type associated with said outage location associated with said outage ticket;
using a machine learning model to iteratively assign a crew type with said outage ticket based on a prioritization and an objective;
dispatching said crew type based on the probability of requiring said crew type;
comparing the probability of requiring said crew type with a first predetermined threshold;
and
assigning said crew type to said outage location if the probability of requiring said crew type is greater than said first predetermined threshold.

12 more elements

Dependencies are inferred from the claim text.

Systems and methods of deploying neural networks for crew allocations

Life of this patent

Systems and methods of deploying neural networks for crew allocations

Life of this patent

Abstract

Claims20