| CPC G01C 21/3484 (2013.01) [G01C 21/3438 (2013.01); G01C 21/3461 (2013.01); G01C 21/3617 (2013.01)] | 11 Claims |
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1. A computer implemented method, the method comprising:
receiving, by a processor, first object data associated with a first object to be transported to a final destination, wherein the final destination includes a plurality of different routes having one or more different weather conditions;
receiving vehicle data associated with a plurality of potential vehicles for transportation of the first object, wherein the vehicle data includes one or more different capabilities for each of the plurality of potential vehicles;
receiving context data associated with a context for the transportation of the first object;
maintaining historical data obtained from a previous transportation of the first object in a database, wherein the first object data, the vehicle data, and the context data are based at least in part on the historic data maintained in the database;
simulating an outcome of the transportation of the first object utilizing each of the plurality of potential vehicles using digital twin simulation based on the first object data, the vehicle data and the context data;
identifying a first vehicle and a second vehicle of the plurality of potential vehicles for transportation of the first object based on an optimization of an optimization factor associated with the outcome of the digital twin simulation;
determining a first transportation route portion, having a first particular weather condition for the first vehicle of the transportation of the first object based on at least one capability of the one or more different capabilities of the first vehicle and the first particular weather condition, wherein the first transportation route portion ends at a partial destination of the final destination of the first object; and
determining a second transportation route portion having a second particular weather condition for a second vehicle of the transportation of the first object based on at least one capability of the one or more different capabilities of the second vehicle and the second particular weather condition, wherein the second transportation route portion includes a route from an originating location of the second vehicle to the partial destination and from the partial destination to the final destination, wherein the digital twin simulation uses machine learning to simulate a respective digital twin of each of the plurality of potential vehicles, wherein each of the respective digital twins represents a virtual representation of each of the plurality of potential vehicles, wherein the processor is located in a cloud node of a cloud computing environment comprising a plurality of cloud computing nodes and generates the optimization factor associated with the outcome of the digital twin simulation, wherein the processor generates the optimization factor associated with the outcome of the digital twin simulation includes:
analyzing the digital twin simulation for one or more transportation impacts; and
selecting at least one of the one or more transportation impacts on which to base the optimization factor, wherein the optimization factor is selected from at least one of:
transportation cost, transportation time, and damage to vehicle,
and further comprising:
sending, by the processor located in the cloud node of the cloud computing environment, a command to a processor of the first vehicle, wherein the processor of the first vehicle controls timing and route of the first vehicle; and
scheduling, based on the command, transportation of the first object by the first vehicle.
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