DIGITAL TWIN SIMULATION SYSTEM AND A COGNITIVE INTELLIGENCE SYSTEM FOR VEHICLE FLEET MANAGEMENT AND EVALUATION
A system may include a digital twin management system to one of create, maintain, or render a digital twin based on received sensor data from one or more sensor systems associated with a vehicle. A system may include a digital twin simulation system to execute vehicle performance simulations using the digital twin by adjusting one or more vehicle performance parameters of the digital twin, and collect simulation outcome data resulting from the simulation. A system may include a cognitive process system to train machine learned models using vehicle performance simulation outcome data and makes predictions for providing decision support related to a simulated performance parameter to minimize a cost criterion associated with operating the vehicle.
1 . A digital twin system for vehicle fleet management to minimize a cost criterion, the digital twin system comprising:
a digital twin management system to one of: create, maintain, or render a digital twin based on received sensor data from one or more sensor systems associated with a vehicle;
a digital twin simulation system to execute vehicle performance simulations using the digital twin by adjusting one or more vehicle performance parameters of the digital twin, and by collecting simulation outcome data resulting from the vehicle performance simulations; and
a cognitive process system configured to train machine learned models using vehicle performance simulation outcome data and to make predictions for providing decision support related to a simulated performance parameter to minimize the cost criterion associated with operating the vehicle.
2 . The digital twin system of claim 1 , wherein the digital twin management system is further configured to update the digital twin in response to changes in the received sensor data from the one or more sensor systems.
3 . The digital twin system of claim 1 , wherein the digital twin simulation system further includes a user interface for displaying the simulation outcome data and receiving user input to adjust the one or more vehicle performance parameters.
4 . The digital twin system of claim 1 , wherein the cognitive process system further comprises a neural network configured to optimize the simulated performance parameter based on historical data and real-time data from the vehicle.
5 . The digital twin system of claim 1 , wherein the cost criterion includes at least one of fuel consumption, maintenance costs, or operational downtime.
6 . The digital twin system of claim 1 , wherein the digital twin simulation system is further configured to simulate vehicle performance under a variety of environmental conditions.
7 . The digital twin system of claim 1 , wherein the cognitive process system utilizes reinforcement learning to improve accuracy of the predictions over time.
8 . The digital twin system of claim 1 , wherein the digital twin management system is further configured to integrate data from external sources to update the digital twin.
9 . The digital twin system of claim 1 , wherein the digital twin simulation system is further configured to provide alerts when the simulation outcome data indicates a potential for exceeding the cost criterion.
10 . The digital twin system of claim 1 , wherein the cognitive process system is further configured to provide recommendations for scheduling vehicle maintenance based on the predictions.
11 . A method for employing a digital twin simulation system to minimize a cost criterion for a vehicle fleet, the method comprising:
simulating, at the digital twin simulation system, effects of evaluated states, the evaluated states including at least one of operational states, excess states, or shortage states related to the vehicle fleet;
collecting, at the digital twin simulation system, simulated values relevant to the evaluated states;
evaluating a performance characteristic of an affected digital twin of the digital twin simulation system until a convergence is achieved; and
providing the simulated values to a cognitive intelligence system configured to evaluate the simulated values and to determine potential actions and to evaluate respective effects of each of the potential actions for a respective desired fit to minimize the cost criterion for the vehicle fleet.
12 . The method of claim 11 , wherein the simulated values relevant to the evaluated states are collected from a plurality of sensors deployed in the vehicle fleet.
13 . The method of claim 11 , wherein the performance characteristic is evaluated based on real-time data received from the vehicle fleet.
14 . The method of claim 11 , wherein the cognitive intelligence system further comprises a neural network configured to process the simulated values to determine the potential actions.
15 . The method of claim 11 , wherein the cost criterion includes at least one of fuel consumption, maintenance costs, or operational downtime.
16 . The method of claim 11 , further comprising:
simulating, at the digital twin simulation system, vehicle performance under a variety of environmental conditions.
17 . The method of claim 11 , wherein the cognitive intelligence system utilizes reinforcement learning to improve decision-making over time.
18 . The method of claim 11 , wherein the cognitive intelligence system is further configured to integrate data from external sources to refine the evaluation of the simulated values.
19 . The method of claim 11 , wherein the digital twin simulation system provides alerts when the simulated values indicate a potential for exceeding the cost criterion.
20 . The method of claim 11 , wherein the cognitive intelligence system provides recommendations for scheduling vehicle maintenance based on the evaluated respective effects of each of the potential actions.