Digital Twin Simulation for Context-Aware Vehicle Selection
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Solution Overview
Problem
Existing systems lack an efficient method for optimizing the transportation of objects by selecting the most suitable vehicle based on real-time data and contextual factors, leading to suboptimal delivery times, costs, and potential damage to vehicles or objects.
Innovation Solution
A digital twin simulation system that processes object, vehicle, and contextual data to simulate transportation scenarios, allowing the selection of the most optimal vehicle based on an optimization factor, which can include delivery time, cost, environmental impact, and vehicle wear.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional transportation selection methods are used, then the process is simple and quick, but the delivery time, cost, and vehicle wear are suboptimal
Solution Approach 1:
The system performs preliminary simulation of transportation scenarios before actual delivery occurs. By pre-simulating multiple vehicle options with their respective outcomes (delivery time, cost, wear), the system enables optimized selection without adding complexity to the actual delivery execution. The simulation happens in advance, allowing quick and informed decision-making.
Solution Approach 2:
The patent creates a digital twin (virtual copy) of the transportation system and vehicles to simulate outcomes. Instead of physically testing each vehicle option, the system copies the essential characteristics of vehicles and transportation contexts into a virtual model, allowing rapid simulation of delivery scenarios without physical trial-and-error, thus improving productivity without proportionally increasing physical complexity.
2Reliability
If comprehensive vehicle options are considered, then the optimization of delivery outcomes improves, but the time and computational resources required increase
Solution Approach 1:
The system creates a virtual copy (digital twin) of the transportation scenario including vehicle characteristics, route conditions, and delivery requirements. This virtual model allows rapid simulation of multiple vehicle options without physically evaluating each one, enabling comprehensive optimization of delivery outcomes while minimizing actual selection time through pre-computed simulation results.
Solution Approach 2:
The system varies key parameters (vehicle type, route, timing) within the simulation to generate multiple potential outcomes. By systematically changing these parameters in the virtual model, the system evaluates numerous vehicle options quickly and selects the optimal combination, improving reliability without proportionally increasing real-time computational burden.
3Measurement precision
If real-time data processing is implemented, then the accuracy of vehicle selection improves, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent implements a digital twin that copies essential real-time data characteristics into a virtual model. This allows the system to process and analyze current transportation data (vehicle status, route conditions, delivery requirements) through simulation without building a complex real-time processing infrastructure. The virtual model processes data in a simplified manner, maintaining selection accuracy while reducing system complexity.
Data Source
AI summary
A processor may receive first object data associated with a first object to be transported. The processor may receive vehicle data associated with one or more potential vehicles for transportation of the first object. The processor may receive context data associated with a context for the transportation of the first object. The processor may simulate the transportation of the first object utilizing each of the one or more potential vehicles using digital twin simulation. The processor may select a first vehicle of the one or more potential vehicles based on an optimization of an optimization factor associated with an outcome of the digital twin simulation.