Digital Twin Decision Training for Dynamic Physical Environments
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Solution Overview
Problem
Devices such as robots and self-driving vehicles face challenges in making accurate decisions in dynamic real scenarios due to the complexity and constant change of their environments, leading to potential losses from incorrect decisions.
Innovation Solution
A decision method involving the construction of multiple physical simulation environments based on diverse data adjustments, using a digital twin device for training, and employing multi-channel parallel simulation to improve decision accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single physical simulation environment is constructed based on a single real physical environment, then the simulation process is simple, but the decision accuracy in dynamic real scenarios is insufficient
Solution Approach 1:
The patent segments the simulation environment into multiple physical simulation environments, each representing different scenarios or conditions. This allows the system to test decision-making capabilities across diverse situations rather than relying on a single environment, thereby improving decision accuracy without requiring complete redesign of the simulation framework
Solution Approach 2:
The patent employs environment data adjustment policies to modify parameters such as scene layout, object positions, weather conditions, and temporal variations. By systematically changing these parameters across multiple simulation environments, the system captures the complexity of real-world dynamics while maintaining a manageable simulation structure
2Reliability
If a large quantity of real physical environments are collected for simulation, then the decision model accuracy is improved, but the time and difficulty of data collection increase
Solution Approach 1:
The patent creates virtual copies of real physical environments through digital twin technology. These digital twins replicate the essential features and dynamics of real-world scenarios without requiring actual physical data collection. The system generates synthetic training data from these digital twins, significantly reducing the time and effort needed to collect real environment data while maintaining model accuracy
Solution Approach 2:
The patent performs preliminary construction of digital twin environments before actual decision-making tasks. By pre-generating diverse simulation scenarios and training data in advance, the system prepares comprehensive training materials that can be quickly applied to real-world decisions, eliminating the need for time-consuming real-time data collection during actual operations
3Productivity
If traditional simulation methods are used, then the simulation process is straightforward, but the training efficiency and decision-making speed are insufficient
Solution Approach 1:
The patent implements dynamic simulation environments that can adapt and evolve based on training progress and real-world conditions. The system dynamically adjusts simulation parameters, scene configurations, and training objectives to optimize learning efficiency. This dynamic approach enables faster training convergence and more effective decision-making models without requiring overly complex static simulation frameworks
Solution Approach 2:
The patent designs a universal simulation platform that serves multiple functions: environment generation, data synthesis, model training, and performance evaluation. This multi-functional system consolidates what would otherwise require separate complex systems into a unified framework, improving training efficiency while managing overall system complexity through integration
Data Source
AI summary
This disclosure provides a decision method and a related device. The decision method includes: obtaining first feature information of a historical real physical environment, where the first feature information includes environment data in a plurality of dimensions; constructing a plurality of physical simulation environments based on the first feature information; obtaining second feature information of an electronic device located in the historical real physical environment, where the second feature information includes body status data in a plurality of dimensions; constructing a digital twin device of the electronic device in a simulation system based on the second feature information; obtaining a decision model based on simulation results of the digital twin device in the plurality of physical simulation environments; and feeding back the decision model to the electronic device.