Design of a Digital Twin Platform Based on Distributed Computing and Resource Optimization Algorithms

The digital twin platform builds a twin model database through data aggregation, achieving centralized storage and management of multi-dimensional, cross professional, and format normalized twin data. By associating various business and perception data and mapping relationships in the data center through the unique identification code entity ID of the model, it serves the reuse of twin data in the later stage. The research results indicate that when the resource pool or queue settings are unreasonable, some pools or queues may have idle resources, while others may be resource saturated, resulting in wasted resources and longer runtime; Reasonable configuration will greatly reduce the completion time of homework. The running time of the algorithm in this article is shorter than that of the first in, first out scheduling algorithm and the naive Bayesian classification scheduling algorithm. It can be seen that the algorithm in this article is better than both of them. The distributed computing digital twin platform needs to dynamically divide different computing domains based on the different characteristics of online and offline application functions, and develop different computing resource allocation plans to shorten the computing cycle of offline application functions as much as possible.