Abstract: The present invention belongs to the field of the digital twin, and relates to a digital twin framework of weld joint fatigue based on a structural stress method. The framework is divided into an off-line stage and an on-line stage, wherein the off-line stage comprises establishing a finite element model, calculating equivalent structural stress, and training an artificial intelligence algorithm; and the on-line stage comprises reading sensor data, predicted by the artificial intelligence algorithm, counting by a rainflow counting method and calculating remaining life by cumulative damage. The framework combines five methods, i.e. a finite element method, a structural stress method, the artificial intelligence algorithm, an upper envelope method, the rainflow counting method, and a Miner linear cumulative damage method.

Abstract: The present invention belongs to the technical field of signal processing, and relates to an online fast processing method for real-time data based on edge computing. In the present invention, a dynamic online de-noising method is adopted to remove noise contained in speeds to ensure the effectiveness and accuracy of de-noising results; for the displacement integrated online, an efficient method is adopted for dynamic online de-noising to further reduce the effectiveness of drift in the displacement value on final integration results; and under the condition of ensuring the accuracy of an integration method, an integration algorithm is embedded into an edge device to realize fast calculation and analysis of data near a data source and realize dynamic fast integration of online signals based on edge computing, which provides effective references for efficient processing and calculation of data.

Abstract: A construction method of digital twin for structure performance of an intelligent excavator. Through the finite element analysis on key parts in the process of the intelligent excavator, the relevant structural mechanics performance is obtained; The important operating states of the key parts of the intelligent excavator in the excavation process are collected, and the key operating data are obtained through data processing and calculation; sensor data and an artificial intelligence algorithm are fused, and the structure performance of the parts of the intelligent excavator under multiple unknown working conditions is predicated by using a prediction model; and finally, the performance data information is modeled and rendered by computer graphics technology, to obtain a digital twin of the structure performance display of the intelligent excavator is obtained, thereby realizing digital twin mapping of the performance information on key parts of the intelligent excavator in the excavation process.