In view of the poor supervision effect of the traditional monitoring cloud supervision system, this paper puts forward a design method of Intelligent Transportation Security Cloud supervision system based on the Internet of vehicles technology, uses the tsed-01 sensor chip to optimize the hardware configuration of the cloud supervision system, perfects the software functions based on the Internet of vehicles technology, and relies on the Internet of vehicles communication platform and cloud data sharing equipment to optimize the software functions of the cloud supervision system, identify and manage the heterogeneous data sources generated by different modules in the cloud supervision system to simplify the steps of the cloud supervision system and provide data support for the comprehensive decision-making of traffic management. The experimental results show that the intelligent traffic safety cloud supervision system based on the Internet of vehicles technology has good practicability, and has guiding significance for the construction of urban rail transit monitoring cloud supervision systems in the future.

Aiming at the problem that the extraction effect of abnormal vibration characteristics of current engineering electric drive construction machinery is poor, an automatic detection method of abnormal vibration of engineering electric drive construction machinery is proposed. Firstly, the abnormal data of mechanical abnormal vibration are collected and identified, and based on the identification results, the dynamic characteristic model of engineering electric drive construction machinery is constructed. The empirical mode decomposition and Hilbert spectrum are used to decompose the abnormal vibration of machinery, calculate the response amplitude and time lag value generated by the operation of the engineering electric drive construction machinery to simplify the diagnosis steps of the abnormal vibration of the engineering electric drive construction machinery and realize the positioning and detection of the transverse and torsional vibration characteristics. Finally, through experiments, it was confirmed that the automatic detection method of the abnormal vibration of the engineering electric drive construction machinery has high accuracy, which can better ensure the healthy operation of mechanical equipment. This endeavor aims to establish scientific methodologies and standards for fault detection techniques in construction machinery, ultimately forging a versatile solution better suited for detecting and resolving issues across various categories of construction equipment.