Transformation of reverse marketing model for electrical appliances based on Markov chain optimization with information technology support

Abstract

Inverters are widely used in the military, industrial production and defense fields as current conversion devices that convert direct current to alternating current. If the inverter fails, it can cause damage to other equipment, resulting in financial losses and, in extreme cases, compromising the safety of users. In this study, by integrating neural networks, the input signals of inverters are quickly converted to Fourier spectrum amplitudes, and from fault signals (such as load phase voltage) to feature vectors. In order to realize automatic extraction and fault detection of inverters, an optimization method is used to determine the appropriate number of nodes in the hidden layer of complex neural networks. The ability to efficiently allocate limited computing, storage, and network resources to meet user demand for services; Continuously optimize quality of service (QoS), including reducing latency, improving bandwidth, and increasing reliability. These problems directly affect the performance and user experience of MEC systems. By studying these issues and proposing corresponding solutions, we aim to improve the performance of MEC systems and provide higher quality services. The accuracy of defect diagnosis can reach higher than 99%, and the method has a high remission rate, demonstrating its effectiveness and benefits.