Multi-dimensional early warning of the entire supply chain of power materials based on RFID technology

Abstract

Early warning system needs to process a large amount of real-time data, and carry out in-depth analysis and mining of these data to identify potential risks and hidden dangers. However, existing data processing and analysis capabilities may not be able to meet this demand. To this end, a multi-dimensional early warning of the entire supply chain of power materials based on RFID technology is designed. According to the real-time failure probability of power materials, the probability of early warning accidents is calculated and classified, and the risk is graded based on these probabilities. Through the methods of bonus points, deduction points and grade evaluation, the risk early warning indicators of different stages in the whole supply chain of power materials were quantified. The objective and rationality of risk assessment can be ensured by means of comprehensive weight. A multi-dimensional early warning system based on RFID technology is established, combining multiple linear regression model and particle swarm optimization algorithm to determine the time window of multi-dimensional early warning, and carry out dynamic monitoring and early warning of supply chain. The experimental results show that the early warning effect of the design method can reach 95% and the highest early warning effect can reach 98% at 10 seconds. The average warning error is only 2.91%, and the average warning time is only 1.34 seconds, which is more accurate in identifying the number of first-level risks, second-level risks and third-level risks.