Design and Implementation of an Early Warning System Based on the Risk Measurement Model

Abstract

In recent years, there are still some defects in the risk management of China’s inspection and quarantine work, such as the omission of major factors in the conformity assessment of inbound and outbound commodities and the subjective and unscientific assignment of risk indicators, which affect the ability of risk monitoring. Therefore, this paper constructs an inspection and quarantine risk measurement model based on random forest algorithm, and develops a risk early warning system for inspection and quarantine business. Firstly, according to the target requirements, the training data set is extracted from the original rough data set, and the overall data is cleaned and modified. Secondly, the integrated machine learning model is used to select the feature values with better prediction ability. Then, the risk measurement model is constructed based on random forest algorithm and deployed in multi-model parallel mode. Finally, a complete risk early warning system is developed based on the risk prediction model. Through the operation of this system, the abilities of risk analysis and discrimination for inspection and quarantine have been greatly improved, and the comprehensiveness and accuracy of risk prevention and control have been effectively guaranteed.