Research On Early Warning Of Bulk Cargo Port Car Lifting Risk

As a key supporting force for domestic and foreign logistics,the development of the port has received much attention.With the increasing competition in the world and the development of information technology,ports are now gradually becoming more intelligent and refined.At present,both the country and the port attach great importance to the intelligent transformation of ports.According to the way of transporting goods,ports can be divided into bulk cargo ports and container ports.Compared with container ports,the types and shapes of bulk cargo ports are uncertain,the overall operation process is relatively extensive,and the degree of intelligence is low,which requires urgent transformation.The wisdom of the port is a rich concept,involving all aspects of the port.This article takes the risk management of the process of picking up automobiles in the bulk cargo port as an entry point to study the intelligent construction of the port.The complexity of the bulk cargo port environment and other factors make the delivery process face multiple risks.Good risk management is not only conducive to avoiding risks,avoiding damage to the port economy and reputation,but also improving the competitiveness of ports.The reason for choosing this process is that the delivery of goods is a frequent and important process at the port,and the second is that the delivery of cars is the most common of all methods.This paper introduces the machine learning method to study the auto pickup risk of bulk cargo ports,hoping to provide a certain reference for the risk management methods of bulk cargo ports and subsequent research.The specific content includes the following aspects:(1)By reading the relevant literature,sorting out the existing processes,and communicating with the field staff,analyze the risks faced by the bulk cargo port car pickup.Summarize the risks faced as consignor behavior risks and theft risk.(2)Analyze the two risks separately,clarify the reasons for them,and study them accordingly.For consignor behavior risk,research and analysis are performed through clustering methods in machine learning;for stolen risk,based on the imbalance characteristics of the data set,the problem is transformed into anomaly detection problems,and a single classification algorithm is used for research.(3)Design and develop an early warning system for delivery risks,and use the results of risk analysis in the field to improve the intelligence level of the port,protect the reputation and interests of the port from damage,and improve the competitiveness of the port.In this paper,the machine learning model is applied to the early warning of cargo pickup risks for bulk cargo ports,which provides new ideas for the intelligent construction of ports and enriches related research.