Low-carbon logistics distribution system and method based on machine learning and interference management

Abstract

The invention discloses a low-carbon logistics distribution management system and method based on machine learning and interference management, and relates to the technical field of machine learning and internet application management. The enterprise-level user side comprises a login management module, a client management module, a vehicle management module and a scheduling management module. According to the invention, an improved quantum ant colony algorithm adjustment strategy is adopted, so that the interference scheduling plan deviation of the logistics distribution system is minimum and the interference event is timely and accurately processed. The improved quantum ant colony algorithm provided by the invention improves the convergence speed of the optimal solution, increases the search range of the global optimal solution, avoids the premature phenomenon of the algorithm, reduces the operation time and cost of the low-carbon logistics distribution system, improves the operation efficiency of the system, and shortens the response time.

Description

technical field [0001] The invention relates to the technical field of machine learning and Internet application management, in particular to a low-carbon logistics distribution management system and method based on machine learning and interference management. Background technique [0002] With the development of machine learning technology and intelligent logistics technology, more and more low-carbon logistics distribution systems based on improved intelligent algorithms have emerged, which has brought rapid development to logistics companies. For example, intelligent unmanned distribution systems based on machine learning, logistics drone distribution systems, and intelligent distribution and scheduling systems. In these application systems, as the actual production process will face more and more sudden interference events from internal and external sources, how to carry out scientific and effective interference management, quickly respond and deal with these sudden int...

AI Technical Summary

Problems solved by technology

[0005] 1. Under the assumption of "complete rationality" in the existing system, the dispatch vehicle interference scheduling system that considers human behavior characteristics cannot meet the needs of actual production today;

[0006] 2. There is a lack of a scientific and efficient interference management model for different types of interference events in actual distribution;

[0007] 3. The distribution plan generated by the existing system deviates greatly from the original distribution plan, which increases the working hours of workers and reduces the interests of the enterprise

Images