| The vigorous development of the Internet of things technology provides a good opportunity for the upgrading of the storage and logistics industry,and also points out the direction of intelligent development.As the link between goods and warehouse,forklift not only undertakes the loading and unloading of goods,but also ensures the task quality and operation safety,which is an important part of the logistics supply chain system.However,in the use process,the traditional supervision mode is still used,and there are many problems about the information acquisition,processing and application ability of forklift equipment,and the modern information management system has not been applied yet.Therefore,the research and design of the supervision system suitable for forklift equipment is of great significance to improve the work efficiency and reduce the management cost of storage and logistics enterprises.The subject comes from the research project of school enterprise cooperation.Aiming at the problems existing in the use and management of forklift equipment and its important position in the logistics transportation,this paper puts forward the supervision system based on the Internet of things on the premise of studying the existing Internet of things technology,task optimization algorithm and Web framework,which provides a new solution for the remote supervision of forklift equipment.The remote monitoring platform receives the real-time operation status data sent by the forklift intelligent terminal,realizes the remote monitoring,and optimizes the scheduling scheme of forklift equipment through the real-time status and task information,so as to improve the intelligent degree of the whole cargo circulation process.Based on the real-time data collected by the Internet of things technology,this paper studies the task optimization and scheduling of forklift trucks,and designs a remote monitoring platform.The main research and work contents are as follows:(1)According to the actual work scenario of the enterprise,the task scheduling optimization problem of forklift is studied.The data information of forklift in the process of storage and transportation is obtained through the Internet of things technology,and the mathematical model of forklift task scheduling with the constraints of working time and time window is established.Based on the study of existing task scheduling algorithms,a task scheduling strategy based on the Internet of things is proposed.In the initial stage,an improved genetic algorithm with Multi-Strategy-Selection and population-expansion-mechanism is adopted,and adaptive crossover and mutation rate are selected.In the dynamic scheduling stage,priority settings are made for dynamic tasks,neighborhood tasks and task groups to be executed,and a heuristic is designed based on greedy idea Solution algorithm.An example is designed and simulated to verify the effectiveness of the scheduling strategy.(2)The design scheme of the remote supervision platform based on Django framework is detailed,including the design and deployment of Web middle layer and data service layer.Among them,Django web framework and the dual web server mode of Nginx and uWSGI are used to complete the construction of the B/S architecture supervision platform,and mqtt protocol is used to realize the data link communication between the on-site vehicle terminal and the supervision platform.Through the analysis of system requirements and data attributes,the database is designed,and the user management,real-time monitoring,task scheduling,historical data query,equipment management and other functional modules are designed and implemented,and the corresponding Web interface is designed and implemented based on the Bootstrap framework.(3)Test the function and performance of the remote monitoring platform software to verify whether the indicators of the platform meet the expectations of R&D.The test results show that the remote monitoring platform designed in this paper meets the needs of users. |
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