Implementation And Application Of Multi Vehicle Command And Scheduling Engine

With the development of the times and changes in economic growth patterns,the overall pattern of consumption will undergo significant changes.From a macro perspective,the factors driving economic development will gradually shift from investment to consumption,and on the other hand,the quality of life will continue to improve.Consumer behavior will shift from the general pursuit of solving food and clothing problems to the pursuit of diversification and personalization of consumer market value.The modern logistics industry focuses on the terminal needs of merchants and consumers,puts people first,and seeks to "deliver goods or services to customers at the right time,at a reasonable total price,and in the right way".In response to changes in the consumer market,the transformation of demand has been further accelerated,providing strong support for expanding domestic demand.For express logistics enterprises,the quality of information systems directly determines the production efficiency and service quality of the enterprise.At present,the digitization level of many logistics centers in our country is relatively low,mainly due to decision-making issues,such as the selection of logistics center locations,optimization of transportation routes,and inventory management,which are all in a semi manual state.The development progress of information management systems is lagging behind,and technical support is not guaranteed.Through project research,the feasibility of implementing unmanned platforms for material transportation is explored,laying the foundation for large-scale and standardized use of unmanned intelligent transportation.This paper designs and implements a multi-vehicle command and dispatch engine based on data fusion technology.The main tasks are as follows:(1)Command and dispatch can be divided into two parts: material dispatch and vehicle dispatch.Material dispatch involves allocating suitable vehicles for the transportation of required materials.If the current station does not have enough materials,vehicles can be arranged to load materials from nearby stations that have sufficient supplies.Vehicle dispatch,on the other hand,considers the existing routes for different types of vehicles and evaluates three key performance indicators(KPIs)-"economy," "speed," and "safety"-to select the optimal vehicle for transportation.(2)Route planning consists of map data parsing,storage,and path planning algorithms for different vehicles.Since map data is generally in vector format and cannot be directly read by code,GIS software is used to convert the source map data into a readable format for program code and store it in a specific way for easy access.Vehicles include autonomous cars,drones,unmanned boats,and trains,which can be categorized into guided transportation(autonomous cars,trains)and unguided transportation(drones,unmanned boats).Guided transportation uses the Dijkstra algorithm combined with road and railway information from the map for route planning,while unguided transportation uses the A~* algorithm combined with administrative boundary information and obstacle data from the map for route planning.(3)Based on three scenarios(task coordination planning,simulation decomposition,dynamic command and dispatch),feasible solutions are obtained considering different KPI indicators(speed,economy,safety).The output results are in JSON format,and each solution contains detailed route information,scheme information,capacity allocation information,and task decomposition information,which facilitate subsequent visualization of the results.