Internet-of-things engineering vehicle monitoring and scheduling method, server and vehicle system

Abstract

The invention provides an Internet of Things engineering vehicle monitoring and scheduling method, a server and a vehicle system, and the method comprises the steps: dividing a moving path of a vehicle into a plurality of regions, and storing the latitude and longitude coordinate information corresponding to each region into a database; assigning a unique number to all the vehicles, and randomly arranging each vehicle to each region, so that each vehicle shuttles back and forth in each region according to a set moving path; collecting the latitude and longitude information of the vehicle i intermittently, calculating the distance between the vehicle i and each area, and screening an area j closest to the vehicle i out; and judging whether the vehicle i is in the region j according to the distance between the vehicle i and the region j. The moving path of the vehicle is divided into a plurality of areas, then the distance between the vehicle and each area is calculated to judge whetherthe vehicle is in a certain area or not, then whether the vehicle moves on the set moving path or not can be further judged, and therefore real-time monitoring of the vehicle is achieved. The method is applied to the field of industrial Internet of Things.

Description

technical field [0001] The invention relates to the field of industrial internet of things, in particular to a method for monitoring and dispatching engineering vehicles of the internet of things, a server and a vehicle system. Background technique [0002] The comprehensive operation and scheduling management of the construction machinery industry is the integrated management and deployment of the resources involved in the industry, such as personnel, finance, and vehicles. With the rapid development of national infrastructure construction, the number of users and user management of the construction machinery industry The number of vehicles is increasing dramatically. This has brought increasingly serious pressure to the scheduling management of construction machinery. Because the internal operation scheduling and vehicle management of traditional construction machinery units rely on manual operation methods for control, sometimes some construction machinery drivers deviat...

AI Technical Summary

Problems solved by technology

The defects of the above scheme mainly include poor fault tolerance, and the latitude and longitude information occasionally has large deviations. When the latitude and longitude information is inaccurate, false alarms will be generated.

Judging by street name or driving time is easily affected by map street information and vehicle conditions, and may also cause false alarms; it has high requirements for computer performance, and vehicle latitude and longitude information is collected in real time, and synchronously calculates whether the coordinate points are on the set track. On the other hand, when the collection frequency is relatively high, it needs to consume a lot of computer performance to do yaw detection

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