Construction method of high-horsepower tractor digital twin

Abstract

Background: Agricultural machinery equipment is the core element of advanced agricultural productivity. The operation system of agricultural machinery equipment involves machine-human-environment-society interactions. Limited by the production mode, operation mode, etc., the design and development, manufacturing, operation and maintenance control, recycling and other links of agricultural machinery equipment are still independent of each other. The massive information in the agricultural production process has not been fully utilized, so there are some outstanding problems such as low operating performance, low production efficiency, and poor integration of agricultural machinery and agronomy. Methods: Focusing on the agricultural production process, this paper proposes the construction method for a high-horsepower tractor digital twin, and expounds on its operation mechanism. Taking high-horsepower tractor ploughing operation as an example, by deploying tractors, central servers, and an Internet of Things (IoT) platform, we developed a digital twin service platform for the agricultural production system of intelligent agricultural machinery equipment and built a tractor digital twin to verify the effectiveness of the proposed method. Results: The accuracy rate of the tractor ploughing quality prediction service based on this platform is 96.65%. Under open-loop control, the number of excellent, good, medium and poor sets of the tractor ploughing quality are 153, 955, 1470, and 1422, respectively. After adopting closed-loop control, the number of excellent and good sets increased by 378, and 821, respectively, and the number of medium and poor groups decreases by 119 and 1080, respectively. Through this platform, the operation quality can be effectively and accurately predicted and improved, which verifies the effectiveness of the proposed construction method of high-horsepower tractor digital twin. Conclusions: This research provides a method framework for the construction of the digital twin of tractor operation and maintenance control processes, and provides strong support for the vigorous development of intelligent agriculture.