Intelligent agricultural machine optimization mu-counting method based on internet of things

Abstract

The present invention relates to an intelligent agricultural machine optimization mu-counting method based on the internet of things. A lower computer collection mu-counting data is equal to the effective work time multiplied by the real-time vehicle speed multiplied by the agricultural machine work effective width and is calculated; the number of mu areas of the upper computer is equal to the difference of an agricultural machine treading track route and a twisted dragon stop track route multiplied by the agricultural machine work effective width, and the GPS mu-counting data is calculated; and the upper computer compares the lower computer collection mu-counting data and the GPS mu-counting data to record a server. The intelligent agricultural machine optimization area-metering method based on internet of things employs the lower computer to collect and calculate the area-metering data, and the upper computer employs the number of the mu areas reported by the lower computer as a main basis and corrects the obtained correction mu-counting data according to the GPS, and therefore the mu-counting precision is high and the drift error of the GPS location points is small. Besides, the lower machine collection mu-counting data is used for correction of the GPS mu-counting data calculated by the upper computer so as to avoid the conditions of the work track overlapping or the polygonon self intersection formed by the work tracks.

Description

technical field [0001] The invention relates to the technical field of agricultural machinery, in particular to an optimal mu calculation method for intelligent agricultural machinery based on the Internet of Things. Background technique [0002] my country is a large agricultural country, but most agricultural production is basically completed by human labor. For example, in most rural areas, operations such as sowing, fertilization, irrigation and harvesting mainly rely on pure human labor, which is not only inefficient, but also affects the growth and harvest of crops. Even if mechanized sowing and harvesting are realized in some places, human cooperation is usually required, and mechanized operations such as sowing and harvesting are independent of each other and are not reasonably connected, which also affects the efficiency and production cycle of agricultural machinery operations. In addition, the timing and amount of agricultural irrigation are basically judged by hu...

AI Technical Summary

Problems solved by technology

The tortuous process of agricultural machinery in the field is complicated, and because GPS positioning is obtained by measuring the transmission delay of satellite signals, while the encoding rate of civil satellite signals is low, and the edges of civilian satellite signals are fuzzy, delay errors will be introduced to affect positioning precision

In addition, satellite signal reflection, satellite distribution, and errors in GPS positioning iterative algorithms will also cause GPS signal drift, and interference will also affect the quality of GPS signals and increase errors, such as clutter interference in the same frequency band, GPS local clock jitter, intelligent agricultural machinery Drastic temperature changes and jitter of the GPS module will cause large errors in the number of acres counted by intelligent agricultural machinery

[0005] So purely relying on the host computer to realize the function of counting mu through GPS positioning, the calculation of mu is complicated and the accuracy is not high.

However, purely relying on the lower computer to upload mu data and upload data through the GPS module, it is inevitable that the GPS signal drift of the mu collected data will cause the mu data to be inaccurate

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