Method and system for determining working time period of agricultural machine based on clustering algorithm

Abstract

The invention discloses a method and system for determining the working time period of an agricultural machine based on a clustering algorithm. The method comprises the steps of collecting longitude and latitude data of agricultural machine movement and recording the collection time; preprocessing the longitude and latitude data to generate a data set which can be directly used for clustering operation; performing clustering operation on the data in the data set to obtain a preliminary clustering result, wherein the preliminary clustering result comprises a road data point set and a field area data point set; correcting the field data point set to obtain a final clustering result; screening out a data point set of the field area from the final clustering result, and calculating a minimum value and a maximum value of sampling time in the data point set of each field area so as to determine a start time period of the agricultural machinery; according to the method, few types of data are acquired, only time and latitude and longitude coordinate data of agricultural equipment need to be acquired, and in practical application, the running speed and efficiency of a program can be effectively improved while problems are solved, so that a large amount of data can be processed within limited time.

Description

technical field [0001] The invention relates to the technical field of agricultural production management, in particular to a method and system for determining the working time period of agricultural machinery based on a clustering algorithm. Background technique [0002] The judgment of the working status of agricultural machinery and the statistics of working hours are common tasks in agricultural production. For agricultural machinery, working hours are an important basis for judging the actual work measurement and whether maintenance is required; in addition, the country is vigorously promoting the development of smart agriculture. The statistics of the working hours of agricultural machinery and equipment are an important basis for agricultural machinery subsidies. [0003] At present, the methods for judging the working status of agricultural machinery and recording working hours mainly include manually recording the corresponding time when the equipment starts and end...

AI Technical Summary

Problems solved by technology

[0003] At present, the methods for judging the working status of agricultural machinery and recording working hours mainly include manually recording the corresponding time when the equipment starts and ends work and using equipment sensors for timing; The problem of lack of efficiency and low accuracy; and the use of sensor timing requires the installation of sensors on the equipment, but some earlier equipment does not have the corresponding hardware conditions

[0004] With the development of technology, there is currently a method to calculate the working time of agricultural equipment by using GPS trajectory, and there is a method of clustering the trajectory of agricultural machinery to calculate the working time of agricultural machinery, but it only uses the clustering points as the working status and working time of agricultural machinery. In the calculation of the duration, the situation of staying in place or turning in a small range after the equipment is started is not considered, and there are problems of easy misjudgment and low accuracy; although the patent CN112905576A has various misjudgments of the track at the junction of roads and fields Discussions and solutions have been conducted to solve the problem of program misjudgment. However, most of these methods are suitable for counting acres of farmland or distinguishing fields and roads. Defects of slow collection and program running speed and low efficiency

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