Farmland operating area measuring and calculating method based on GPS positioning data of agricultural machinery

Abstract

The invention discloses a farmland operating area measuring and calculating method based on GPS positioning data of agricultural machinery. Effective and accurate area measurement can be performed onthe farmland operating area, and a re-farming removal effect is achieved. The method comprises the following steps: during farmland operation, obtaining, by a GPS positioning terminal of the agricultural machinery, GPS positioning information of the agricultural machinery after every set time, transmitting the GPS positioning information to a background data storage center, after the farmland operation is completed, selecting GPS positioning points within the set time period, performing random interpolation on a rectangular cultivated land area between the adjacent GPS positioning points, andperforming spectral clustering on all GPS positioning points after the random interpolation to obtain at least two block regions of first time clustering; performing spectral clustering on each blockregion of first time clustering to obtain at least two block regions of second time clustering; for each block region of second time clustering, removing a blank region without the GPS positioning point, and removing cultivated land inflection points to obtain final block regions; and using the total sum of areas corresponding to all final block regions as a final operating area.

Description

technical field [0001] The invention relates to the technical field of agricultural machinery data analysis, in particular to a method for measuring and calculating farmland operation area based on agricultural machinery GPS positioning data. Background technique [0002] As rural land becomes more and more concentrated, large farmland provides favorable conditions for mechanized farming. Mechanized and intelligent modern agriculture is constantly developing. Under the development trend of precision agriculture, the measurement of agricultural machinery operation area can count crops on the one hand. Yield, on the other hand, is also the basis for charging for public agricultural machinery operations, so efficient and accurate methods for calculating the operating area are urgently needed. [0003] The existing methods for measuring and calculating the working area of ​​agricultural machinery mainly have the problem of not being able to effectively solve the problem of calcu...

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Problems solved by technology

[0004] The method of using manpower to measure and rely on manual hand-held operating area measurement equipment to measure is not only inefficient but also increases unnecessary labor costs; according to the method of measuring the path of agricultural machinery, the length of the path traveled by the agricultural machinery is multiplied by the width of the farm tool To calculate the operating area, this method has the problem of heavy plowing, and the calculated operating area is larger than the actual operating area.

[0005] The polygonal area boundary modeling method collects n vertices of the polygonal farmland operation area boundary, establishes the upper boundary function model and the lower boundary function model to complete the boundary modeling, and then calculates the area between the upper and lower boundary functions. The accuracy requirement is high, the positioning of the boundary vertices is not accurate, and the result of the operation area measurement is also inaccurate. At the same time, this method does not effectively solve the problem of re-cultivation

[0006] The cell processing method is to divide the working area into many rectangles according to the GPS positioning data, and then calculate the area of ​​the rectangles, and at the same time calculate the area of ​​the overlapping parts. However, because the directions of each rectangle are different, each overlapping part is an irregular shape. Due to the problem of GPS positioning offset, it is difficult to determine the position of the overlapping part

[0007] The spatial grid subdivision method mainly uses machine learning methods to divide the cultivated land area, such as using clustering algorithms to cluster and block the area, but the existing methods have not applied this method to solve the problem of re-cultivation, and due to the The path is irregular, resulting in many blank areas without anchor points in the clustered area, so the direct application of the clustering method and its improved method cannot effectively divide the area into blocks

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