Agricultural Machine State Detection Using GNSS State Vectors
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Solution Overview
Problem
Farmers face challenges in complying with regulations and optimizing agricultural machine operations to reduce costs and minimize breakdowns, while manufacturers aim to design machinery that meets farmer needs efficiently and cost-effectively.
Innovation Solution
A method utilizing GNSS data acquisition, processing, and classification of state vectors to determine the agricultural machine's state, enabling precise position tracking, classification of machine operations, and predictive maintenance to optimize performance and compliance with regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous GNSS data acquisition is performed at high frequency (30-50 times per second), then positional accuracy is improved, but data processing load and energy consumption increase
Solution Approach 1:
The system performs high-frequency data acquisition (30-50 times per second) only when the agricultural machine is in operation, rather than continuously. This partial action approach maintains high positional accuracy during working periods while reducing overall energy consumption and data processing requirements during non-operational periods.
2Loss of information
If all recorded position data is processed continuously, then complete operational analysis is achieved, but processing time and computational resources increase
Solution Approach 1:
The system creates state vectors from position data in real-time during operation, performing preliminary processing of the raw GNSS data. This preliminary action organizes the data into meaningful state representations (position, speed, acceleration) that can be quickly analyzed later, reducing the computational burden during post-processing while maintaining complete operational analysis capability.
3Measurement precision
If detailed position data is collected and processed, then machine state classification accuracy is improved, but device complexity increases
Solution Approach 1:
The system transforms raw position coordinates into state vectors that include derived parameters such as speed and acceleration. This parameter transformation simplifies the classification process by providing directly usable features for determining machine state (working, transporting, idle) without requiring complex analysis of raw coordinate data, thus maintaining high classification accuracy while reducing system complexity.
Data Source
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AI summary
The invention relates to a method for detecting the current state of an agricultural machine (1), comprising the steps of: acquiring position data (Pn,t, Pn+1,t+1, ...) relating to the position of the agricultural machine (1) from a global navigation satellite system (GNSS) including associated timestamps by means of a receiver unit; storing the acquired position data (Pn,t, Pn+1,t+1, ...) including the associated timestamps (t) in a memory; reading stored position data (Pn,t, Pn+1,t+1, ...) including the associated timestamps (t) from the memory by means of a processing unit; generating state vectors (Qm,v, Qm+1,v+1 ...) by means of the processing unit to describe the relative change in position (m) and the travel speed (v) of the agricultural machine (1); and classifying the generated state vectors (Qm,v,k, Qm+1,v+1,k+1 ...) using the computing unit.