Sugarcane transfer trolley danger prediction method based on BP neural network

Abstract

The invention discloses a sugarcane transfer trolley danger prediction method based on a BP neural network, and belongs to the field of agricultural mechanization and intellectualization. The method comprises the following steps of acquiring the waveform of inclination angle and stress data and transfer vehicle stability parameters; carrying out data processing, waveform processing and feature extraction; selecting the first several values representing the maximum danger association degree of the transfer trolley by using a grey association analysis method; inputting data with larger association degree as a model; optimizing BP neural network modeling based on a genetic algorithm and predicting the state of the sugarcane transfer trolley to obtain the current state of the transfer trolley; and adjusting four auxiliary supporting legs of the transfer trolley according to the data of a three-axis angle sensor. On the premise of ensuring that the danger condition of the transfer trolley can be correctly predicted, the grey association analysis method is used for selecting a monitoring point most relevant to the state of the transfer trolley for monitoring, and data are transmitted into the BP neural network optimized by the genetic algorithm, so that compared with the method which only uses the BP neural network, the time is saved, and the prediction accuracy is improved.

Description

technical field [0001] The invention relates to the field of agricultural mechanization and intelligence, in particular to a method for predicting the risk of a sugarcane transfer vehicle based on a BP neural network. Background technique [0002] Guangxi is the main sugarcane production area in the country, accounting for about 60%. However, since the sugarcane fields are mostly located in hilly areas, after the sugarcane is harvested, the sugarcane is transferred to a larger cane collection truck. Due to the transfer from the transfer vehicle to a larger sugarcane collection vehicle, the stability of the transfer vehicle will not be as stable as in the plains due to the hilly area, and many nonlinear and strong coupling risks will occur. Especially when the transfer vehicle is loaded and placed on uneven hilly land and then tilted at a certain angle, there will be a danger of the transfer vehicle tipping over and the support rod breaking. These are very dangerous accidents...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to provide a method for predicting the risk of sugarcane transfer vehicles based on BP neural network to solve the technical problem that the existing sugarcane transfer vehicles are not equipped with stability monitoring and are prone to rollover when working in hilly areas

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