A hazard prediction method for sugarcane transport vehicles based on bp neural network

Abstract

The invention discloses a risk prediction method for a sugarcane transfer vehicle based on BP neural network, which belongs to the field of agricultural mechanization and intelligence. The method includes collecting inclination angle and stress data and the waveform of the stability parameter of the transfer vehicle; data processing and waveform processing and Feature extraction; use the gray correlation analysis method to select the first few values ​​that represent the highest risk correlation degree of the transfer vehicle; use the data with a high correlation degree as the model input; optimize the BP neural network modeling based on the genetic algorithm and predict the state of the sugarcane transfer vehicle , to get the current state of the transfer vehicle; adjust the four auxiliary support feet of the transfer vehicle according to the data of the three-axis angle sensor. Under the premise of ensuring that the dangerous situation of the transfer vehicle can be correctly predicted, the gray correlation analysis method is used to select the most relevant monitoring points for the status of the transfer vehicle for monitoring, and the data is transferred to the BP neural network optimized by the genetic algorithm, which is better than only using the BP neural network. Time is saved and the accuracy of forecasts 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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