Training method, storage medium and terminal of pso-bp neural network model

Abstract

The invention discloses a training method of PSO-BP neural network model for heavy metal content prediction, a storage medium and terminal equipment. The training method includes: measuring content ofheavy metal of sample soil to acquire heavy metal content data of the sample soil; performing spectrum reflectivity treatment on the sample soil to acquire a spectrum reflectivity curve of the samplesoil after being treated; according to the heavy metal content data of the sample soil and the spectrum reflectivity curve of the sample soil after being treated, performing feature waveband selection to acquire feature waveband of the sample soil; building the PSO-BP neural network model; inputting the feature waveband of the sample soil into the PSO-BP neural network model for training study until the PSO-BP neural network model converges. Accuracy of soil heavy metal content estimation and predication through the model trained by the method is improved greatly.

Description

technical field [0001] The present invention relates to the field of artificial intelligence technology, in particular to a training method, storage medium and terminal equipment for a PSO-BP neural network model used for heavy metal content prediction Background technique [0002] Soil is a limited and important natural resource, and heavy metal pollutants in the environment have the characteristics of circulation and extremely difficult to degrade, especially the heavy metals in the soil are both enriched, which will eventually cause the existing or potential degradation of soil quality, ecological environment Deterioration and other phenomena, and can reach the human body through the food chain, directly endangering human health. For example, South China is one of the most important mineral belts in China. At present, many mineral resources are mainly distributed in the upstream or tributaries of the Pearl River Delta water system and the mountainous areas of important ag...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art, the invention provides a kind of training method, storage medium and terminal equipment for the PSO-BP neural network model of heavy metal content prediction, the PSO-BP neural network trained by this method The prediction accuracy of the model has been greatly improved, solving technical problems such as the low prediction accuracy of the original model

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