Intelligent granary management and control method based on machine learning and terminal

Abstract

The invention discloses an intelligent granary management and control method based on machine learning and a terminal. The method comprises steps of obtaining a to-be-predicted input factor of the intelligent granary, sending the to-be-predicted input factor to a granary analysis decision model to obtain a personnel scheduling strategy, the granary analysis decision model comprising a feedforwardmodel established by using a multi-layer feedforward neural network trained by an error back propagation algorithm, and the personnel scheduling strategy comprising sampling inspection personnel, sampling inspection objects and sampling inspection time. According to the invention, the historical data is learned to obtain the granary analysis decision model, and the output error of the network is continuously reduced by adopting the feedforward model established by the multi-layer feedforward neural network trained by the error back propagation algorithm, so that a finer prediction and traceability result is presented; the optimal manual scheduling strategy is found on the basis of the model, so that an intuitive, clear and effective means is provided for personnel allocation for grain management of government grain departments and storage enterprises, and optimal manual scheduling is realized.

Description

technical field [0001] The invention relates to the field of intelligent granary management, in particular to a machine learning-based intelligent granary management and control method and terminal. Background technique [0002] Grain is one of the country's three major strategic resources. Maintaining a certain amount, variety and quality of grain reserves is an essential measure to ensure national food security. Grain storage technology has always been valued by the country's war preparation materials. my country currently has a total More than 27,000 stores. [0003] The grain in the grain storage bin is closely related to the ambient air temperature and humidity outside the grain storage bin. Due to the self-heating characteristics of grain, the temperature and humidity of the grain pile will change seasonally with the outside world, which will cause the grain to heat and mildew, resulting in changes in the quality of the grain. And low temperature storage will make the...

AI Technical Summary

Problems solved by technology

However, no matter which system is used, the following problems cannot be avoided: grain is a poor conductor, and the ambient temperature collected by the temperature sensor is the temperature around the sensor. With the seasons and other factors, different types of grains with different stacking densities accumulate spontaneously. The temperature obtained by the temperature sensor has a certain hysteresis

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