Facility feed delivery prediction method and prediction system

Abstract

The invention discloses a facility feed delivery prediction method and prediction system and belongs to the Internet of things field. The method includes the following steps that: a BP neural network model is built; data to be predicted and sample data are read from a database, and normalization processing is performed on the data to be predicted and the sample data; the normalized sample data are input into the BP neural network model, so that learning can be carried out, and therefore, connection weight parameters between each layer in the BP neural network can be determined; the normalized data to be predicted are inputted to the BP neural network model, so that the BP neural network model can calculate the normalized data to be predicted according to the connection weight parameters between each layer, so that predicted feed delivery quantity can be obtained. According to the facility feed delivery prediction method and prediction system of the invention, the BP neural network model is utilized to predict the optimal delivery quantity of facility breeding in a certain period, and delivery and detection can be carried out based on the optimal delivery quantity, and therefore, facility feed delivery techniques can be optimized, feed delivery cost can be decreased, labor cost can be reduced, and the quality of facility breeding can be improved.

Description

technical field [0001] The invention relates to the field of the Internet of Things, in particular to a method and system for forecasting facility feeding. Background technique [0002] With the gradual rise of labor costs and the continuous penetration of intelligent sensing equipment in the field of enterprise production, Internet of Things technology and decision support methods have received more and more attention in the field of facility farming. [0003] At present, the feeding of feed in traditional facility farming has been semi-automatic feeding under the guidance of farming experience and scientific data, and the control of feed feeding is essentially determined and operated manually. figure 1 It is a schematic diagram of feeding control in the prior art. Among them, timing and manual feeding are used to meet the feeding needs of facility breeding, and when actual deviations occur, inspections and corrections are performed manually. [0004] In general, the actu...

AI Technical Summary

Problems solved by technology

Too much input will cause waste of cost, too little will not meet the normal growth needs of animals

In order to control the accuracy of feed delivery, the enterprise increases multiple manual inspections and delivery operations, resulting in a large amount of additional labor costs

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