Gracilaria raw material liquid-to-material ratio prediction method and device based on neural network

A neural network and prediction method technology, which is applied in the field of Gracilaria raw material liquid-material ratio prediction device based on neural network, can solve problems such as difficulty in effectively predicting Gracilaria raw material and liquid-material ratio, water resource consumption, etc., so as to ensure the quality of agar. , the effect of reducing consumption

Pending Publication Date: 2020-09-29
JIMEI UNIV
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Problems solved by technology

[0003] In related technologies, it is difficult to effectively predict the ratio of Gracilaria raw materia

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  • Gracilaria raw material liquid-to-material ratio prediction method and device based on neural network
  • Gracilaria raw material liquid-to-material ratio prediction method and device based on neural network
  • Gracilaria raw material liquid-to-material ratio prediction method and device based on neural network

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Embodiment Construction

[0030] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0031] In the related art, in the production process of agar, due to the inability to effectively predict the ratio of the raw material and liquid material of Gracilaria, a large amount of water resources are consumed; according to the method for predicting the raw material ratio of Gracilaria raw material and liquid material based on the neural network of the embodiment of the present invention, First, obtain historical data, wherein, the historical data includes agar index and corresponding Gracilaria raw material liquid-to-mate...

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Abstract

The invention discloses a gracilaria raw material liquid-to-material ratio prediction method, medium, equipment and device based on a neural network, and the method comprises the steps: obtaining historical data which comprises an agar index and a corresponding gracilaria raw material liquid-to-material ratio; training a neural network model according to the historical data to generate a gracilaria raw material liquid-to-material ratio prediction model; acquiring a to-be-predicted agar index, and inputting the to-be-predicted agar index into the gracilaria raw material liquid-to-material ratioprediction model, so as to enable the gracilaria raw material liquid-to-material ratio prediction model to generate a corresponding predicted gracilaria raw material liquid-to-material ratio according to the to-be-predicted agar index. According to the method, the gracilaria raw material and liquid material ratio can be effectively predicted before production, so that the consumption of water resources is reduced while the agar quality is guaranteed.

Description

technical field [0001] The present invention relates to the technical field of intelligent production, in particular to a neural network-based method for predicting the raw material ratio of Gracilaria gracilarius, a computer-readable storage medium, a computer device, and a neural network-based method for gracilaria raw material liquid material than predictive devices. Background technique [0002] Agar, a polysaccharide extracted from seaweed, is one of the most widely used seaweed gels in the world. [0003] In the related art, it is difficult to effectively predict the proportion of Gracilaria raw material and liquid material during the production of agar, which leads to a large consumption of water resources. Contents of the invention [0004] The present invention aims to solve one of the technical problems in the above-mentioned technologies at least to a certain extent. For this reason, an object of the present invention is to propose a kind of Gracilaria raw mat...

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Application Information

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IPC IPC(8): G06N3/02G06N3/08G06Q10/04G06Q10/06G06Q50/04G06F17/11
CPCG06N3/02G06N3/08G06Q10/04G06Q10/06393G06F17/11G06Q50/04Y02P90/30
Inventor 倪辉梁懿陈艳红姜泽东朱艳冰李清彪
Owner JIMEI UNIV
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