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Intelligent feeding control system

A control system and intelligent technology, applied in the direction of instruments, fish farming, biological neural network models, etc., can solve the problems of inability to intelligently adjust feeding parameters in time, so as to achieve full absorption, improve feeding effect, and reduce clustering Effect

Active Publication Date: 2021-12-31
广州市蓝得生命科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The invention provides an intelligent feeding control system, which solves the technical problem that the prior art cannot intelligently adjust feeding parameters in time

Method used

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  • Intelligent feeding control system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0038] The embodiment is basically as attached figure 1 shown, including:

[0039] The data collection module is used to collect image information and feeding parameters in the fish tank in real time;

[0040] The image recognition module is used to identify the species of fish and the movement form of fish in the image information;

[0041] The feeding judging module is used to obtain the preset feeding conditions corresponding to the species of fish, and judge whether the feeding parameters meet the preset feeding conditions: if the feeding parameters meet the preset feeding conditions, send an instruction to put in fish feed to the fish feed feeding module; if feeding If the parameters do not meet the preset feeding conditions, send an instruction to adjust the feeding parameters to the parameter adjustment module;

[0042] The parameter adjustment module is used to receive instructions for adjusting feeding parameters, use the fuzzy neural network algorithm to calculate ...

Embodiment 2

[0055] The only difference from Example 1 is that

[0056] In S2, the image recognition module also uses an image recognition algorithm to determine the number and body length of fish. For example, the fish in the fish tank is type A fish, and the number of type A fish can be obtained by image recognition algorithm and statistical algorithm, which is recorded as N; the body length of each type A fish can also be obtained by image recognition algorithm, which is recorded as Lj, and Lj is The body length of the jth species A fish, 1≤j≤N.

[0057] In S5, the fish feed module also obtains the average body length of the fish according to the number and body length of the fish, and determines the fish feed particle size and fish feed hardness according to the average body length. Proportional to body length.

[0058] First, the average body length La of the fish is obtained according to the number and body length of the fish, La=∑Lj / N, 1≤j≤N.

[0059] Then, according to the avera...

Embodiment 3

[0063] The only difference from Example 2 is that

[0064] In S5, the fish feed input module also determines the amount of fish feed to be fed according to the number and body length of the fish, and the amount of fish feed is proportional to the number and body length of the fish; the input of fish feed is determined according to the average body length of the fish The number of times, the single feeding amount is determined according to the number of feeding times and the amount of fish feeding, and the feeding times are inversely proportional to the average body length of the fish.

[0065] One, determine the amount of fish feed that needs to be fed according to the quantity and body length of the fish. For example, the amount of fish feed Q1=α×∑Lj of fish feed A, α is a coefficient obtained in advance according to the eating habits of species A fish, 1≤j≤N; the amount of fish feed is determined according to the number and body length of fish, Ensure that the amount of fis...

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Abstract

The invention relates to the technical field of intelligent fish culture, in particular to an intelligent feeding control system. The intelligent feeding control system comprises a data acquisition module used for acquiring image information and feeding parameters in a fish tank in real time; an image recognition module is used for recognizing the type of fishes and the motion form of the fish in the image information; a feeding judgment module is used for acquiring preset feeding conditions corresponding to the types of the fishes and judging whether the feeding parameters meet preset feeding conditions or not; a parameter adjusting module is used for calculating a correction factor of the feeding parameters according to the type of the fish and the motion form of the fish by adopting a fuzzy neural network algorithm, and adjusting the feeding parameters according to the correction factor; and a fish feed throwing module is used for receiving a fish feed throwing instruction and throwing fish feed into the fish tank. A fuzzy neural network algorithm is adopted to calculate the correction factors of the feeding parameters according to the types of the fishes and the motion forms of the fishes, the feeding parameters are adjusted according to the correction factors, and the accuracy of adjustment of the feeding parameters and the feeding effect are improved.

Description

technical field [0001] The invention relates to the technical field of intelligent fish farming, in particular to an intelligent feeding control system. Background technique [0002] With the continuous improvement of living standards, more and more people raise ornamental fish at home. Although fish farming will bring a lot of fun to life, it is necessary to feed the fish frequently. When you are busy at work or on a business trip or traveling, and there is no one at home, you cannot feed the fish in time. [0003] In this regard, the intelligent fish farming control technology can be used to automate the management of the fish tank, which is convenient for automatic feeding when there is no one at home. For example, the automatic control of feeding is realized through the controller, and the controller cooperates with the camera to build a convolutional neural network and model in the controller, and identify the type and quantity of fish in the fish tank through image r...

Claims

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Application Information

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IPC IPC(8): A01K61/85G06N3/00G06N3/04G06N3/08
CPCA01K61/85G06N3/006G06N3/08G06N3/043Y02A40/81
Inventor 邓汝炬蔡诗李俊斌杨岩陈桂波曹辉
Owner 广州市蓝得生命科技有限公司
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