Multi-user aquaculture automatic regulation system based on reinforcement learning and method

Abstract

The present invention provides a multi-user aquaculture automatic regulation system based on reinforcement learning, which is mainly composed of three modules: control terminals, a data terminal and a cloud platform. The control terminals are used to collect water body data and atmospheric environment data and transmit the collected data to the data terminal while controlling an automatic control module to adjust the aquatic environment. The data terminal is used to collect the data collected by the control terminals and upload the data to the cloud platform. The data terminal can receive a control instruction from the cloud platform and distribute the control instruction to each control terminal. The cloud platform uses the reinforcement learning algorithm to analyze the water quality data of a fishpond, and automatically transmits the control instruction to the data terminal. The invention can be used for the real-time monitoring of the aquaculture environment, adopts automatic means to realize automatic regulation, saves manpower and is suitable for large-scale deployment.

Description

technical field [0001] The present invention relates to a multi-user aquaculture automatic control system and method based on reinforcement learning, specifically a multi-user aquaculture automatic control system and method based on continuous time reinforcement learning Q-Learning algorithm, belonging to reinforcement learning, automatic Control and aquaculture technology field. Background technique [0002] In aquaculture, the growth of fish is not only affected by the aquatic environment, but also by the atmospheric environment. The water temperature, PH value, dissolved oxygen and food quantity of the aquatic environment are all very important to the production of fish, and these indicators need to be tested and monitored frequently. The oxygen content in water is mainly obtained from the contact with air and the photosynthesis of aquatic plants, so it is necessary to monitor the air pressure that affects the dissolved oxygen and the air humidity that affects the photos...

AI Technical Summary

Problems solved by technology

However, the actual aquatic environment is complex, and it is far from enough to judge the range of dissolved oxygen

Some use deep reinforcement learning algorithms to adapt the optimal dissolved oxygen regulation scheme for dissolved oxygen in the water environment (application number: CN201810501384), and perform remote control and automatic execution. However, different types of fish require different ranges of dissolved oxygen. The various aquatic environments (PH, feed, water body capacity, etc.) are also different, which is not conducive to the individual management of multiple fish ponds by each farmer, and is not conducive to the overall growth of fish

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