Intensive breeding precise feeding control method based on fuzzy-MPC control

By using underwater cameras and water quality sensors to construct a multi-task learning model and a fuzzy-MPC control system in intensive aquaculture, the problem of the accuracy of feeding control in intensive aquaculture was solved, and precise control of fish growth and health status was achieved, improving the real-time response and long-term optimization capabilities of feeding strategies.

CN121900193APending Publication Date: 2026-04-21NANJING AGRI MECHANIZATION INST MIN OF AGRI +2
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Patent Information

Application Number
CN202610346555.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-20
Publication Date
2026-04-21

AI Technical Summary

Technical Problem

In existing intensive aquaculture, feed feeding relies on experience and labor, making it difficult to achieve precise control over fish growth and health. Furthermore, the lack of multi-source data fusion and collaborative decision-making leads to a disconnect between feeding behavior and fish growth and health, making it impossible to achieve multi-objective optimization for long-term prediction.

Method used

A sensing system encompassing underwater cameras and water quality sensors is employed, and an MTL-LSTM-SAT multi-task learning model is constructed. By combining fuzzy control algorithms and hierarchical control strategies, real-time acquisition and prediction of multi-source data are achieved. Through rapid response of the fuzzy control layer and long-term optimization by the MPC controller, a feeding strategy is formulated.

Benefits of technology

It enables forward-looking prediction of fish growth rate and disease rate, and can complete status assessment and reasoning within milliseconds. It has real-time response and long-term optimization capabilities, improving the accuracy and adaptability of feeding control and reducing feed waste and disease risk.

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Abstract

The invention discloses an intensive culture precise feeding control method based on fuzzy-MPC control, and relates to the technical field of aquaculture intelligent control, and the method comprises the steps: employing a sensing system, collecting multi-source sensing data including fish activity images, healthy growth states and water quality environment data in real time, and forming a comprehensive state data set; and based on the acquired multi-source sensing data, constructing an MTL-LSTM-SAT multi-task learning model. According to the method, the multi-task learning model fusing the LSTM and the soft attention mechanism is constructed, the long-term dependency relationship and dynamic association among the water quality, the feeding and the fish state can be captured, the attention mechanism enables the model to be automatically focused on a key time step, the perception ability of the water quality fluctuation and the feeding influence stage is enhanced, and the accuracy of the water quality fluctuation and feeding influence stage is improved. Meanwhile, double-task collaborative prediction of the growth speed and the disease proportion is achieved through the shared feature layer, a prospective state sequence of the future 7 days is output, and a quantitative and reliable prediction basis is provided for feeding decision making.
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