Liquor production optimization method and system based on swarm intelligence optimization algorithm and medium

By combining a four-level causal cascade prediction model based on swarm intelligence optimization algorithm with particle swarm optimization algorithm, the problems of uncontrollable process and quality fluctuation in liquor production were solved, realizing the scientific, intelligent and inheritable upgrading of liquor production, and improving the synergistic optimization capability of output and quality.

CN122287992APending Publication Date: 2026-06-26JINAN BAOTU SPRING BREWING CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
JINAN BAOTU SPRING BREWING CO LTD
Filing Date
2026-03-13
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

The production of baijiu (Chinese liquor) is characterized by difficulties in inheriting traditional techniques, uncontrollable processes, and large fluctuations in quality. Existing intelligent methods are insufficient to achieve precise control over output and quality, and there is a lack of a systematic framework for process logic such as multi-stage and quality grading.

Method used

A four-level causal cascade prediction model based on swarm intelligence optimization algorithm is constructed. Combined with particle swarm optimization algorithm, data-driven modeling and brewing mechanism are integrated to achieve end-to-end intelligent decision-making from historical production data to optimal process parameters. The "master's experience" is made explicit into a computable model to characterize the causal relationship of the whole link and perform multi-objective optimization.

Benefits of technology

It has improved the scientific, intelligent, and inheritable nature of baijiu production processes, achieved synergistic optimization of output and quality, enhanced the objectivity and reproducibility of process decisions, reduced the cost of manual trial and error, adapted to changes in the production environment, and improved the accuracy of prediction and optimization efficiency.

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Abstract

This application discloses a method, system, and medium for optimizing baijiu (Chinese liquor) production based on a swarm intelligence optimization algorithm, relating to the field of baijiu production technology. The method includes: acquiring historical baijiu production datasets and cleaning and reconstructing them to obtain a structured modeling dataset; constructing a four-level causal cascade prediction model based on the modeling dataset; determining the prediction module in the four-level causal cascade prediction model as the objective function for multi-objective optimization; based on the objective function, using a particle swarm optimization algorithm to perform a global search within a preset process parameter space to calculate the Pareto optimal combination of process parameters, obtaining the optimized process parameter set; inputting the optimized process parameter set into the four-level causal cascade prediction model for simulation verification; if the preset yield and quality synergistic optimization objective is met, it is determined as the final recommended process parameter. This application achieves a scientific, intelligent, and inheritable upgrade of baijiu production technology.
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