A warehouse environment dynamic control optimization method based on artificial intelligence

By extracting multi-scale features through wavelet decomposition and dynamic time warping, and combining historical control strategies and random weights to generate the initial position of the whale optimization algorithm, chaotic perturbation and local enhancement factors are introduced to solve the spatiotemporal dependence problem of temperature and humidity data in the storage environment, thus achieving efficient dynamic control and optimization of the storage environment.

CN122131611APending Publication Date: 2026-06-02CHENGDU BIZ UNITED INFORMATION TECH
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Patent Information

Application Number
CN202610581117.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-04-29
Publication Date
2026-06-02

AI Technical Summary

Technical Problem

Existing technologies cannot effectively capture the spatiotemporal dependencies of temperature and humidity data in the storage environment. Swarm intelligence optimization algorithms have low initial solution quality and are difficult to adapt to multi-peak, time-varying search spaces. Static constraint processing methods cannot simultaneously consider the feasibility of the solution and the optimal energy consumption.

Method used

Wavelet decomposition and dynamic time warping are used to extract multi-scale features. The initial position of the whale optimization algorithm is generated based on historical control strategy and random weights. Chaotic perturbation and local enhancement factor are introduced. Combined with population diversity index and dynamic penalty coefficient adjustment strategy, the search method and constraint handling of whale optimization algorithm are optimized.

Benefits of technology

It improves the spatiotemporal consistency and local fluctuation capture capability of warehouse environment control, enhances the quality of initial solutions and population diversity, achieves an adaptive balance between global exploration and local development, and optimizes the balance between energy consumption and constraint satisfaction.

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Abstract

This application discloses an artificial intelligence-based dynamic control optimization method for warehouse environments, mainly relating to the field of control optimization technology. It addresses the problems of existing solutions where feature representations fail to reflect the local fluctuation patterns and phase differences of different microenvironments within the warehouse. The method includes: adjusting the convergence coefficient, adaptive adjustment factor, and spiral shape parameters involved in the whale optimization algorithm by introducing a population diversity index; employing a fitness function that integrates the total energy consumption target and constraint violation penalties, and using a dynamic penalty coefficient adjustment strategy based on the total historical violations, combined with fuzzy logic to smoothly change the penalty during the iteration process; constructing the next generation of whale population after each iteration through elite retention, crossover operations, and diversity maintenance; and outputting the current globally optimal whale individual position when a preset termination condition is met.
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