Self-adaptive production scheduling system based on artificial intelligence

By constructing an adaptive production scheduling system with a directed acyclic graph and a dual-domain coupled decision module, the problem of misidentification of non-systematic noise by traditional scheduling systems is solved, and accurate response to real faults and improved production stability are achieved.

CN121504092AActive Publication Date: 2026-02-10FUJIAN MINGUANG SOFTWARE CO LTD
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Patent Information

Application Number
CN202610035468.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-01-12
Publication Date
2026-02-10
Estimated Expiration
2046-01-12

AI Technical Summary

Technical Problem

Existing production scheduling systems are prone to misidentifying non-systematic noise caused by sensor signal jitter, minor fluctuations in manual operation, or network latency as systematic disturbances, leading to frequent and meaningless scheduling jitters that affect the continuity and stability of production operations.

Method used

An AI-based adaptive production scheduling system is constructed. The system obtains a directed acyclic graph of production resource allocation and time-series flow through a data acquisition and benchmark construction module. Combined with a theoretical disturbance simulation module and a real deviation extraction module, the system uses a dual-domain coupled decision module to verify causal relationships and generate an adaptive scheduling scheme.

Benefits of technology

It effectively filters out non-systematic noise, ensuring that scheduling instructions only change real physical constraints, thereby improving the robustness and accuracy of scheduling decisions and maintaining the stability and continuity of production rhythm.

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Abstract

The invention relates to the technical field of intelligent manufacturing and production management, in particular to a self-adaptive production scheduling system based on artificial intelligence, which comprises a data acquisition and reference construction module for analyzing process data to construct a directed acyclic graph representing a non-interference state as a reference map; the theoretical disturbance simulation module is used for converting the interference rule into a graph change instruction, generating a theoretical damaged state graph and obtaining a theoretical difference feature vector; the theoretical difference feature vector comprises, but is not limited to, a vector form obtained after a difference matrix is expanded according to rows or columns in terms of mathematical representation; the real deviation extraction module is used for collecting real-time state data to construct a real-time operation state diagram and calculating a real difference feature vector; a double-domain coupling decision module; an adaptive scheduling execution module; according to the method, the causal relationship is verified by comparing the form of theoretical deduction and actual observation, non-systematic noise is effectively filtered, and accurate response to real faults is realized while the stability of the production rhythm is maintained.
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