The invention relates to the technical field of industrial
data scheduling and intelligent manufacturing, and provides a big-data-driven intelligent
information resource management platform which adopts a closed-loop architecture composed of five modules, namely a data
perception and characterization module, a knowledge
cognition and constraint modeling module, an online dynamic optimization decision module, a resource execution and coordination module and a meta-
cognition and evolution module. Real-time quantitative evaluation of
data quality is realized through a triple quality vector; automatically converting the text
business rule into a computable mathematical constraint and
knowledge graph; carrying out dynamic scheduling
decision making by adopting a knowledge enhancement
reinforcement learning agent fused with a double reward mechanism; node-level real-time
fine tuning is realized through an edge
actuator; and closed-loop self-evolution of the scheduling model and the
knowledge base is realized by means of a meta-cognitive mechanism. According to the method, the scheduling efficiency, the business compliance and the
system adaptive capacity are cooperatively improved, and the method is suitable for industrial quality inspection
data scheduling scenes with high real-time performance and high compliance requirements.