An adaptive AI-enabled framework for cognitive data management and real-time optimization of industrial task...

The high-performance industrial processes under dynamic and complicated workloads require efficient allocation of tasks and control of information. Traditional frameworks are based on unrestricted scheduling and have low scalability, low adaptability, and low resource demand, which in turn causes slow information latency and maintenance downtime, eventually reducing operation efficiency and productivity. The proposed research is the Integrated Information Analytical Framework (IIAF) to optimize the management of cognitive data and processing industrial tasks. The framework also includes an initial task category determination facility to allow task-oriented data processing and a smooth transition to the task allocation stream to enhance processing speed and the accuracy of classification. It also uses dynamic scheduling and smart data management in order to minimize information latency and responsiveness to dynamic workloads. There are real-time cognitive decision-making algorithms that are used to optimize the allocation of resources as well as enhance the scalability and throughput of systems. The experiments prove that IIAF enhances allocation rate by 7.03%, accuracy by 11.78%, lowers information latency by 12.03%, and allocates time by 20.89%. Moreover, it can minimize overload tasks by 18.65% and minimize downtime by up to 37.4%, which proves to be efficient in optimizing the industry.