Research on the Resource Optimization Configuration Algorithm of Scientific Research System Based on Data-Driven

This paper proposes a resource optimization configuration algorithm for scientific research system based on data-driven. The purpose is to achieve efficient scheduling and dynamic optimization of various resources in the scientific research system through the combination of big data analysis technology and meta-heuristic algorithm. This algorithm digs deep into the past information and current operating status of the scientific research system, builds an advanced model with data as the core, and then accurately estimates and appropriately arranges various resources such as human resources, materials, and finance to ensure that resource utilization reaches the optimal level. Compared with conventional methods, this research formula not only achieves a leap in the rate of convergence, but also demonstrates an outstanding advantage in optimization accuracy. The simulation experiments show that the resource utilization rate of the algorithm is improved by about 15% in different scientific research scenarios, showing its significant results in resource optimization. In addition, the sensitivity analysis of the algorithm further verifies its robustness and stability in dealing with fluctuations in resource demand, and proves its wide applicability and practical value in resource management of scientific research systems.