Research on Efficient Parallel Computing Methods for Industrial Big Data Streams in Agile Low Code Development Environments

Industrial Big Data Streams (IBDS) play a pivotal role in the digital transformation of industries, aligning with the special issue's emphasis on computational innovations to manage high-velocity, high-volume data.Traditional approaches to stream processing often fall short in addressing the challenges posed by the dynamic nature, heterogeneity, and real-time demands of IBDS environments, leading to inefficiencies in scalability, adaptability, and resource utilization.To overcome these limitations, this study introduces an Adaptive Stream Processing Framework (ASPF), which integrates distributed computing, machine learning, and dynamic resource allocation to process IBDS with low latency and high throughput.Complementing ASPF, a Dynamic Hierarchical Decision Strategy (DHDS) ensures multi-level decision-making for optimal resource distribution and real-time adaptation.The ASPF leverages predictive analytics and anomaly detection to enhance operational insights, while the DHDS employs distributed consensus and reinforcement learning to dynamically balance workloads and maintain system resilience.Simulation results demonstrate a 25% improvement in data processing efficiency and a 20% reduction in energy consumption compared to conventional methods, underscoring the 17 potential of this hybrid framework to revolutionize industrial data systems.