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New Progress in Artificial Intelligence Algorithm Research Based on Big Data Processing of IOT Systems on Intelligent

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This study optimizes artificial intelligence algorithms for intelligent production lines by improving Internet of Things (IoT) big data processing. The NCluster system demonstrates superior performance over the Namenode system in data processing efficiency and stability.

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Area of Science:

  • Industrial Engineering and Operations Research
  • Computer Science and Information Systems
  • Artificial Intelligence and Machine Learning

Background:

  • Intelligent production lines integrate automation, digital workshops, and intelligent factories, utilizing six key intelligent manufacturing processes.
  • The Internet of Things (IoT) system enhances information dissemination and resource utilization through integrated identification and positioning technologies.
  • Effective big data processing is crucial for intelligent manufacturing, with data quality and processing efficiency being paramount.

Purpose of the Study:

  • To investigate advancements in artificial intelligence (AI) algorithms for processing big data from IoT systems on intelligent production lines.
  • To identify methods for optimizing AI algorithms through the development of intelligent production lines and big data technologies.
  • To propose and evaluate novel approaches for improving data replication and network state prediction within intelligent production environments.

Main Methods:

  • Development of a metadata replication method using a separate replication strategy to shorten data replication time.
  • Implementation of an intelligent production line network state prediction system based on neural networks.
  • Comparative analysis of data processing performance between the NCluster and Namenode systems under varying client loads.

Main Results:

  • The proposed metadata replication method independently replicates the data operation log, reducing overall replication time.
  • The neural network-based prediction system effectively forecasts the operational status of the intelligent production line network.
  • The NCluster system exhibits significantly better data processing stability and efficiency compared to the Namenode system, especially with an increasing number of clients.

Conclusions:

  • Optimized AI algorithms, coupled with advanced big data processing techniques, are essential for enhancing intelligent production lines.
  • The NCluster system offers a more robust and scalable solution for big data processing in intelligent manufacturing compared to traditional methods.
  • This research provides a foundation for further improvements in AI-driven intelligent manufacturing systems and network management.