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Related Experiment Video

Updated: Sep 1, 2025

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Design of Table Tennis Training Competition Knowledge Interaction Platform Integrating Improved Swarm Intelligence

Deqi Li1

  • 1Department of Physical Education, Zhejiang Yuexiu University of Foreign Languages, Shaoxing, Zhejiang 312000, China.

Computational Intelligence and Neuroscience
|August 12, 2022
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Summary
This summary is machine-generated.

This study enhances table tennis big data analysis using an improved swarm intelligence algorithm. The optimized firefly algorithm boosts platform efficiency and reduces resource usage for better training insights.

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Area of Science:

  • Sports Science
  • Data Science
  • Computer Science

Background:

  • Table tennis is a significant sport in China, generating vast amounts of training and match data.
  • Existing data analysis methods may not fully leverage big data for optimizing training and competition strategies.

Purpose of the Study:

  • To develop an intelligent knowledge interaction platform for table tennis training and competition.
  • To enhance big data processing capabilities using swarm intelligence algorithms.

Main Methods:

  • Utilized swarm intelligence algorithms, specifically an improved firefly algorithm, for big data processing.
  • Implemented Nginx and Tomcat for technical support within the platform.
  • Established a firefly algorithm model to enhance resource search capabilities.

Main Results:

  • The improved swarm intelligence algorithm demonstrated enhanced global convergence.
  • Load balancing effectiveness decreased over time, indicating efficient resource allocation.
  • The enhanced firefly algorithm showed significant performance improvements under low bandwidth, reducing resource occupancy by 12.55% at bandwidth 20.

Conclusions:

  • The developed platform, powered by an improved swarm intelligence algorithm, offers a more intelligent and efficient solution for table tennis big data analysis.
  • The system effectively addresses limitations of long processing times and low success rates in traditional approaches.
  • The findings validate the system's operational convenience and functional capabilities for sports data analysis.