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Intelligent Community and Real Estate Management Based on Machine Learning.

Zhixiao Ye1, Yunlong Zhang2, Juan Jiang3

  • 1School of Management, Zhejiang Shuren University and Institute of Modern Services, Hangzhou 310015, Zhejiang, China.

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This study enhances intelligent community and property management systems by applying machine learning to predict network data and optimize mobile data allocation. The research integrates advanced algorithms for improved network services and property management efficiency.

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Area of Science:

  • Intelligent Systems
  • Computer Science
  • Real Estate Management

Background:

  • Network services are crucial for intelligent community construction and property management.
  • Integrating machine learning into real estate property management requires further research for improved network services.

Purpose of the Study:

  • To improve network services in intelligent communities using machine learning.
  • To develop a property management information system leveraging advanced algorithms.
  • To propose strategies for addressing existing challenges in the property management industry.

Main Methods:

  • Proposed a time-series-based network data prediction model using machine learning.
  • Developed a spectrum allocation algorithm considering time and frequency domains for mobile data.
  • Utilized VP-tree algorithm for spatial vector relationships and attention mechanisms for mobile data distribution and traffic prediction.
  • Designed a property management information system with field subsystem, data acquisition, and cloud service layers.

Main Results:

  • The proposed machine learning models demonstrated effectiveness in network data prediction and mobile data allocation.
  • The developed property management information system passed system tests, indicating successful operation.
  • The study successfully integrated machine learning for enhanced intelligent community network services.

Conclusions:

  • Machine learning offers significant potential for improving network services and data management in intelligent communities and property management.
  • The developed system provides a robust framework for intelligent property management.
  • Further development strategies are proposed for the property management industry.