基于数据挖掘技术的BIM工程信息管理平台的构建

B. Xu
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引用次数: 0

摘要

数据挖掘技术是当前数据库和人工智能领域的研究热点之一。从海量的数据信息中,可以很容易地提取出易于理解、丰富、隐藏、潜在有效、未知的实用信息。它结合了数据库管理技术、模式识别技术、人工智能、机器学习、统计学等多学科的理论,形成新的技术方法。本文旨在研究基于数据挖掘技术的BIM工程信息管理平台的构建。在分析数据挖掘方法和BIM特点的基础上,设计并实现了BIM工程信息管理平台,最后对平台进行了测试。测试结果表明,当数据量较大时,加速比与节点数成一定比例正相关。随着节点数量的增加,该比率几乎保持不变。这表明,随着节点数量的增加,Hadoop集群的效率明显高于单机。它也相对稳定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Construction of BIM Engineering Information Management Platform Based on Data Mining Technology
Data mining technology is one of the current research hotspots in the field of database and artificial intelligence. From the massive amount of data information, practical information that is easy to understand, rich, hidden, potentially effective, and unknown can be easily extracted. It combines the theories of database management technology, pattern recognition technology, artificial intelligence, machine learning, statistics and many other disciplines to form new technical methods. This article aims to study the construction of a BIM engineering information management platform based on data mining technology. Based on the analysis of data mining methods and the characteristics of BIM, the BIM engineering information management platform is designed and implemented, and finally the platform is tested. The test results show that when the amount of data is large, the speedup ratio is positively correlated with the number of nodes in a certain proportion. As the number of nodes increases, the ratio remains almost unchanged. This shows that as the number of nodes increases, the Hadoop cluster is significantly more efficient than a stand-alone machine. It is also relatively stable.
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