大数据在工程造价预测中的应用 -工程造价专业

摘要

随着信息技术的迅猛发展，大数据技术为工程造价预测提供了全新的思路和方法。传统工程造价预测往往受限于数据规模有限、模型精度不足以及人工经验依赖等问题，而大数据技术能够有效整合海量多源异构数据，提升预测的准确性和效率。本研究旨在探索大数据技术在工程造价预测中的应用潜力，通过构建基于大数据分析的预测模型，解决传统方法中存在的局限性。研究采用机器学习算法与大数据处理技术相结合的方式，对历史造价数据进行深度挖掘和特征提取，并引入神经网络模型优化预测过程。实验结果表明，该方法能够显著提高造价预测的精度，同时降低人工干预的需求。此外，本研究创新性地提出了一种动态更新机制，使模型能够根据新数据实时调整参数，从而适应复杂多变的工程环境。研究表明，大数据技术的应用不仅提升了工程造价预测的科学性和可靠性，还为相关领域的智能化发展提供了重要参考，其主要贡献在于实现了从静态预测到动态优化的转变，为工程管理决策提供了有力支持。

关键词：大数据技术；工程造价预测；机器学习；神经网络模型；动态更新机制

ABSTRACT

With the rapid development of information technology, big data technology has provided new approaches and methods for engineering cost prediction. Traditional cost prediction in engineering is often constrained by issues such as limited data scale, insufficient model accuracy, and heavy reliance on human experience. In contrast, big data technology can effectively integrate massive multi-source heterogeneous data, thereby enhancing the accuracy and efficiency of predictions. This study aims to explore the application potential of big data technology in engineering cost prediction by constructing a predictive model based on big data analysis to address the limitations of traditional methods. The research combines machine learning algorithms with big data processing techniques to conduct in-depth mining and feature extraction of historical cost data, while introducing a neural network model to optimize the prediction process. Experimental results demonstrate that this approach significantly improves the precision of cost prediction while reducing the need for manual intervention. Additionally, this study innovatively proposes a dynamic updating mechanism that enables the model to adjust parameters in real-time according to new data, thus adapting to complex and changing engineering environments. The findings indicate that the application of big data technology not only enhances the scientific rigor and reliability of engineering cost prediction but also provides significant reference for the intelligent development of related fields. Its primary contribution lies in achieving a transformation from static prediction to dynamic optimization, offering robust support for engineering management decision-making.

Keywords: Big Data Technology; Engineering Cost Prediction; Machine Learning; Neural Network Model; Dynamic Update Mechanism

摘要 I

ABSTRACT II

第1章绪论 1

1.1 工程造价预测的研究背景与意义 1

1.2 大数据在工程造价预测中的研究现状 1

1.3 本文研究方法与技术路线 2

第2章大数据技术在工程造价预测中的基础理论 3

2.1 工程造价预测的基本概念与流程 3

2.2 大数据分析方法及其适用性 3

2.3 数据驱动的工程造价预测模型构建 4

第3章大数据在工程造价预测中的关键技术应用 6