Research on Efficient Processing Algorithm for Engineering Cost Data Using Cloud Computing Platform

This paper proposes a research scheme for efficient processing algorithm of engineering cost data based on cloud computing platform, aiming to improve data processing efficiency by utilizing the high-performance computing capability and distributed characteristics of cloud computing. The algorithm design takes distributed compression algorithm as the core, combines data partitioning, parallel computing and storage optimization technology to achieve efficient storage and fast access to large-scale cost data. In the system implementation, a parallel algorithm architecture that adapts to multi-node environment is designed. The algorithm performance is evaluated by building a simulation model. The experimental results show that the method proposed in this paper is superior to traditional algorithms in data compression rate, processing speed and resource utilization. When processing 1TB cost data, the compression rate reaches 85%, and the processing efficiency is improved by about 60%. Data analysis further verifies the stability and scalability of the algorithm, and provides theoretical basis and technical support for intelligent data processing in the field of engineering cost. This study opens up a new path for engineering cost data processing, and the proposed algorithm and platform architecture can be widely used in other complex data scenarios.

I. Introduction

With the continuous expansion of the scale of modern engineering projects, engineering cost data presents the characteristics of multidimensionality, high complexity and large scale. The traditional engineering cost data processing method can no longer meet the actual needs. How to efficiently and accurately process engineering cost data has become one of the important research directions in the field of engineering management [1]. In recent years, cloud computing technology has provided a new idea for solving large-scale data processing problems with its powerful computing power and distributed architecture advantages. Engineering cost data is an important basis for the whole process management of engineering projects, involving many aspects such as project budget preparation, contract management and cost control [2]. However, these data are usually large in volume and complex in format. The traditional centralized processing method is not only inefficient, but also easily limited by hardware performance, and it is difficult to meet the requirements of real-time and high efficiency. In addition, the dynamic and multi-source nature of engineering cost data makes it urgent to process and store it quickly and efficiently. As a new type of distributed computing architecture, cloud computing provides efficient and scalable technical support for large-scale data processing, which can effectively deal with the computing resource bottleneck and storage optimization requirements in engineering cost data processing [3].