宁波市建设项目工程总成本预测数据

用于预测宁波市建设项目工程的总成本，进行建设项目工程全生命周期成本管理模型。在统计时间内，整合采购成本、硬件成本、调整因子、软件功能点（工时*单价）、调整因子、软件成本和隐性成本，输出工程总成本预测。该模型可解决传统造价依赖人工经验，数据滞后性高；市场价格波动导致预算偏差；多源数据（例如设计变更、供应链、工时）协同效率低的问题，实现更加高效精确的建设项目工程总成本预测。建设项目工程总成本预测能够为企业深度剖析各项目的规模体量与增长趋势提供数据支撑，利于统筹规划，这对于制定长期战略规划优化布局、提升市场竞争力至关重要。1.数据采集主要包括维护的建设工程相关的信息、统计时间、采购成本、调整因子、硬件成本、开发工时、单价、软件功能点、软件成本、隐性成本，使用多源异构ETL工具采集动态市场数据，结合OA的项目过程采集和管理数据，建立预测模型。
2.算法规则：硬件成本=采购成本*调整因子。软件成本=软件功能点*调整因子（软件功能点=开发工时*工时单位）。工程总成本预测=硬件成本+软件成本+隐性成本。隐性成本通过历史数据和专家经验进行合理估算。
3.针对不同建设项目工程项目特点和需求，可以灵活调整数据加工模型中的参数和因子，以实现更精准的工程总成本预测。有利于企业能够更准确地把握市场动态，优化资源配置，提高市场竞争力，并制定有效的市场进入和扩张策略。

This dataset is designed to predict the total cost of construction projects in Ningbo, and build a full-life cycle cost management model for construction engineering. Within the statistical time frame, it integrates procurement costs, hardware costs, adjustment factors, software function points (man-hours × unit price), adjustment factors, software costs and implicit costs to output the total project cost prediction.

This model addresses the core challenges of traditional construction cost estimation: heavy reliance on manual experience, high data lag, budget deviations caused by market price fluctuations, and low collaborative efficiency of multi-source data such as design changes, supply chain records and man-hour data, enabling more efficient and accurate total cost prediction for construction projects.

The total cost prediction of construction projects can provide data support for enterprises to deeply analyze the scale and growth trend of each project, facilitate overall planning, which is crucial for formulating long-term strategic planning, optimizing layout and enhancing market competitiveness.

1. Data collection: Mainly includes maintained construction project-related information, statistical time, procurement costs, adjustment factors, hardware costs, development man-hours, unit price, software function points, software costs and implicit costs. Multi-source heterogeneous ETL tools are used to collect dynamic market data, combined with OA-based project process collection and management data, to establish the prediction model.
2. Algorithm rules: Hardware cost = procurement cost × adjustment factor. Software cost = software function points × adjustment factor (software function points = development man-hours × unit price). Total project cost prediction = hardware cost + software cost + implicit cost. Implicit costs are reasonably estimated based on historical data and expert experience.
3. According to the characteristics and needs of different construction projects, parameters and factors in the data processing model can be flexibly adjusted to achieve more accurate total project cost prediction, which helps enterprises accurately grasp market dynamics, optimize resource allocation, improve market competitiveness, and formulate effective market entry and expansion strategies.

搜集汇总

背景与挑战

背景概述

该数据集包含510条记录，每月更新，用于预测宁波市建设项目工程总成本，通过整合采购成本、硬件成本、软件成本和隐性成本等字段，结合算法规则实现高效精确的成本管理，解决传统造价中的数据滞后和预算偏差问题。