<p>在我国水利工程建设规模持续扩张、投资力度不断加大的背景下，成本管理已成为决定项目经济效益与行业可持续发展的核心要素。当前，行业同质化竞争加剧、监管体系滞后，加之传统成本管理模式存在预算粗放、现场管控薄弱等弊端，导致水利项目普遍面临成本超支、利润压缩等困境。在此背景下，探索智慧化成本管理路径对提升水利工程管理水平、增强企业竞争力具有重要现实意义。</p> <p>本研究以R公司BOT模式下N水利工程项目为对象，综合运用问卷调查、案例分析等方法，对项目成本管理全流程展开系统研究。通过设计发放调查问卷，并结合施工现场实地调研与财务数据梳理，研究发现：N项目在成本预算、施工现场管理、运行维护等环节存在显著问题。成本预算阶段，因项目周期长、单体复杂且市场监测机制缺失，导致预算编制依赖经验估算，与实际成本偏差达15%-20%；施工现场存在材料浪费严重、劳务机械成本失控等问题，其中材料损耗率超行业标准8个百分点；运行管理方面，智慧化水平低、设备维护依赖人工，致使年度运营成本居高不下。深入分析其原因，主要涉及内部组织架构职责不清、人员成本管理意识薄弱、信息化技术应用滞后等方面。</p> <p>针对上述问题，本文构建了N项目成本管理智慧化改造框架体系。在成本预算环节，引入大数据分析与智能算法，建立动态成本预测模型，实现市场价格实时追踪与预算动态调整；在施工现场管理中，依托物联网技术搭建智能监控平台，对材料消耗、人员调度、设备使用进行可视化管控，并推行电子报销流程提升经费透明度；在运行管理层面，打造统一智慧化运维系统，整合设备监测、故障预警、人员调度功能。实施智慧化改造后，N项目施工成本降低18%，运行成本减少22%，管理效率提升35%，有效验证了智慧化策略在水利工程成本管理中的可行性与有效性。研究成果为水利工程行业突破成本管理瓶颈、实现数字化转型提供了理论参考与实践借鉴。</p>

<p>Against the backdrop of the continuous expansion of water conservancy project construction scale and increasing investment in China, cost management has become a core factor determining the economic benefits of projects and the sustainable development of the industry. Currently, intensified homogeneous competition within the industry, a lagging regulatory system, coupled with drawbacks such as rough budgeting and weak on-site control in traditional cost management models, have led to common dilemmas in water conservancy projects, including cost overruns and compressed profit margins. In this context, exploring intelligent cost management approaches is of great practical significance for enhancing the management level of water conservancy projects and strengthening enterprise competitiveness.</p> <p>This study takes the N water conservancy project under the BOT model of Company R as its subject.&nbsp;as the research object and comprehensively applies methods such as questionnaires and case analysis to conduct a systematic thesis on the entire process of project cost management. Through the design and distribution of questionnaires, combined with on-site investigations at the construction site and the collation of financial data, the thesis reveals significant issues in various aspects of the N Project, including cost budgeting, construction site management, and operation and maintenance. During the cost budgeting stage, due to the project&#39;s long cycle, complex individual components, and the absence of a market monitoring mechanism, budget preparation relies on empirical estimates, resulting in a&nbsp;deviation of 15%-20% from the actual costs. At the construction site, problems such as severematerial waste and out of labor and machinery costs are evident, with the material loss rate exceeding the industry standard by 8 percentage points. In terms of operation management, the low level of intelligence and heavy reliance on manual equipment maintenance contribute&nbsp;to high annual operating costs. An in-depth analysis of the causes reveals that these issues are mainly related to unclear responsibilities within the internal organizational structure, weak cost management awareness among personnel, and a lag in the application of information technology.</p> <p>In response to the above problems, this paper constructs an intelligent transformation framework system for the cost management of the N Project. In the cost budgeting process, big data analysis and intelligent algorithms are introduced to establish a dynamic cost prediction model, enabling real-time tracking of market prices and dynamic budget adjustments. In construction site management, an intelligent monitoring platform is built based on the Internet of Things technology to conduct visual control over material consumption, personnel scheduling, and equipment usage, and an electronic reimbursement process is implemented to enhance the transparency of funds. At the operation management level, a unified intelligent operation and maintenance system is created to integrate functions such as equipment monitoring, fault warning, and personnel scheduling. After the implementation of the intelligent transformation, the construction costs of the N Project decreased by 18%, operation costs were reduced by 22%, and management efficiency increased by 35%, effectively verifying the feasibility and effectiveness of intelligent strategies in the cost management of water conservancy projects. The research results provide theoretical references and practical insights for the water conservancy project industry to overcome cost management bottlenecks and achieve digital transformation.</p>

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