摘要：针对设备集成类工程项目投标中历史数据复用难、设备匹配主观化、信息安全风险高、协同效率低等问题，本文提出构建基于智能匹配算法的投标数据管理系统。该系统以Levenshtein编辑距离为核心，融合设备名称与规格参数加权相似度计算，结合RBAC权限控制与多维数据可视化技术，构建投标全生命周期数据管理闭环。通过理论分析与实践验证，该系统实现了投标数据结构化管理、设备智能匹配、跨部门高效协同与科学决策支持，显著提升了报价效率与准确性，推动投标管理从“经验驱动”向“数据驱动”转型，为建筑及设备集成领域企业提供了可复制的智能化解决方案。

关键词：投标管理；智能匹配算法；Levenshtein距离；数据管理系统；权限控制

Abstract：Aiming at the problems in the bidding of equipment-integrated engineering projects such as difficult reuse of historical data，subjective equipment matching，high information security risks，and low collaboration efficiency，this paper proposes the construction of a bid data management system based on an intelligent matching algorithm. Taking Levenshtein edit distance as the core，the system integrates the weighted similarity calculation of equipment names and specification parameters，and combines RBAC access control and multi-dimensional data visualization technology to build a closed-loop data management system covering the entire bidding life cycle. Through theoretical analysis and practical verification，the system realizes structured management of bid data，intelligent equipment matching，efficient cross-departmental collaboration and scientific decision support，significantly improves the efficiency and accuracy of quotation，and promotes the transformation of bid management from "experience-driven" to "data-driven". It provides a replicable intelligent solution for enterprises in the construction and equipment integration fields.

Keywords：bid management；intelligent matching algorithm；Levenshtein distance；data management system；access control

参考文献

[1] Levenshtein V I. Binary codes capable of correcting deletions，insertions，and reversals[J]. Soviet Physics Doklady，1966（8）：707-710.

[2] Ferraiolo D F，Sandhu R，Gavrila S，etal. Proposed NIST standard for role-based access control[J]. ACM Transactions on Information and System Security，2001（3）：224-274.

[3] Riverbank Computing. PyQt6 Documentation[EB/OL].（2023-06-15）. https：//www.riverbankcomputing.com/static/Docs/PyQt6.

[4] SQLite Development Team. SQLite Database Documentation[EB/OL].（2023-05-20）. https：//www.sqlite.org/docs.html.

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