科学技术与应用

企业投标业务的AI协同提效研究

李 明

青岛新力通工业有限责任公司

徐 俊凯

青岛新力通工业有限责任公司

高 凯

青岛新力通工业有限责任公司

孙 夏天

青岛新力通工业有限责任公司

王 宁

青岛新力通工业有限责任公司

DOI: https://doi.org/10.59429/kxjsyy.v3i5.15724

Keywords: 人工智能；招标信息采集；标书生成；投标自评；辅助报价

---

Abstract

针对企业投标业务中商机检索重复、标书编制耗时和投标前检查工作量大等问题，以企业大市场中心的 AI 应 用为基础，提出信息复用、依据关联与人工复核相结合的协同方法。通过招标信息结构化整理、标书辅助生成及逐 项检查评分，衔接商机发现与投标文件优化，并依据技术、商务和价格评分规则设计辅助报价情景分析。使用人员 反馈标书编制效率提升约 30%。该方法为企业投标业务提效及报价决策提供参考。

---

References

[1] LEWIS P,PEREZ E,PIKTUS A,et al.Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks[C]//Advances in Neural Information Processing Systems:Vol.33.Red Hook:Curran Associates,2020:9459-9474.

[2] LI J,XU Y,CUI L,et al.MarkupLM:Pre-training of Text and Markup Language for Visually Rich Document Understanding[C]//Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1:Long Papers).Dublin:Association for Computational Linguistics,2022:6078-6087.DOI:10.18653/v1/2022.acl-long.420.

[3] XU Y,XU Y,LV T,et al.LayoutLMv2:Multi-modal Pre-training for Visually-rich Document Understanding[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1:Long Papers).Online:Association for Computational Linguistics,2021:2579-2591.DOI:10.18653/v1/2021.acl-long.201.

[4] KARPUKHIN V,OGUZ B,MIN S,et al.Dense Passage Retrieval for Open-Domain Question Answering[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP).Online:Association for Computational Linguistics,2020:6769-6781.DOI:10.18653/v1/2020.emnlp-main.550.

[5] MADAAN A,TANDON N,GUPTA P,et al.Self-Refine:Iterative Refinement with Self-Feedback[C]//Advances in Neural Information Processing Systems:Vol.36.Red Hook:Curran Associates,2023:46534-46594.DOI:10.52202/075280-2019.

[6] ZHENG L,CHIANG W L,SHENG Y,et al.Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena[C]//Advances in Neural Information Processing Systems:Vol.36. Red Hook:Curran Associates,2023:46595-46623. DOI:10.52202/075280-2020.

[7] AUTIO C,SCHWARTZ R,DUNIETZ J,et al.Artificial Intelligence Risk Management Framework:Generative Artificial Intelligence Profile[R].Gaithersburg:National Institute of Standards and Technology,2024:1-2.DOI:10.6028/NIST. AI.600-1.