基于感性工学增强的GPT模型在分布式定制化制造方案中的研究

    A Study of GPT Model Enhanced by Kansei Engineering in Distributed Customized Manufacturing Solutions

    • 摘要: 针对消费者个性化需求得不到满足、传统制造业对用户自然语言表达分析不到位,以及商家不能准确把握反馈的产业环境等问题,提出一种基于感性工学增强的GPT模型(简称“感性工学GPT”),并基于感性工学GPT进一步提出分布式定制化制造方案。该方案通过研究、微调、训练GPT语言模型,并结合基于感性工学的文字情感挖掘方法,探讨感性词汇的情感量化,根据用户的个性化需求生成定制产品。同时将个性化产品数字原型上传至区块链,通过区块链技术保护用户知识产权和敏感信息。针对分布式制造环境中缺少透明管理机制、设备差异大、用户需求多等问题,提出一种基于多属性逆拍卖和区块链的任务分配及定价机制。最后建立基于感性工学GPT的区块链分布式3D制造管理平台,通过与其他分配方法对比,验证提出的任务分配策略在分布式制造环境中的可行性,证明了基于感性工学GPT驱动的分布式定制化制造方案能有效地在分布式环境中实现用户的个性化需求。

       

      Abstract: According to the industrial challenge where consumers’ personalized demands are unmet and feedback is inaccurately captured by merchants, this paper introduces an enhanced GPT model based on Kansei engineering, termed “Kansei engineering GPT”, and proposes a distributed customized manufacturing solution based on it. The solution fine-tunes the GPT language model, trains it on a customized dataset, and integrates a sentiment mining approach to generate products that meet user personalization. Digital prototypes are blockchain-uploaded to safeguard intellectual property and sensitive information. Addressing transparency and variability in distributed manufacturing, we introduce a task allocation and pricing mechanism using multi-attribute inverse auction and blockchain.Finally,by establishing a blockchain distributed 3D manufacturing management platform based on Kansei engineering GPT and verifying the feasibility of the task allocation strategy proposed in the paper in a distributed manufacturing environment by comparing it with other allocation methods, it is generally proved that this perceptual engineering GPT-driven distributed customized manufacturing scheme can effectively realize the user's personalized needs in a distributed environment.

       

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