Optimizing AI-Driven Bid Pricing Models for Non-Standard Automation Projects: Leveraging Historical Financial Data and Machine Learning Algorithms

This study proposes an optimization model for bidding quotations of non-standard automation projects based on historical financial data, aiming to address the subjectivity and instability inherent in traditional pricing methods. Through data preprocessing, feature engineering, model selection, and ensemble techniques, a multi-model collaborative prediction framework is constructed. Combining the strengths of tree models, linear regression, and neural networks, the model employs a residual correction mechanism to refine prediction outcomes.Experimental results demonstrate that the integrated model achieves superior pricing accuracy across projects of varying scales and process categories, significantly enhancing quotation stability and reliability. This research provides a feasible technical pathway for automating non-standard project quotations and holds strong potential for engineering applications.