月收入提升9w+，零售业用大模型实现AI商品出清

Core Insights - The article discusses the challenges faced by the AI product clearance system of Duodian Shuzhi, particularly in the context of generative AI technology and its application in retail [1][2][3][4][5][7]. Challenges - Data Fusion and Quality Risks: The reliance on multi-dimensional data for product decisions is hindered by data dispersion, format heterogeneity, and quality issues. Generative AI can process unstructured data but may produce erroneous associations due to noise, necessitating a self-adaptive data cleaning framework [1]. - Agent Collaboration Conflicts: Conflicts may arise among agents regarding category planning and clearance goals, exacerbated by the opaque nature of generative AI. This requires reinforcement learning to align agent objectives and create interpretable decision protocols [1]. - Model Adaptability to Dynamic Markets: Rapid market changes due to consumer trends or unexpected events necessitate real-time model updates, which traditional training methods struggle to provide. Incremental learning or lightweight models are needed for improved responsiveness [2]. - Integration of Business Rules and AI Decisions: The operational need to balance business logic with AI outputs presents challenges, as rigid rules are difficult to embed in models. Transforming business rules into optimizable constraints and establishing human-AI collaboration mechanisms is essential [3]. Solutions - Data Collection and Preprocessing: The system collects extensive historical sales data, real-time inventory updates, and contextual knowledge about store and product types to enhance model accuracy in identifying unsold and near-expiry items [4]. - Model Training and Optimization: Advanced deep learning algorithms are employed to analyze historical data, enabling the model to predict unsold and near-expiry risks while providing discount recommendations that consider operational realities [4]. - System Integration and Application: The AI model is seamlessly integrated into store management systems, automating the clearance process and significantly improving efficiency and accuracy in handling unsold products [5]. Key Technologies - Large Model Application: A robust industry intelligence model is developed through extensive data training, enhancing the system's ability to understand and analyze complex retail scenarios [7]. - Data-Driven Optimization: The system leverages vast amounts of unique merchant data for continuous model learning and optimization, transitioning from manual decision-making to intelligent automated processes [7]. Economic Benefits - The AI clearance system is projected to enhance monthly revenue by over 90,000 yuan and increase daily profits by over 3,000 yuan, while reducing promotional costs by 15% and maintaining a product availability rate of 98% [8]. Social and Industry Impact - The initiative aims to reduce product waste, improve consumer experience, and enhance operational efficiency, thereby contributing to job stability and sustainable economic development [8][9].