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Data collection provides the foundation for the training and optimization of AI models, ensuring the acquisition of high-quality data to enhance model accuracy. The workflow includes data definition, collection, cleaning, and authorization: defining data types and formats, acquiring data through multiple channels, preprocessing to improve quality, and ensuring data use complies with privacy regulations. Personal large models are built based on personalized user data to simulate user behaviors and preferences, providing precise services. The construction process encompasses collection, training, and optimization. Intelligent agents interact between users and AI systems, generating various roles through Prompt engineering, including Memory Agent (retrieving historical data), Emotion Agent (simulating feelings and emotions), Decision Agent (simulating decision paths and outcomes), and Creation Agent (integrating outputs from various roles to produce multimodal content).