The work aims to alleviate the homogenization design phenomenon of medical waste logistics products and promote the construction of smart hospitals by exploring high-quality and effective design strategies for medical waste logistics robots through the collaborative application of the qualia theory and the new quality productivity AIGC. Firstly, the thinking framework of the qualia theory is applied to analyze the design of medical waste logistics robots. ChatGPT is embedded to sort and analyze the target qualia requirements, which are summarized as intentional vocabulary for styling design. The Analytic Hierarchy Process is used to organize the qualia requirements element system, which is used as a visual prompt word in conjunction with Midjourney to construct a styling scheme evaluation library. Then, the target scheme is selected based on public evaluation, and expert evaluation is conducted with qualia force criteria as evaluation indicators. The target scheme is then selected by analogy and optimized in detail using software such as Stable Diffusion and Rhino, and corresponding design strategies are proposed. By combining the qualia theory with AIGC in the design workflow of medical waste logistics robots, qualia information can be obtained and analyzed from multiple dimensions, and the product shape can be deduced from multiple angles. The entire design method can revolutionize the design process of medical waste logistics robots and efficiently construct multi forms and multi style robot design solutions, while also providing corresponding design strategies. In the construction of the medical waste logistics system, a new design concept for medical waste logistics robots has been provided, which combines the qualia theory with AIGC application.