Research on data-driven optimization and decision support algorithm for building design parameters

With the wide application of intelligent and information technology in the field of architecture, the traditional experience-based design method has been difficult to meet the demand for high-performance, green and personalized buildings. How to efficiently extract valuable information from massive multi-source data and construct a scientific and reasonable optimization model of building design parameters has become a key issue in the development of intelligent building design. This paper focuses on the design concept of “data-driven”, systematically researches the core technology of architectural design parameter optimization and decision support, and proposes a set of integrated methods that integrate data mining, optimization algorithms and decision models. In this paper, a data-based architectural design system architecture is first constructed, covering key aspects such as data collection, parameter sensitivity analysis, model construction and evaluation, to ensure the scientific and operability of the design scheme. On this basis, a variety of intelligent optimization algorithms, such as genetic algorithm and Bayesian optimization, are combined to realize the automatic optimization of building performance parameters for multiple objectives. At the same time, a decision support system with interactive and visualization functions is developed to support designers to carry out multiple rounds of iteration and scheme comparison, which significantly improves the design efficiency and quality of results. Through the application of actual architectural design projects, the proposed method can effectively reduce the design energy consumption, improve spatial comfort, and realize the intelligence and refinement of parameter selection. The results show that the data-driven design method not only enhances the adaptability and foresight of architectural programs, but also provides theoretical support and technical paths for the development of intelligent buildings and green design in the future.