With the advent of the era of big data, the combination of science and technology and urban and rural planning has become the focus of many countries. The improvement of village planning and the redesign of rural buildings can promote the rapid development of villages and strengthen rural cohesion. Based on the above situation, this paper proposes a dynamic programming algorithm combined with discrete dynamic modeling technology to improve rural planning. Firstly, the dynamic programming algorithm is used to reconstruct the village layout and optimize the original village model. The dynamic monitoring technology is used to update the dynamic data in real time to provide specific information for the follow-up rural architectural design. Secondly, the dynamic modeling technology is used to build the building model, which can calculate the building location and building angle of the village. In order to further improve village development, we also put forward the concept of green building design. The performance of traditional modeling technology and discrete dynamic modeling technology in the green building design model is compared. The results show that the discrete dynamic modeling technology can improve the overall performance of rural buildings and improve the operation efficiency of the system in the batch design of green buildings. The village layout improved by dynamic planning reduces the specific travel distance of villagers and provides effective help for rural construction and economic development. Compared with traditional modeling technology, dynamic modeling technology has a shorter workflow and less time. Discrete dynamic modeling technology can realize dynamic batch architecture design and has higher applicability than traditional algorithms.

City leading industries are the pillars of urban economic development and are constantly changing as urban economic development enters different stages. The weight setting of many factors in the existing leading industry selection methods and means is mainly set by humans, which is highly subjective and lacks dynamics, integrity, and quantification, and the accuracy of prediction results is not high. Therefore, starting from respecting objective data, the SSM selection method with both dynamic and quantifiable properties is introduced. Based on the SSM mathematical model and principles, 35 manufacturing industries in Guangzhou in 2015 and 2020 are selected as initial variables and stage variables, respectively, taking 35 corresponding industrial sectors in the province as reference variables at the same time point and using the SSM algorithm as an analytical tool to conduct an empirical analysis of the share deviation component, structural deviation component, and competitiveness deviation component of the 35 manufacturing industry sectors in Guangzhou. After drawing the Shift-share analysis chart, it was found that there are 12 industrial sectors most likely to become the city leading industries in Guangzhou, and 4 suggestions for the development planning of city leading industries were put forward; they are, respectively, ➀ accelerate traditional industries technological upgrading, ➁ focus on optimizing automobile manufacturing industry, ➂ promote leading industries independent innovation, and ➃ create leading industry sharing platform.

Site road alignment is one of the main elements of architectural environmental design [B. Holdsworth, Refocus 6(1) (2005) 58–60]. It is difficult for traditional methods to achieve both qualitative exploration and quantitative optimization objectives, and optimization algorithms can only optimize quantitative objectives. On the other hand, the shape of the site road is subject to various other human interventions besides the designer, which is a multi-objective problem. Based on the idea of meta-design, this paper proposes a new method for “form-finding” of site roads. This method develops the architecture and components of an expert system, which can handle both qualitative and quantitative objectives in conceptual design, and can realize various human interventions. Component development combines site specification, recursive algorithm, and genetic algorithm, which treats human intervention as a factor of variation, allows for qualitative exploration of different preferences, and finally demonstrates the capabilities of this new approach through case studies.

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