Research on Intelligent Layout Planning of Flower Base under the Perspective of Multi-model Integration

Abstract

In this paper, particle swarm algorithm and Monte Carlo simulation are used to focus on the optimization of the layout of the flower base, and a multi-objective decision-making model based on the behavior of tourists and planting efficiency is constructed. Firstly, for the path planning, two design schemes are proposed: single entrance and single exit and double entrance and three exits; through particle swarm algorithm to optimize the calculation model of tourists' sight range; combined with Monte Carlo simulation to construct the queuing system; and comprehensively balance the time satisfaction and viewing efficiency. It is found that the single-entrance layout has a significant advantage when the flow of visitors is controllable, while the multiple-entrance design is more adaptable during peak hours. Secondly, for multi-flowering peony planting, a regression model of yield and production value based on quadratic saturated D-optimal design was established, and an improved immunity algorithm was introduced to optimize the planting density ratio, so as to clarify the spatial allocation strategy among subspecies in different flowering periods. The results of the study show that reasonable path planning and planting structure optimization can significantly improve the operational efficiency of the base, which provides a methodological reference for the sustainable development of modern urban agricultural parks.