Research on Planting Optimisation Strategies Based on 3D Dynamic Matrix Linear Optimisation Algorithm and Monte Carlo Simulation

The aim of this paper is to investigate the application of three-dimensional dynamic matrix linear optimisation algorithms and Monte Carlo simulation in planting optimisation strategies. In this paper, a series of computer algorithm-based models and strategies are constructed as an example of a crop planting programme optimization problem from 2024 to 2030 in a village. Firstly, a three-dimensional dynamic matrix linear optimization algorithm is used to transform the crop planting problem into a profit-maximising optimization problem. By constructing a multidimensional matrix under the constraints and combining the CBC solver with the Big-M algorithm, the optimal planting plan that meets the requirement of maximum profit is solved. Next, Monte Carlo simulation is used to deal with the uncertainties in crop cultivation by forecasting the expected sales volume, cultivation cost, sales price, etc., and combining with the revised constraints to optimise the cultivation strategy to reduce the potential risks. The whole process is efficiently solved and optimised by computer algorithms on the data and models, and 3D spatial distribution maps and 2D heat maps are shown to visualise the optimal crop planting scenarios for each year. This study provides scientific decision support for optimal allocation of resources in complex systems through advanced algorithmic models, demonstrating the potential of computer technology to be widely applied in agricultural planning and sustainable development.