Urban Land Layout Optimization Using Nested Plot-Grid Simulation
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Solution Overview
Problem
Existing urban land layout optimization methods rely heavily on personal experience, leading to difficulties in accurately quantifying multiple planning objectives and obtaining precise land use layout plans, and fail to consider carbon costs associated with land use function adjustments.
Innovation Solution
A low-carbon urban land layout optimization method using a genetic algorithm with vector plots and nested grid points, incorporating objective functions for low-carbon transportation, urban heat island reduction, and carbon cost control, along with constraints to optimize land use layout.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If grid units are used as analysis units in simulation, then the simulation can be performed, but it leads to excessive computation and land use fragmentation
Solution Approach 1:
The patent implements a nested structure where grid units are embedded within vector plot units. The vector plot unit serves as the primary analysis unit for land use simulation, while grid units are nested inside to provide detailed computational support. This nested approach allows the system to leverage the spatial accuracy of vector units while utilizing the computational simplicity of grid units only where necessary, thereby reducing overall computation while maintaining land use accuracy.
2Manufacturing precision
If vector plot units are used as analysis units, then land use structure can be controlled, but it is difficult to accurately identify neighborhood conditions
Solution Approach 1:
By nesting grid units within vector plot units, the system combines the advantages of both approaches. The vector plot unit maintains land use structure control through its geometric boundaries, while the nested grid units provide fine-grained spatial resolution for accurately identifying neighborhood conditions and spatial relationships.
3Object-affected harmful factors
If optimization focuses only on land use carbon emissions and traffic carbon emissions, then these aspects can be improved, but the carbon cost caused by land use function adjustment is ignored
Solution Approach 1:
The patent segments the carbon emission assessment into three distinct components: land use carbon emissions, traffic carbon emissions, and carbon costs from land use function adjustments. By dividing the overall carbon assessment into these separate segments, the system can independently evaluate and optimize each component, ensuring that the carbon costs associated with land use changes are not overlooked and can be appropriately managed in the optimization process.
4Productivity
If traditional screening methods are used for adjustable plots, then the process can be completed, but the screening method needs to be improved for better accuracy
Solution Approach 1:
The patent incorporates a multi-objective feedback mechanism that evaluates adjustable plots based on multiple criteria including carbon emissions, heat island effect, and land use structure. The genetic algorithm iteratively refines the selection of adjustable plots by receiving feedback from these multiple objective functions, continuously improving the accuracy of plot identification while maintaining screening efficiency through automated optimization.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method accurately quantifies planning objectives, reducing reliance on personal experience and enhancing the precision of land use layout plans by leveraging global and holistic characteristics of the genetic algorithm.
Implementation Method 1
in a genetic algorithm, using a vector plot with internal nested grid points as a simulation analysis unit to simulate the land layout optimization scheme, in a simulation process, using the simulation analysis unit for selecting, crossing, and mutating in the genetic algorithm
Data Source
AI summary
A low-carbon urban land layout optimization simulation method includes establishing a land layout optimization scheme by determining the objective function and constraint conditions of the adjustable land layout optimization; the objective function includes the first objective function to achieve low-carbon transportation, the second objective function to reduce the urban heat island effect, and the third objective function to control and adjust the carbon cost; the vector plot with internal nested grid points is used as the simulation analysis unit, and the genetic algorithm is used to simulate the land layout optimization scheme. In the simulation process, the simulation analysis unit is used to select, cross, and mutate in the genetic algorithm, and finally, the optimal land layout scheme is selected. The method uses the global and holistic characteristics of a genetic algorithm to screen out the optimal land use layout scheme.