3D Coverage Demand Heat Maps Using Layered Spatial Grids
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Solution Overview
Problem
Existing methods fail to visually display and model three-dimensional coverage demand situations accurately, lacking targeted analysis for decision-making optimization in complex environments.
Innovation Solution
A digital method and system for constructing a three-dimensional coverage demand heat map by dividing a three-dimensional space into layers, quantifying grid values based on points of interest and detection directions, simulating multiple and hierarchical coverage, and extracting a heat map using pooling nodes to balance computational performance and cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional situation assessment methods (analytic hierarchy process, principal component analysis, grey relational analysis, Bayesian networks) are used to evaluate coverage demand, then quantitative analysis can be performed, but the three-dimensional coverage demand situation cannot be visually displayed and targeted modeling for key factors is lacking
Solution Approach 1:
The patent transitions from traditional two-dimensional or abstract quantitative analysis to three-dimensional visualization by introducing elevation layers (low, medium, high) into the coverage demand heat map. This dimensional change enables intuitive spatial understanding of coverage demand distribution across different altitudes, directly addressing the inability to visually display three-dimensional coverage situations while maintaining manageable modeling complexity through structured layering.
Solution Approach 2:
The patent employs color-coded heat map visualization where different colors represent varying coverage demand levels across the three-dimensional space. This color encoding technique transforms complex quantitative data into intuitive visual patterns, enabling decision-makers to immediately grasp coverage demand distribution without requiring complex mathematical interpretations, thus resolving the visual display capability issue.
2Measurement precision
If three-dimensional spatial discretization is performed to achieve layered gridding, then accurate visual display of coverage demand is achieved, but computational complexity and processing time increase
Solution Approach 1:
The patent divides the three-dimensional environmental space into K elevation layers (low, medium, high) and further grids each layer into I×J cells, creating a structured discrete representation. This segmentation approach maintains measurement precision by capturing coverage demand at multiple elevation levels while managing computational complexity through regular grid patterns that facilitate systematic processing and optimization.
Solution Approach 2:
The patent calculates coverage demand values for all grid cells across all elevation layers, which may exceed the minimum required for basic analysis. This excessive action ensures comprehensive coverage of the three-dimensional space, capturing all relevant coverage demand information, while the structured nature of the grid system allows for efficient processing and selective use of data based on specific decision-making needs.
3Adaptability or versatility
If multiple sets of virtual points of interest are generated to simulate multiple coverage and hierarchical coverage, then comprehensive coverage analysis is achieved, but computational cost increases
Solution Approach 1:
The patent pre-calculates and stores coverage demand values for multiple virtual points of interest and their corresponding detection directions before final analysis. This preliminary action enables comprehensive coverage simulation including multiple coverage scenarios and hierarchical coverage patterns, while the pre-computed values can be efficiently retrieved and combined during decision-making, reducing real-time computational requirements.
Solution Approach 2:
The virtual points of interest and their associated coverage demand values serve multiple functions: they represent actual sensor locations, simulate alternative deployment scenarios, provide hierarchical coverage patterns, and enable analysis of detection direction effectiveness. This multi-functionality achieves comprehensive coverage analysis while minimizing the need for separate computational models for each specific analysis type.
Data Source
AI summary
A digital method and system for constructing a three-dimensional coverage demand heat map can generate a digital coverage demand heat map for a three-dimensional actual scenario. The method includes: firstly, performing layered discretization on a three-dimensional space; secondly, quantifying grid values of a heat map based on points of interest, detection directions, and weights of elevation layers; thirdly, proposing concepts of virtual points of interest and environmental regional division to achieve multiple coverage based on the points of interest and hierarchical coverage based on the detection directions; and finally, introducing pooling nodes and extracting a coverage demand heat map of a relatively low resolution, thereby achieving a trade-off between computational performance and computational cost. The method supports decision-makers in intuitively grasping the coverage demand situation and provides numerical input for downstream decision-making optimization tasks, which is beneficial for the deployment of sensor networks.