Spatial Data Correlation Using Indexed Geometric Groups
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Solution Overview
Problem
Current spatial data processing methods consume excessive calculation resources and result in low efficiency due to the need for cyclic correlation calculations between large numbers of spatial geometric objects in multiple spatial data sets, leading to wasted CPU and GPU resources.
Innovation Solution
Construct spatial indexes and geometric groups for spatial data sets, utilizing idle threads to perform spatial correlation calculations by placing these groups into a message queue, thereby balancing thread loads and optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cyclic correlation calculation is performed between all spatial geometric objects in multiple spatial data sets, then complete spatial correlation analysis is achieved, but calculation resource consumption increases excessively and processing efficiency decreases
Solution Approach 1:
The patent segments the spatial data sets into multiple partitions and performs correlation calculations only between corresponding partitions of different data sets, rather than calculating all possible combinations. This segmentation approach maintains the completeness of spatial correlation analysis while dramatically reducing the number of calculations required.
Solution Approach 2:
The patent performs preliminary spatial indexing and partitioning of spatial geometric objects before correlation calculation. By pre-organizing data into spatial indexes and identifying overlapping regions in advance, the system avoids unnecessary calculations and directly processes only those objects that may have spatial correlations, thus improving processing efficiency.
2Measurement precision
If cyclic correlation calculation is performed between all spatial geometric objects in multiple spatial data sets, then complete spatial correlation analysis is achieved, but calculation resource consumption increases excessively
Solution Approach 1:
The patent divides spatial data sets into partitions and performs correlation calculations only between corresponding partitions, avoiding redundant calculations across all possible object combinations. This segmentation reduces calculation resource consumption while maintaining analysis completeness.
Solution Approach 2:
The patent extracts only the spatial geometric objects that fall within overlapping regions identified through spatial indexing, and performs correlation calculations exclusively on these extracted objects. This extraction approach eliminates wasted computational resources on objects that cannot possibly have spatial correlations.
3Measurement precision
If large number of spatial geometric objects are processed using traditional methods, then comprehensive correlation analysis is achieved, but thread load balancing deteriorates and resource utilization decreases
Solution Approach 1:
The patent segments both the spatial data and the processing threads in a corresponding manner, ensuring that each thread handles a manageable subset of partitioned data. This segmentation approach simplifies thread load balancing while maintaining comprehensive correlation analysis across all data sets.
Solution Approach 2:
The patent introduces spatial indexes and partitioning structures as intermediary layers between the raw spatial data and the correlation calculation process. These intermediaries organize and pre-process data before it reaches the calculation threads, simplifying the processing workload and improving thread load distribution.
Data Source
AI summary
The application discloses a method and apparatus for spatial data processing, and relates to the technical field of data processing. One specific implementation mode of the method comprises: acquiring at least two types of spatial data sets; constructing spatial indexes for the spatial data sets; constructing spatial geometric groups in accordance with the spatial indexes and the at least two types of spatial data sets; placing the spatial geometric groups into a message queue, and subjecting the spatial geometric groups to a spatial correlation calculation by idle threads, which are threads for processing spatial data in the spatial data sets. The implementation mode can effectively balance calculation resources, reduce consumption of the calculation resources, and meanwhile improve a processing efficiency of spatial data.