Hierarchical Spatial Data Indexing for Scale Unification
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Solution Overview
Problem
Conventional data processing methods fail to unify objectively existing intrinsic scales with subjectively set non-intrinsic scales, leading to performance bottlenecks in data organization, storage, indexing, and analysis, particularly in spatial data processing, which restricts the development of applications like computer graphics, virtual reality, and geographic information systems.
Innovation Solution
A method and apparatus for processing data that involves setting a scale, analyzing and calculating relationships between data, and establishing an index to unify the management, storage, transmission, and display of data across different scales, allowing for efficient storage, retrieval, and analysis of complex spatial data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If spatial data is pre-processed by a server to render into map images and cut into raster images, then the performance bottleneck of map display is solved, but other functions of the system cannot be realized and new bottlenecks are created
Solution Approach 1:
The patent segments spatial data into hierarchical levels (macro, intermediate, micro) that can be independently processed and managed. This allows the system to handle both display-optimized raster images and analysis-ready vector data separately, resolving the contradiction between display performance and system versatility.
Solution Approach 2:
The patent creates a unified data processing framework that serves multiple functions: rendering map images for display, performing spatial analysis, and supporting various data formats. This multi-functional approach eliminates the need for separate processing pipelines, thereby improving both display performance and system adaptability.
2Measurement precision
If different data, coordinate points, and data bits are used for maps with different scales, then the problem of unification between intrinsic and non-intrinsic scales cannot be solved, but data processing complexity increases
Solution Approach 1:
The patent implements a nested hierarchical structure where micro-level data is contained within intermediate-level data, which is contained within macro-level data. This nesting allows seamless transitions between scales while maintaining a unified data model, reducing processing complexity compared to managing separate data systems for each scale.
Solution Approach 2:
The patent introduces dynamic scale adaptation where the data representation automatically adjusts based on the required scale. The system dynamically selects appropriate data levels and processing methods, enabling unified handling of intrinsic and non-intrinsic scales without manual intervention or increased complexity.
3Quantity of substance
If massive spatial data is processed and transmitted, then the data amount increases, but network bandwidth and server pressure become bottlenecks
Solution Approach 1:
The patent extracts only the necessary data for each specific task (display vs. analysis) from the massive spatial dataset. By separating display-optimized raster data from analysis-ready vector data and transmitting only what is needed, the system reduces network bandwidth consumption while maintaining full data availability for different functions.
Solution Approach 2:
The patent performs preliminary processing and organization of spatial data into hierarchical levels before transmission. This pre-processing structures the data in advance, allowing the system to efficiently transmit and retrieve only the required portions based on scale and function, thereby reducing network load and server pressure.
Data Source
AI summary
Disclosed are a data processing method and device, the method including: setting the scale of data; analyzing and computing a correlation between the data according to the scale of the data; and processing the data on the basis of the correlation according to a processing method corresponding to a set processing type. According to different processing types, disclosed are: a data management and storage method; a method for establishing indexes and analyzing management data; a method for managing and storing data on the basis of indexes; a method for displaying data; a method for analyzing and computing data; and a method for progressive transmission of data.