Route Optimization Memory Management via Dynamic Tile Loading
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Solution Overview
Problem
Route optimization algorithms face challenges in efficiently determining optimal routes for complex projects like pipeline construction due to the need to harmonize multiple geological, environmental, and engineering factors, often requiring large amounts of data and computational resources, which can lead to inefficiencies and increased costs.
Innovation Solution
The implementation of a route optimization system that dynamically processes data by using a cost surface representation and a memory management technique where data is loaded only as needed, allowing the algorithm to find optimal routes without relying on predefined bounding boxes, thus reducing computational burden and increasing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional route optimization algorithms process entire large geographic areas at once, then complete route coverage is achieved, but memory consumption and computational burden increase significantly
Solution Approach 1:
The patent divides the large geographic area into multiple smaller tiles or segments. Each tile can be processed independently and loaded into memory only when needed for route calculation. This segmentation allows the system to handle large geographic areas without requiring excessive memory resources, as only the currently relevant tile data is loaded into memory at any given time.
Solution Approach 2:
The patent pre-processes and stores geographic data in a tiled format with predefined boundaries and spatial indices before route optimization begins. This preliminary organization of data into manageable segments enables efficient random access during route calculation without requiring the entire dataset to be loaded into memory simultaneously.
2Productivity
If route optimization algorithms load all necessary data into memory, then processing speed improves, but memory footprint increases
Solution Approach 1:
By segmenting geographic data into tiles, the system can load only the necessary tile segments into memory during processing. This maintains fast access speeds for the loaded data while keeping the overall memory footprint manageable, as unused tiles remain on disk or other persistent storage.
Solution Approach 2:
The patent implements dynamic tile loading and unloading based on the current processing stage and route search progress. Tiles are loaded into memory when they become relevant to the current route calculation and unloaded when no longer needed, optimizing the balance between processing speed and memory consumption throughout the optimization process.
3Device complexity
If predefined bounding boxes are used to limit processing area, then computational burden reduces, but route optimization accuracy decreases due to arbitrary boundaries
Solution Approach 1:
Instead of imposing arbitrary rectangular bounding boxes, the patent segments the geographic area into tiles that conform to the actual terrain and feature boundaries. This allows the algorithm to process only relevant areas without artificial cutoffs, maintaining route optimization accuracy while still limiting computational burden through selective processing of individual tiles.
Solution Approach 2:
The patent applies different processing resolutions and detail levels to different geographic tiles based on their specific characteristics. Areas with complex terrain or critical route decisions receive higher processing fidelity, while less critical areas use coarser representations, optimizing the balance between accuracy and computational burden locally across different regions.
Data Source
AI summary
Technologies are provided for memory management for route optimization algorithms. An example method can include determining a cost surface of a route-based project associated with an area, the cost surface including nodes comprising costs associated with respective locations within the area; determining whether a cache has data of each neighbor of a current node being processed to determine a least-cost path from a start node to an end node; obtaining, from the memory cache, the data of each neighbor; for each particular neighbor that is not a boundary node in the cost surface, determining a projected cost of the particular neighbor based on an accumulated cost of the particular neighbor and an additional cost estimated based on a distance between the particular neighbor and the end node; and based on the projected cost of each particular neighbor, determining the least-cost path from the start node to the end node.