Waypoint Prediction Engine for GIS Route Compression Accuracy
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Solution Overview
Problem
The generation and management of vast GIS datasets pose challenges in terms of data transmission and storage, leading to high bandwidth requirements, costly infrastructure, environmental impact, and loss of spatial and temporal resolution due to conventional compression techniques.
Innovation Solution
A waypoint prediction engine that predicts and corrects waypoints based on historical data patterns, using a compression module locally and a decompression module remotely, reducing data transmission and storage by transmitting only correction factors for inaccurate waypoints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional data compression techniques are used to reduce GIS data volume, then storage requirements and bandwidth requirements are reduced, but spatial and temporal resolution and data accuracy are lost
Solution Approach 1:
The patent segments the compression task into two independent parts: a compression model trained on historical data that captures spatial-temporal patterns, and a residual model that only stores the differences between predicted and actual values. This segmentation allows the majority of data to be compressed using the learned patterns while preserving accuracy through minimal residual storage.
Solution Approach 2:
The system performs preliminary action by training the compression model offline on historical GIS data to learn spatial and temporal patterns before actual compression occurs. This pre-trained model can then rapidly compress new data without losing accuracy, as the heavy computational work of pattern recognition has already been done.
2Quantity of substance
If data aggregation is used to summarize detailed data points, then data volume is reduced, but localized trends and anomalies are obscured
Solution Approach 1:
The patent applies local quality by allowing different levels of compression and detail preservation across different regions of the data. Areas with high variability or localised trends are preserved with higher fidelity through the residual model, while homogeneous areas can be more aggressively compressed, maintaining information where it matters most.
3Measurement precision
If full GIS datasets are transmitted and stored to maintain accuracy, then data precision is preserved, but bandwidth requirements and storage costs increase significantly
Solution Approach 1:
The patent introduces an intermediary compression model that acts as a mediator between the original GIS data and the stored representation. This model predicts the majority of data values based on learned patterns, and only the small residual differences need to be transmitted and stored, dramatically reducing data volume while maintaining accuracy through the intermediary's predictive capabilities.
4Reliability
If large GIS datasets are stored to ensure data integrity, then data security and accessibility are maintained, but infrastructure costs and energy consumption increase
Solution Approach 1:
The patent extracts the essential information patterns from large GIS datasets during the offline training phase, separating the redundant information (captured by the compression model) from the essential residuals (stored in the database). This extraction allows storage of only the necessary data components, reducing energy consumption while maintaining data integrity through the reversible compression-decompression process.
Data Source
AI summary
Various embodiments of the present technology generally relate to systems and methods for providing a waypoint prediction engine and its functions. For example, a waypoint prediction engine may determine navigation data associated with a client device and a source model. The waypoint prediction engine may determine a current waypoint of the client device as the client device travels along a navigation route and generate, by a compression-side of the waypoint prediction engine, a predicted waypoint based on the source model and the navigation data. The compression-side may determine an accuracy of the predicted waypoint and generate a correction factor based on the accuracy of the predicted waypoint. The compression-side may transmit the correction factor to a decompression-side of the waypoint prediction engine, which may, in turn store the correction factor such that the correction factor and the source model allow for recreation of the navigation route of the client device.