Real-Time Position Snapping to Terrestrial Features in Mining
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Mining operations face challenges in accurately correlating vehicle position data with dynamic and complex terrestrial features due to inherent uncertainties in positioning systems and frequent changes in mining environments, leading to inefficiencies in fleet tracking and dispatching systems.
Innovation Solution
A computer-implemented method and system that uses a pre-processing algorithm to prepare terrestrial data and a real-time snapping algorithm to select and snap sensed position data to the best snap point candidate based on predictive variables and weighting factors, ensuring accurate and rapid correlation with terrestrial features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a best snap point candidate is chosen from a large set of candidates using multiple predictive variables and weighting factors, then the accuracy of position correlation is improved, but the computational time and processing complexity increase
Solution Approach 1:
The patent divides the terrestrial feature data into discrete snap point candidates and segments the selection process into multiple stages: initial candidate generation, filtering based on predictive variables, and final selection using weighting factors. This segmentation allows the system to process large datasets in manageable portions rather than evaluating all candidates simultaneously, reducing computational time while maintaining accuracy.
Solution Approach 2:
The system performs preliminary actions by pre-processing terrestrial feature data to create a database of snap point candidates before real-time tracking begins. During real-time operation, the system only needs to evaluate and select from pre-processed candidates using weighted predictive variables, rather than processing raw terrestrial data from scratch, significantly reducing real-time computational requirements.
2Productivity
If real-time correlation of position data with terrestrial features is performed, then the responsiveness and utility of the system is improved, but the computational load and processing requirements increase
Solution Approach 1:
The patent changes parameters by using a weighted combination of predictive variables with dynamically adjustable weights. The system can adapt the importance of different variables (e.g., distance, bearing, elevation) based on operational context, allowing efficient real-time processing by focusing computational resources on the most relevant parameters rather than equally processing all possible features.
Solution Approach 2:
The system creates simplified copies of terrestrial features as snap point candidates with essential attributes (coordinates, identifiers, weighted predictive variables) rather than processing complete terrestrial datasets in real-time. This copying approach maintains the necessary information for accurate correlation while dramatically reducing the computational load during real-time operations.
3Measurement precision
If multiple predictive variables with weighting factors are used to select the best snap point candidate, then the precision of position matching is improved, but the complexity of the selection algorithm increases
Solution Approach 1:
The patent employs parameter changes by using a weighted scoring system where each predictive variable is assigned a weight reflecting its importance. The algorithm computes a total weighted score for each candidate by summing the products of variable values and their weights, providing a straightforward mathematical approach that improves selection accuracy without requiring complex decision logic or multiple algorithmic steps.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Methods and systems of correlating sensed position data with terrestrial features receive sensed position data from a position sensing system operatively associated with a moveable object; select a reduced set of snap point candidates from terrestrial data based on a sensed position point; choose a best snap point candidate from among the reduced set of snap point candidates based on a plurality of predictive variables and corresponding weighting factors for each snap point candidate in the reduced set of snap point candidates; and snap the sensed position point to the best snap point candidate to produce a snapped position point. The selecting, choosing, and snapping processes are performed in substantially real time so that the systems and methods correlate the sensed position data from the moveable object with terrestrial features in substantially real time.