Parcel Path Reconstruction For Sorting Position Error Detection
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Solution Overview
Problem
Existing geolocation technologies in sorting facilities face inaccuracies and missed scans, leading to inconsistent naming conventions and inability to determine parcel position errors, which hampers accurate tracking and analytical metrics.
Innovation Solution
A method and apparatus that compare load data from parcel scans with configuration data to detect parcel position errors by determining if the load data exceeds a threshold, allowing for automatic detection of errors such as parcels falling off conveyers or being miss-sorted.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If geolocation technologies use scan-based tracking, then asset presence can be detected, but measurement precision deteriorates due to inaccurate or missed scans
Solution Approach 1:
The system compares actual scan data against expected scan patterns to detect deviations. By continuously monitoring whether scans occur at expected locations and times, the system identifies inaccurate or missed scans and triggers corrective actions, thereby improving measurement precision while maintaining reliable asset presence detection
Solution Approach 2:
The system pre-establishes expected scan patterns and locations before tracking begins. By having predetermined criteria for what scans should look like, the system can immediately identify anomalies without requiring complex real-time analysis, thus improving scan accuracy assessment
2Ease of manufacture
If geolocation technologies store scan events with inconsistent naming conventions, then data collection is simplified, but measurement precision deteriorates due to inability to map to physical locations
Solution Approach 1:
The system introduces a standardized location reference system that acts as an intermediary between inconsistent scan names and physical locations. By mapping all scans to a common reference framework, the system preserves the simplicity of data collection while enabling precise location determination through the standardized intermediary layer
Solution Approach 2:
The system transforms scan data by adding standardized location parameters alongside the original inconsistent naming conventions. This parameter transformation allows the system to maintain easy data collection while enriching the data with precise location information that can be consistently interpreted
3Device complexity
If no analytical metrics are determined, then system complexity is reduced, but measurement precision deteriorates due to inability to identify inaccurate or missing scans
Solution Approach 1:
The system implements a focused analytical metric that specifically measures scan occurrence against expected patterns, rather than attempting comprehensive analysis of all tracking data. This partial action approach provides sufficient precision for identifying inaccurate or missing scans without the complexity of a full analytical framework
Solution Approach 2:
The system uses the scan data itself to evaluate tracking accuracy by comparing actual scans against expected patterns. The tracking system performs its own validation without requiring external analytical systems, thereby improving measurement precision while avoiding additional system complexity
Data Source
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AI summary
Methods and apparatus for determining a parcel position error based on a comparison of load data and configuration data are provided. Embodiments describe a number of methods of determining the parcel position error such as by determining a parcel path from the load data.