Proximity-Based Queue Cycle Analysis for Automated Logistics Tracking
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Solution Overview
Problem
Current logistics operations, particularly in agricultural settings, face challenges in accurately monitoring inventory, input costs, and machine usage due to reliance on manual tracking methods, leading to inefficiencies and errors.
Innovation Solution
A system and method for proximity-based analysis that utilizes sensors to collect geospatial data, generates event blocks and queue cycles, performs correlation analysis, and determines representative cycles for automated operational tracking, including contract load assignments and chain of custody evidence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tracking methods are used for logistics operations, then implementation simplicity is maintained, but accuracy and reliability of tracking data deteriorate
Solution Approach 1:
The patent replaces manual tracking methods with an automated sensor-based system that collects geospatial data, timestamps, and operational information. Sensors and processors automatically generate event blocks and queue cycles, eliminating human error and improving tracking accuracy without requiring complex manual procedures
Solution Approach 2:
The system performs self-service by automatically collecting data from sensors, generating event blocks, creating queue cycles, and performing correlation analysis without human intervention. The automated processing pipeline handles data collection, analysis, and reporting independently, improving both accuracy and operational efficiency
2Reliability
If automated sensor-based tracking is implemented, then tracking accuracy and reliability improve, but system complexity and implementation difficulty increase
Solution Approach 1:
The patent segments the tracking system into distinct functional modules: sensor data collection, event block generation, queue cycle creation, and correlation analysis. Each module handles a specific aspect of tracking, making the overall complex system manageable through modular design while maintaining high reliability through specialized processing at each stage
Solution Approach 2:
The patent introduces event blocks and queue cycles as intermediary data structures that bridge raw sensor data and final tracking results. These intermediaries organize and structure the data flow, making the system more manageable and reliable by providing clear data transformation stages between data collection and analysis
3Productivity
If manual recording methods are used, then implementation cost is low, but productivity and operational efficiency deteriorate
Solution Approach 1:
The patent implements continuous automated data collection and processing through sensors that continuously monitor geospatial information, timestamps, and operational parameters. The system continuously generates event blocks and queue cycles without interruption, eliminating the discontinuous nature of manual recording and significantly improving operational efficiency through uninterrupted tracking
Solution Approach 2:
The patent replaces manual recording operations with automated sensor-based systems that continuously collect and process data. This substitution eliminates the labor-intensive nature of manual tracking while improving productivity through automated data generation, processing, and analysis pipelines
4Loss of information
If comprehensive sensor data collection is performed, then data completeness and analysis accuracy improve, but data processing time and computational complexity increase
Solution Approach 1:
The patent performs preliminary organization of sensor data by automatically generating event blocks from raw data and pre-structuring queue cycles before correlation analysis. This preliminary action prepares the data in advance with proper formatting and structure, reducing the computational burden during final analysis and decreasing processing time while maintaining data completeness
Solution Approach 2:
The patent segments comprehensive sensor data into discrete event blocks and queue cycles, organizing large volumes of data into manageable units. This segmentation allows for efficient processing by handling data in structured portions rather than as a monolithic dataset, reducing computational complexity while preserving all information
Data Source
AI summary
A system for proximity-based analysis for operational tracking includes one or more processors. The one or more processors receive event blocks, which include information associated with an operation. The one or more processors are further configured to generate a set of queue cycles based on the received event blocks. Each queue cycle includes at least a starting and ending event block. The one or more processors are further configured to perform a correlation analysis between the set of queue cycles and the set of evidence artifacts, where each artifact is compared to each queue cycle and assigned a correlation score for each artifact-queue cycle pair. The one or more processors are further configured to determine a representative queue cycle by identifying the queue cycle with the highest quality score, which will be stored in a database for subsequent retrieval along with the corresponding evidence artifact.