AI Vehicle Monitoring for Production Time Discrepancies
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing production monitoring processes in industries like construction and mining face challenges in detecting time discrepancies and bottlenecks in repeatable processes, particularly in large-scale operations involving heavy-duty vehicles, which hinders efficient production cycles.
Innovation Solution
A computer-implemented method using vehicle location and pose data to train an AI system for real-time monitoring, identifying time discrepancies by comparing location data points with predetermined points of interest, and providing adjustments to optimize production processes through a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If production monitoring processes are implemented to detect time discrepancies and bottlenecks, then production efficiency is improved, but the complexity of implementation increases due to the need for on-site assessment
Solution Approach 1:
The patent replaces manual on-site assessment mechanisms with an automated computer-implemented monitoring system. The system automatically collects location data from vehicles via GPS, processes this data to identify points of interest, and compares actual timing with predetermined timing without requiring physical presence at the production site. This substitution of mechanical/on-site monitoring with automated electronic monitoring resolves the contradiction by maintaining productivity improvement while eliminating implementation complexity.
Solution Approach 2:
The patent introduces a computer-implemented system as an intermediary between the production process and the monitoring function. This intermediary automatically collects data from vehicles, processes location information, identifies points of interest, and performs timing comparisons without direct human intervention. The intermediary handles all complex processing tasks, allowing productivity monitoring to occur without the complexity of direct on-site assessment.
2Loss of time
If real-time monitoring of vehicle locations and stop times is performed, then time discrepancies are detected earlier, but the use of energy and computational resources increases
Solution Approach 1:
The patent extracts only the essential data elements needed for monitoring - specifically location data and stop time information from vehicles. Rather than processing all vehicle operational data, the system selectively extracts and processes only the location coordinates and timing information relevant to detecting time discrepancies at points of interest. This extraction of essential data reduces computational resource consumption while maintaining effective real-time detection capability.
Solution Approach 2:
The patent implements partial monitoring by focusing only on specific points of interest within the production process rather than continuously monitoring all vehicle activities. The system identifies key locations where time discrepancies are most likely to occur and concentrates processing resources on these critical points. This partial action approach reduces overall computational energy requirements while still achieving effective detection of time discrepancies when they occur.
Data Source
AI summary
A computer-implemented method for indicating time discrepancies in a repeatable process performed by a plurality of vehicles includes receiving location data including a plurality of data points, each data point corresponding to a time-marked geographical position reported by a vehicle, processing the location data to determine at least one data point subset which is indicative of a point-of-interest, POI, for the repeatable process, comparing the determined data point subset with one or more data point subsets associated with predetermined POIs of a repeatable process, and based on an output of the comparison, identifying a number of POIs of the repeatable process, comparing time information associated with one of the determined POIs of the repeatable process with time information associated with the one or more predetermined POIs, and based on an output of the comparison of time information, identifying one or more time discrepancies in the repeatable process.