Geofence-Based Product Identification for Work Vehicle Loading
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Solution Overview
Problem
Existing systems rely on manual operator input for product type identification in industrial job sites, which is time-consuming and prone to errors.
Innovation Solution
A controller on the work vehicle determines product type based on geolocation using geofences and updates the model with operator feedback or image data to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual operator input is used for product type identification, then the system is simple to operate, but the process is time-consuming and error-prone
Solution Approach 1:
The system enables self-service by allowing the work vehicle to automatically identify product types through geolocation data and model matching, eliminating the need for manual operator input. The controller autonomously determines product types by comparing geofence locations with material database entries, thereby improving both productivity and reliability simultaneously.
2Measurement precision
If automated geolocation-based identification is implemented, then identification accuracy improves, but system complexity increases
Solution Approach 1:
The patent introduces a model database as an intermediary that stores pre-defined geofence locations and associated product types. This intermediary layer simplifies the complexity by providing a structured reference system that the controller can query, rather than requiring complex real-time analysis of all possible variables.
Solution Approach 2:
The system performs preliminary action by pre-establishing geofences and associating them with product types in a model database before operation. This pre-processing of spatial data and material information reduces runtime complexity, as the controller only needs to match current geolocation against pre-defined zones rather than performing complex analysis during operation.
3Reliability
If the model is updated with operator feedback, then identification reliability improves, but data processing time increases
Solution Approach 1:
The system implements feedback by allowing operators to confirm or correct identified product types. This feedback is stored in the model database to refine future identifications. The feedback mechanism improves reliability by continuously learning from actual operational data while maintaining a efficient update process.
Solution Approach 2:
The system applies partial action by selectively updating the model database only when operator feedback indicates a correction is needed, rather than continuously processing all data. This approach maintains model accuracy while minimizing the time loss associated with unnecessary updates.
Data Source
AI summary
A product identification system includes a controller configured to identify a loading action performed by a work vehicle and determine whether the work vehicle is located within a threshold distance of a geofence. In response to determining that the work vehicle is located within a threshold distance of the geofence, the controller determines a determined product type associated with the loading action based on a model which associates the geofence with the determined product type. In response to determining that the work vehicle is not located within the threshold distance of the geofence, the controller determines a current product type associated with the loading action. The controller determines whether the current product type matches the determined product type associated with the geofence. In response to determining that the current product type matches the determined product type, the controller updates the geofence to include a location of the work vehicle.