Worksite Inefficiency Identification via Equipment Data Analysis
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Solution Overview
Problem
Worksites face inefficiencies due to the lack of comprehensive data analysis to identify and address equipment interactions and site conditions, often relying on manual worker assessments that are incomplete.
Innovation Solution
A system and method that utilize equipment data from both mobile and fixed equipment to generate representations of current routes and cycle times, identify inefficient aspects, and recommend actions to improve worksite efficiency by adjusting operating parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual worker assessments are used to identify inefficiencies, then implementation simplicity is maintained, but measurement precision and completeness of inefficiency detection deteriorate
Solution Approach 1:
The patent replaces manual worker assessments with an automated computer vision system using cameras and machine learning algorithms to detect inefficiencies. The system captures images of the worksite, processes them through trained models, and automatically identifies equipment interactions and inefficiencies without human intervention, thereby improving detection accuracy while maintaining implementation simplicity through software-based solutions.
2Measurement precision
If comprehensive equipment data collection is implemented, then measurement precision of worksite conditions improves, but device complexity increases
Solution Approach 1:
The patent employs a multi-functional computer vision system that simultaneously performs multiple tasks: detecting equipment locations, identifying equipment interactions, analyzing work cycles, and detecting inefficiencies all through a single integrated platform. This universal approach consolidates what would otherwise require multiple separate systems, reducing overall device complexity while maintaining comprehensive data collection capabilities.
Solution Approach 2:
The system uses passive data collection through existing cameras and sensors that capture worksite information without active intervention. The machine learning models automatically process the captured data and generate insights without requiring complex manual data gathering protocols, allowing the system to self-serve the data collection and analysis functions.
3Productivity
If real-time route representation and cycle time analysis are generated, then productivity optimization improves, but loss of time for data processing increases
Solution Approach 1:
The patent pre-trains machine learning models with extensive worksite data before deployment. This preliminary training enables the models to rapidly process real-time data without requiring complex calculations during actual operation. The system performs heavy computational work in advance during the training phase, allowing for quick inference and decision-making during actual worksite operations, thus minimizing real-time processing delays.
Solution Approach 2:
The system uses efficient algorithms that skip unnecessary computational steps by leveraging pre-processed data and pre-trained models. Rather than performing complete analysis from scratch for each worksite scenario, the system rapidly processes key parameters using optimized machine learning inference, rushing through the essential analysis needed for real-time productivity optimization while minimizing processing time.
Data Source
AI summary
Systems and methods to identify inefficiencies in a worksite and generate recommended actions to improve the inefficiencies are provided herein. The inefficiencies may be identified by analyzing equipment data from mobile equipment and fixed equipment associated with the worksite. The equipment data may be used to generate one or more routes through the worksite. The equipment data and one or more routes may be used to generate a cycle time for the worksite. The equipment data, one or more routes, and cycle time may be used to identify an inefficiency in the worksite, and the identified inefficiency may be used to generate a recommended action to improve and thus remedy the inefficiency.