Forestry Machine Output Control Using Normalized Production Metrics
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Solution Overview
Problem
Current systems face difficulties in utilizing machine-specific sensor information to control and improve the performance of various machines across a forestry operation, as this information is typically expressed within the context of individual machines and lacks consistency for productivity measurement across the entire site.
Innovation Solution
A machine output detection and control system that translates machine-specific information into normalized metrics, using machine metric logic to generate quantity metrics, normalization logic to aggregate these metrics, dependency logic to define operation order, and action signal logic to generate control signals for improving overall site performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine-specific sensor information is used directly, then each machine can operate with its own parameters, but the information cannot be utilized to control and improve performance across the entire forestry operation
Solution Approach 1:
The system transforms machine-specific sensor parameters into normalized productivity metrics by applying conversion rules and normalization factors. This allows data from different machine types (harvesters, forwarders, skidders) to be expressed in common units that enable cross-machine performance comparison and optimization
Solution Approach 2:
A centralized control system acts as an intermediary between individual machines and the overall forestry operation. This system collects machine-specific data, normalizes it according to defined rules, and uses the processed information to generate control signals that optimize fleet-wide productivity while preserving individual machine operational characteristics
2Quantity of substance
If machine-specific information is collected from multiple machines, then detailed operational data is available, but the information cannot be aggregated to measure productivity across the entire site
Solution Approach 1:
The system applies normalization transformations to convert diverse machine-specific metrics into consistent productivity units. By changing the parameter representation from machine-specific to standardized metrics, the system enables precise aggregation and comparison of productivity data across the entire forestry operation
Solution Approach 2:
The normalization framework creates a universal measurement system that can handle data from multiple machine types and sensor sources. This universal approach allows the system to aggregate information from harvesters, forwarders, skidders, and other equipment into a cohesive productivity measurement that reflects site-wide performance
3Adaptability or versatility
If different types of forestry machines operate independently, then each machine can perform its specific function, but bottlenecks cannot be identified and overall site productivity is limited
Solution Approach 1:
The system establishes a feedback loop where normalized productivity data from all machines is continuously monitored and analyzed. This feedback enables the identification of bottlenecks in the material flow between different machine operations, allowing for real-time adjustments that optimize overall site productivity while maintaining individual machine specialization
Solution Approach 2:
The centralized control system serves as an intermediary that coordinates between specialized machines. By analyzing normalized data and generating control signals, it optimizes the interaction and material flow between harvesters, forwarders, skidders, and other equipment, thereby eliminating bottlenecks and enhancing overall site productivity
Data Source
AI summary
A machine output detection and control system includes machine metric logic that detects a plurality of quantity metrics, each quantity metric corresponding to sensor information associated with at least one mobile machine in a plurality of different mobile machines. The system also includes normalization logic that aggregates the plurality of different quantity metrics to generate, for each mobile machine, a normalized production unit that is normalized across the plurality of different mobile machines. Dependency logic correlates the normalized production unit, for each mobile machine, to a machine dependency that identifies an order in which the plurality of different mobile machines operate at a jobsite. Further, action signal logic generates an action signal, based on the correlation.