Logging Machine Yield Prediction with Feedback-Based Forest Updates
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Solution Overview
Problem
Existing forest resource information used in logging operations becomes outdated and includes errors, leading to compromised production targets and increased costs due to inefficient harvesting and unnecessary machine movement.
Innovation Solution
A method involving a network service connected to logging machines that maintains master forest resource information with prediction cells, receives and associates logging machine measurements, determines product yield differences, and compensates prediction cell yields to improve accuracy and control logging operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing forest resource information is used for production prediction, then initial planning is enabled, but the information becomes outdated and contains errors leading to compromised production targets
Solution Approach 1:
The system implements feedback by continuously comparing actual logging machine measurements with predicted values from forest resource information. The difference (residual) is calculated and used to update the forest resource information, creating a closed-loop system that progressively improves accuracy while maintaining timely production target achievement.
Solution Approach 2:
The system dynamically updates forest resource information parameters based on actual measurements. By changing the parameters (tree volume, diameter, height) from static predicted values to dynamically adjusted values based on real-time data, the system maintains reliability without sacrificing time efficiency.
2Productivity
If harvester moves to new work area when production target not met, then extended driving time increases, but production costs increase and time to meet targets is extended
Solution Approach 1:
The system uses feedback from actual logging measurements to continuously update forest resource information in real-time. This enables accurate tracking of production targets and prevents premature machine relocation, ensuring the harvester operates efficiently at each work area until actual production goals are achieved, thereby minimizing unnecessary driving time.
3Measurement precision
If forest resource information is updated frequently, then accuracy improves, but system complexity and data processing requirements increase
Solution Approach 1:
The system applies local quality by updating forest resource information selectively based on actual measurements from specific work areas and prediction cells. Rather than globally updating all data frequently, it focuses computational resources on localized updates where measurement data is available, improving precision while managing complexity through targeted rather than universal processing.
Data Source
AI summary
There is provided maintaining, at a network service operatively connected to a logging machine, master forest resource information, wherein the master forest resource information comprises prediction cells for product yield over a work area or a set of work areas, receiving, at the network service from the logging machine, logging machine measurements associated with the work area or the set of work areas, associating, at the network service, the received logging machine measurements to at least one prediction cell of the master forest resource information, determining, at the network service, based on the received logging machine measurements, a logging machine product yield for the work area or the set of work areas, modeling, at the network service, a difference between the logging machine product yield and a product yield of the at least one prediction cell, and compensating, at the network service, product yields of prediction cells based on the modeled difference.