Reinforcement Learning Risk Optimization for Site Productivity

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Conventional risk management systems fail to optimize operator behavior and equipment usage in work sites, leading to either catastrophic accidents or reduced productivity, as they do not effectively balance risk and productivity.

Innovation Solution

A risk management system that analyzes user, site, and equipment data to detect operator tolerance and behavior patterns, using reinforcement learning to optimize site productivity within safe limits by determining relationships between behavior, performance, and risk factors, and creating schedules to manage risk and productivity.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If operators perform operations at higher speed and torque to improve key performance indicators, then productivity is improved, but risk increases

Engineering Contradiction:

Improvekey performance indicatorsVSAvoidrisk

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system continuously monitors operator behavior, equipment data, and site conditions, then provides real-time feedback through the user interface to guide operators toward safer behaviors that maintain productivity. The reinforcement learning model learns from historical data to predict risk and provide actionable feedback to operators.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent replaces traditional mechanical risk management approaches with an intelligent system using machine learning algorithms, sensors, and data analytics to dynamically assess and manage risk, enabling more precise and adaptive control compared to conventional safety systems.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If operators engage in risk-taking behavior to improve productivity outcomes, then productivity is improved, but safety incidents increase

Engineering Contradiction:

Improveproductivity outcomesVSAvoidsafety incidents

Core Design Contradiction:

ProductivityVSObject-affected harmful factors

Solution Approach 1:

The system performs preliminary risk assessment by analyzing historical behavior data and predicting future risk levels before incidents occur. The reinforcement learning model proactively identifies operators who may engage in dangerous risk-taking behaviors and provides preventive feedback to guide safer decision-making.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system acts as an intermediary between operator intent and actual equipment operation, monitoring and analyzing operator behavior patterns to detect when risk-taking may lead to unsafe conditions, then providing guidance to mediate between productivity goals and safety requirements.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Reliability

If overly cautious operators operate equipment to maintain safety, then risk is reduced, but productivity decreases

Engineering Contradiction:

ImprovesafetyVSAvoidproductivity

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system dynamically adjusts safety guidance based on real-time conditions, operator experience level, and contextual factors. Rather than applying static cautious behavior rules, the reinforcement learning model adapts recommendations to match current operational needs, allowing operators to be appropriately cautious without unnecessarily limiting productivity.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes operational parameters and safety thresholds based on learned patterns from historical data. By analyzing when cautious behavior was necessary versus when it limited productivity unnecessarily, the system adjusts its guidance parameters to optimize the balance between safety and productivity for each specific situation.

Inventive Principle:

Principle #35Parameter changes

4Productivity

If aggressive operation of equipment is used to achieve key performance indicators, then productivity is improved, but overall site risk increases

Engineering Contradiction:

Improvekey performance indicatorsVSAvoidoverall site risk

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system applies differentiated risk management to different operators, equipment, and site conditions rather than uniform rules. It identifies which operators benefit from aggressive operation guidance versus which need more conservative guidance based on individual behavior patterns, equipment type, and site context, providing locally optimized recommendations.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system serves multiple functions simultaneously: monitoring individual operator behavior, assessing overall site risk, providing real-time feedback to operators, and generating aggregate analytics for site management. This multi-functional approach enables coordinated optimization of both individual productivity and overall site safety.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS11810038B2Risk optimization through reinforcement learning

Publication Date: 2023.11.07 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

A risk management method, system, and non-transitory computer readable medium, include a data analyzing circuit configured to analyze user data, site data, and equipment data to map prior behavior types to an event on a site, a relationship determining circuit configured to determine a relationship between the mapped data and the event based on behaviors exhibited by the user and an impact on a performance factor and a risk factor, and a reinforcement learning circuit configured to use reinforcement learning to learn the performance factor to the risk factor ratio to optimize an overall site productivity.