Dynamic Risk Scoring System for Workplace Hazard Prediction
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Solution Overview
Problem
Current systems for assessing workplace risk and severity are inadequate, as they tend to overpredict risks due to large numbers of low-weight indicators and fail to accurately determine the probability and consequence of workplace hazards, leading to inefficient risk mitigation.
Innovation Solution
A cloud-based system using machine learning processes that integrates internal and external data to create a proprietary scoring model, combining active and passive user inputs to provide real-time workplace hazard scores, leveraging metrics like standard deviation of hazard resolution time, and employing algorithms like Regression, Random Forest, and Gradient Boosting for accurate risk prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If prior art systems assign different weights to different indicators, then some indicators are recognized as stronger than others, but when there is a large number of low weight indicators in an entity's publications, the system tends to over predict the probability that an entity is in a particular class
Solution Approach 1:
The patent changes the parameter of indicator weighting by introducing a dynamic weighting mechanism that adjusts weights based on the total number of indicators present. The system calculates a normalization factor based on the count of indicators and adjusts individual indicator weights accordingly, preventing over-prediction when multiple low-weight indicators are present. This resolves the contradiction by making the measurement more accurate while maintaining reliability across different indicator scenarios.
2Ease of manufacture
If traditional approaches are used for workplace risk assessment, then some type of risk assessment is performed, but they cannot accurately determine the probability of a workplace risk and the consequence/severity of the workplace risk
Solution Approach 1:
The patent segments the risk assessment into multiple independent indicator categories (safety management indicators, workplace condition indicators, incident history indicators, etc.). Each segment is evaluated separately with its own weighting and scoring mechanism, then aggregated to produce the overall risk probability and severity scores. This segmentation enables accurate determination of both probability and severity while maintaining ease of implementation through modular assessment.
Solution Approach 2:
The system changes the parameters of risk assessment by introducing multiple weighted indicators across different categories, each contributing differently to the final probability and severity scores. The dynamic weighting system adjusts the influence of each parameter based on its relevance and the overall indicator set, enabling accurate determination of risk characteristics while keeping the assessment process straightforward and implementable.
Data Source
AI summary
A system and method for the collection and processing of workplace, public and private data to predict and score risk incident frequency and severity for a commercial client. In one embodiment, the risk assessment may be performed using one or more machine learning techniques.