Plant Hazard Mapping Using NLP and Machine Learning
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Solution Overview
Problem
Current systems lack a unified, easily readable dataset to identify, predict, or anticipate plant hazards and assess associated risks to human workers and equipment, relying heavily on manual analysis of vast amounts of unstructured data.
Innovation Solution
A plant safety manager system utilizing machine learning models, including natural language processing and structured data, to analyze structured and unstructured safety data, predict hazards, and generate hazard maps, automatically triggering mitigation operations when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of vast amounts of unstructured data is used to identify plant hazards, then comprehensive hazard identification is possible, but the process is time-consuming and prone to human error
Solution Approach 1:
The patent replaces manual mechanical analysis with automated machine learning models and natural language processing systems. These systems process unstructured safety data, maintenance logs, and inspection reports automatically, eliminating human error and significantly reducing analysis time while maintaining comprehensive hazard identification capabilities.
Solution Approach 2:
The system enables self-service hazard identification by automatically processing and analyzing safety data without requiring manual intervention. The machine learning models autonomously identify patterns, predict hazards, and generate safety assessments, allowing the system to serve itself in the hazard identification process.
2Reliability
If comprehensive safety data collection is implemented to improve hazard prediction, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the complex safety management system into distinct functional modules: data collection modules for structured and unstructured data, natural language processing modules for text analysis, machine learning models for hazard prediction, and visualization modules for hazard maps. This segmentation manages complexity while maintaining comprehensive data collection and high prediction reliability.
Solution Approach 2:
The patent introduces machine learning models and natural language processing systems as intermediaries between raw safety data and hazard predictions. These intermediaries automatically process and transform diverse data formats into actionable safety insights, reducing the complexity burden on the overall system while improving prediction reliability.
3Speed
If automated mitigation operations are implemented, then response speed to hazards improves, but control system complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-configuring mitigation operations and criteria in the control system. When hazards are predicted, the system can automatically execute pre-planned mitigation sequences, dramatically improving response speed. The complexity is managed by establishing these actions in advance rather than creating complex real-time decision logic.
Data Source
AI summary
A method may include determining classified safety data regarding a plant area using the unstructured data and a first machine-learning model that uses natural language processing. The method may further include determining a plant hazard for the plant area using a second machine-learning model, the classified safety data, and the structured safety data. The method may further include obtaining historical hazard data for various plant facilities. The method may further include determining a hazard rate for the plant area based on the plant hazard and the historical hazard data. The method may further include generating, within a graphical user interface on the user device, a hazard map for a plant facility. The method may further include determining a mitigation operation for the plant hazard. The method may further include transmitting a command to a control system to perform the mitigation operation.